CN103235556A - Feature-based numerical-control method for processing and manufacturing complicated parts - Google Patents
Feature-based numerical-control method for processing and manufacturing complicated parts Download PDFInfo
- Publication number
- CN103235556A CN103235556A CN2013101031341A CN201310103134A CN103235556A CN 103235556 A CN103235556 A CN 103235556A CN 2013101031341 A CN2013101031341 A CN 2013101031341A CN 201310103134 A CN201310103134 A CN 201310103134A CN 103235556 A CN103235556 A CN 103235556A
- Authority
- CN
- China
- Prior art keywords
- feature
- information
- processing
- geological information
- decision
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 366
- 238000012545 processing Methods 0.000 title claims abstract description 129
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 78
- 230000008569 process Effects 0.000 claims abstract description 201
- 238000001514 detection method Methods 0.000 claims abstract description 43
- 238000005457 optimization Methods 0.000 claims abstract description 34
- 238000005516 engineering process Methods 0.000 claims abstract description 31
- 230000010354 integration Effects 0.000 claims abstract description 7
- 238000005520 cutting process Methods 0.000 claims description 96
- 238000003754 machining Methods 0.000 claims description 70
- 239000000523 sample Substances 0.000 claims description 23
- 238000012544 monitoring process Methods 0.000 claims description 20
- 238000010276 construction Methods 0.000 claims description 18
- 230000001133 acceleration Effects 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 15
- 238000012360 testing method Methods 0.000 claims description 15
- 239000000463 material Substances 0.000 claims description 14
- 238000004886 process control Methods 0.000 claims description 13
- 239000000284 extract Substances 0.000 claims description 12
- 238000000354 decomposition reaction Methods 0.000 claims description 10
- 238000012512 characterization method Methods 0.000 claims description 9
- 238000011161 development Methods 0.000 claims description 9
- 238000013459 approach Methods 0.000 claims description 8
- 230000008520 organization Effects 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 230000009471 action Effects 0.000 claims description 5
- 238000013473 artificial intelligence Methods 0.000 claims description 5
- 230000001413 cellular effect Effects 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 4
- 235000012149 noodles Nutrition 0.000 claims description 4
- 238000007514 turning Methods 0.000 claims description 4
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 2
- 238000007726 management method Methods 0.000 abstract description 2
- 239000000969 carrier Substances 0.000 abstract 1
- 238000012805 post-processing Methods 0.000 abstract 1
- 210000003205 muscle Anatomy 0.000 description 21
- 230000006870 function Effects 0.000 description 12
- 238000009826 distribution Methods 0.000 description 9
- 230000008859 change Effects 0.000 description 7
- 230000018109 developmental process Effects 0.000 description 7
- 230000007704 transition Effects 0.000 description 6
- 241001269238 Data Species 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000004913 activation Effects 0.000 description 3
- 238000003801 milling Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000011112 process operation Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 241001212149 Cathetus Species 0.000 description 1
- 241000272194 Ciconiiformes Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000011960 computer-aided design Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000004377 microelectronic Methods 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 239000011148 porous material Substances 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000013341 scale-up Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Disclosed is a feature-based numerical-control method for processing and manufacturing complicated parts. The feature-based numerical-control method includes expressing features on the basis of a body and an object-oriented process; expressing geometrical and technological information of a part in an integral manufacturing procedure on the basis of the features; utilizing the features as carriers of manufacturing knowledge and experience to implement effective integration and closed-loop control for information of manufacturing stages of designing, processing, detecting and the like; and carrying out automatic technological decision, automatic numerical-control programming, post-processing, control for a processing procedure, online detection, technology optimization and working hour prediction on the basis of automatic feature identification and the features. The feature-based numerical-control method has the advantages that information links are effectively connected with one another in the processing procedure, the manufacturing procedure is automatic and intelligent, dependence on manual experience is reduced in the manufacturing procedure, production management for the manufacturing procedure is facilitated, the manufacturing efficiency is improved, and the production cost is reduced.
Description
Technical field
The present invention relates to a kind of integrated manufacturing method, especially a kind of with CAD(Computer Aided Design)/CAPP (Computer Aided Process Planning)/CAM(Computer Aided Manufacturing)/CNC(Computer Numerical Control)/CAI(Computer Aided Inspection) serve as the manufacture method that the basis can generate the complex parts nc program automatically, specifically a kind of complex parts digital control processing manufacture method based on the aircraft component feature.
Background technology
High precision, high-level efficiency, high reliability are the targets of manufacturing technology unremitting pursue.The process of field high, precision and frontier complex products such as Aeronautics and Astronautics, automobile, boats and ships, energy microelectronics constantly proposes new challenge to the limiting performance of modern manufacturing process, equipment and system, presses for the method that can solve manufacture process essence.The digitizing manufacturing combines advanced person's manufacturing technology with digitizing technique, aspect description, planning, operation and the control of manufacturing process, equipment and system deep change is taking place.The digitizing manufacturing is by manufacture process digitalized artificial and optimization, and digitizing expression, tissue and the storage etc. of making knowledge, information, realizes digitization modeling and information is integrated, working in coordination with between manufacture process and the manufacturing system, virtual emulation etc.But so far, also there are not a kind of method or means can realize the integrated of above-mentioned digitizing manufacturing knowledge and express, in order to realize the integrated of whole manufacturing process.
A kind of means that feature technology is made as a kind of digitizing, brought into play vital role in information aspect integrated, in the application of traditional research and business software, feature technology is widely used in the CAD/CAM field, but all being in conceptual phase aspect integrated, all is the independent utility that is in CAD or CAM basically.Feature technology has a lot of scholars in research aspect STEP-NC in recent years, and has proposed a series of international standard, but all is in the exploratory stage, and is poor with the equipment interface that present industry is actual, is difficult to apply.
Retrieval has been delivered or disclosed documents and materials, substantially all is the integrated or integrated aspect of CAM/CNC of CAD/CAM, or single application research.Simultaneously, can not be applied to complex parts and information that can not the effective expression whole manufacturing process aspect information representation and the method.
In sum, in view of digitizing is manufactured on vital role and the challenge thereof in whole manufacturing field, but whole manufacturing system is loose relatively; And feature technology is not also realized the integrated of manufacturing system as a kind of digitized realization means.
Summary of the invention:
The objective of the invention is at existing whole manufacturing system loose relatively, be difficult to realize the problem of the integrated manufacturing of digitizing, with the carrier of feature as the manufacturing knowledge and experience, the effective integration of realization information generates the complex parts digital control processing manufacture method based on feature of the nc program of whole part automatically.
Technical scheme of the present invention is:
A kind of complex parts digital control processing manufacture method based on feature, it is characterized in that at first reading in the three-dimensional digital model of part to be processed, secondly, carry out automated characterization identification, from the three-dimensional digital model that reads in, extract geological information and the technique information of part to be processed, the information that does not comprise in the three-dimensional digital model is by obtaining with the supporting related process file of digital-to-analogue, and geometry and the technique information that extracts carried out the characterization mark, all features are all carried out unique identification according to feature classification and sequence number, be signature identification=feature classification+sequence number, identify instrument as feature association between different application with this; If feature is decomposed in manufacture process, signature identification=decompositions sign+feature classification+sequence number+decompose sub-sequence number then, thus realize with the carrier of feature as the manufacturing knowledge and experience effective integration of realization information; The 3rd, for the validity of maintenance feature in whole manufacturing process and the continuity of feature development, adopt object-oriented and be the body of defined feature and the class of each application feature based on the method representation feature of body, adopt the method for holographic attribute face edge graph to carry out feature identification; At last, the process process optimization generates the nc program of complex parts automatically, drives numerically controlled processing equipment and finishes whole process.
After carrying out automated characterization identification and finishing again based on the feature construction processing technology optimize model, based on feature carry out the automatic process decision-making, based on feature carry out the automatic numerical control programming, based on feature carry out rearmountedly handling, based on feature carry out process control, based on feature carry out online detection, based on feature construction processing parts man-hour the sample storehouse and construction feature geological information and technique information to the mapping model of machine tool capability parameter;
(1) describedly should comprise the geological information of feature, information, lathe information and the workpiece material information of cutter when optimizing model based on the feature construction processing technology, that optimizes model is constrained to cutting force and vibration, optimizing the Model Optimization target is working (machining) efficiency, crudy and processing cost, the project of optimizing is cutting-in, cuts wide and speed of feed, sets up the cutting parameter storehouse based on feature; Application-oriented field makes up the cutting test test piece three-dimensional model based on feature, comprises characteristic feature and typical combination thereof, as the master body of checking processing technology;
It is (2) described that to carry out automatic process decision-making based on feature be to provide complete information support for the automatic process decision-making, based on feature representation process rule and process program, and then carry out automatic process based on feature and make a strategic decision, based on the result of feature representation automatic process decision-making;
(3) describedly carry out automatic numerical control programming based on feature and refer to the result of automatic process decision-making also as the foundation of automated programming, and then generate processing cutter rail automatically based on feature, based on the result of feature representation automated programming;
(4) described to carry out rearmounted the processing based on feature be that result with automated programming is foundation, considers the dynamic perfromance of digital control system, carries out handling based on the postposition of feature, based on the rearmounted result who handles of feature representation, forms nc program;
It is (5) described that to carry out process control based on feature be for expressing the processing policy of the problem that occurs in the process, comprise the status monitoring that carries out process based on feature, carry out the intelligent adaptive adjustment in the process, based on the data in the feature record process, use for process optimization;
(6) describedly carry out online detection based on feature and refer to that forming check point based on the geological information of feature and technique information generates standard, gauge head orientation vector and detection track when optimizing online detection based on the geological information of feature, form the detection analysis result based on feature, as the foundation of optimizing processing technology;
(7) the described geological information that comprises the feature relevant with man-hour man-hour in the sample storehouse and part man-hour based on feature construction processing parts, with feature geometries information and technique information as input, make up neural network, the man-hour of prediction part to be processed under the prerequisite that does not have the part by numerical control program is as the foundation that part processing is externally offered and the production schedule is arranged; The geological information and the technique information that in based on the numerical control program of feature, add feature, the dynamic perfromance of combining with digital control lathe and the cutting force in the working angles, the digital control processing man-hour of each operation of accurately predicting is as the foundation of lathe scheduling;
(9) described construction feature geological information and technique information are based on the performance parameter that signature analysis satisfies the required lathe of processing parts to the mapping model of machine tool capability parameter, and application oriented lathe correlation parameter design reference is provided.
Described step of carrying out automatic process decision-making based on feature is:
(1) based on feature representation geological information and the technique information relevant with process decision, wherein geological information is: the maximum tool diameter that the physical dimension of feature, position, geometry, feature allow, and technique information is: material, blank form, process redundancy and machining precision;
(2) carry out according to the order of lathe decision-making, processing sequence decision-making, job operation decision-making and cutting parameter decision-making based on the process decision of feature, form the automatic process result of decision according to process rule and process program;
(3) based on feature representation process decision result formats be: the per pass operation comprises some work steps, comprises some operations under each work step, and each operation is corresponding with a machining feature, contains action type, Processing Strategies and the cutting parameter of feature in the feature.
Described automatic numerical control programmed method based on feature is:
(1) based on feature representation geological information and the technique information relevant with automated programming, wherein geological information is: the physical dimension of feature, required how much of drivings, cutter rail starting point and terminating point and the cutter rail of generation cutter rail dodge how much, and technique information comprises: the tool-information of machining feature, the machining precision of feature and cutter rail strategy;
(2) extract machining feature by feature identification, output comprises the feature recognition result of geological information and technique information;
(3) based on the geological information of feature recognition result and process decision result's technique information, generate the cutter rail of each machining feature automatically;
(4) generate the cutter location file of the processing cutter rail of each feature with the unit of being characterized as, according to different application demands, the corresponding information of mark.
Described rearmounted treatment step based on feature comprises:
(1) in numerical control program, handles relevant geological information and technique information based on feature representation with postposition, wherein geological information refers to: the type of the line in the geometry of feature: straight line, circular arc, conic section, free curve, and the type of face: plane, ruled surface, free form surface, the numerical control processing and operating type during technique information middle finger processing character pair;
(2) characteristic of the digital control system of considering in rearmounted the processing has comprised acceleration characteristic, interpolation precision characteristic and handle of a knife compensation control parameter type;
(3) based on feature geometries and technique information, the dynamic perfromance of combining with digital control system is carried out match to cutter location and is formed the polynomial interpolator method that digital control system is supported, and optimizes speed of feed to adapt to complicated digital control processing operating mode.
The described step of controlling based on the process of feature comprises:
(1) based on feature representation geological information and the technique information relevant with process control, wherein geological information is: corner information and driving geological information, and technique information is: process tool, cutting parameter, monitoring policy and detection strategy;
(2) according to geological information and technique information and the processing policy of feature, carry out on-line monitoring, and realize the processing of monitoring result based on artificial intelligence approach;
(3) data of record in the process refer to the tool wear and the flutter that occur in the operate power, process of cutting parameter actual in the process, lathe; To be characterized as the above data of cellular organization.
Described online detection based on feature may further comprise the steps:
(1) based on feature representation geological information and the technique information relevant with detection, wherein geological information is: the theoretical geometric model of the Noodles type of detected characteristics, curvature variation, area, normal direction and middle machining state; Technique information is: machining precision;
(2) make up the create-rule of the check point of detected characteristics based on the geological information of feature and technique information, generate the check point of detected characteristics based on feature, and consider that the intermediateness optimization of detected characteristics detects track, generate to detect file;
(3) probe is installed and is carried out online detection, record testing result and analysis result, and organize based on feature, feed back to technology department, as the foundation of process optimization.
Described processing technology based on feature is optimized geological information and the technique information that model is considered feature, and wherein, geological information refers to: as the physical dimension of the feature of rigidity important consideration, thickness and the height that feature constitutes geometric element.
Described based on feature construction processing parts man-hour sample storehouse time institute foundation geological information comprise angle position, corner size etc.; Technique information comprises chipping allowance, cutting-in, cuts wide main speed of feed; The dynamic perfromance of lathe comprises acceleration, acceleration and the turn performance of lathe, dynamic perfromance based on geological information, technique information and lathe, actual feed in the predicting machine bed motion process, and in conjunction with prediction of Turning Force with Artificial because the variation of the speed of feed that the variation of cutting force causes, and then the accurately predicting speed of feed, calculate machining period based on cutter path then.
Described object-oriented and be based on the character representation method of body:
(1) defined feature body at first, the feature body comprises unique identification, the identified surface of feature and the seed identified surface that may become identified surface, and definition seed identified surface is because the development of feature in manufacture process, and feature might be decomposed;
(2) derive the feature class of each application based on the body of feature, class has been inherited the attribute of body, comprise geological information and technique information in the class, demand at the different application field, define different geological information and technique information, thereby realize the integrated and transmission of information of whole manufacturing process.
Beneficial effect of the present invention:
(1) based on the information under body and the OO method expression characteristic different phase different application view, makes the information effective integration of different phase different application view.
(2) the present invention is integrated effectively manufacturing knowledge and experience has reduced the dependence of manufacture process to people's experience.
(3) the present invention can make the information of manufacture process carry out digitizing effectively to express, and the information of process can effective integration, and whole manufacturing process has formed closed-loop control.
(4) the present invention is conducive to the management of production information, provides Information Assurance for enterprise carries out knowledge engineering.
(5) the present invention is conducive to the robotization of production run and intelligent, has improved production efficiency, reduces production costs.
Description of drawings
Fig. 1. based on the manufacture method process flow diagram of feature.
Fig. 2 .(a) typical two-sided aircraft structure front; (b) typical two-sided aircraft structure reverse side.
Fig. 3. cavity feature process decision information needed file sectional drawing.
Fig. 4. cavity feature process decision object information file sectional drawing.
Fig. 5. cavity feature automated programming information needed file sectional drawing.
Among the figure, 1 expression groove, 2 expression muscle, 3 expression holes, 4 expression profiles.
Embodiment
The present invention is further illustrated below in conjunction with drawings and Examples.
A kind of complex parts digital control processing manufacture method based on feature, by express geometry and the technique information of part in the whole manufacturing process based on feature, with the carrier of feature as the manufacturing knowledge and experience, the effective integration of realization information, for the validity that in whole manufacturing process, keeps feature and the continuity of feature development, adopt object-oriented and based on the method representation feature of body:
(1) class of the body of defined feature and each application feature;
(2) carry out feature identification based on the method for holographic attribute face edge graph, from three-dimensional digital model, extract geological information and the technique information of part, the information that does not comprise in the three-dimensional digital model is obtained by associated documents, all features are all carried out unique identification according to feature classification and sequence number, be signature identification=feature classification+sequence number, identify instrument as feature association between different application with this; If feature is decomposed in manufacture process, the sub-sequence number of signature identification=decomposition sign+feature classification+sequence number+decompose then.
The present invention is based on geometry and the technique information of part in the feature representation whole manufacturing process, in order to adapt to the different application field and in whole manufacturing process, to keep the validity of feature and the continuity of feature development, adopt object-oriented and based on the method representation feature of body, based on this, carry out automatic process decision-making, automatic numerical control programming, rearmounted processing, process control, online detection, process optimization and prediction in man-hour based on feature, this method realizes by following steps:
(1) class of the body of defined feature and each application feature;
(2) carry out feature identification based on the method for holographic attribute face edge graph, from three-dimensional digital model, extract geological information and the technique information of part, the information that does not comprise in the three-dimensional digital model is obtained by associated documents, all features are all carried out unique identification according to feature classification and sequence number, be signature identification=feature classification+sequence number, identify instrument as feature association between different application with this; If feature is decomposed in manufacture process, the sub-sequence number of signature identification=decomposition sign+feature classification+sequence number+decompose then;
(3) optimize model based on the feature construction processing technology, the factor of optimizing model comprises the geological information of feature, information, lathe information and the workpiece material information of cutter, that optimizes model is constrained to cutting force and vibration, optimizing the Model Optimization target is working (machining) efficiency, crudy and processing cost, the project of optimizing is cutting-in, cuts wide and speed of feed, sets up the cutting parameter storehouse based on feature; Application-oriented field makes up the cutting test test piece three-dimensional model based on feature, comprises characteristic feature and typical combination thereof, as the master body of checking processing technology;
(4) based on feature representation geological information and the technique information relevant with process decision, for the automatic process decision-making provides complete information support, based on feature representation process rule and process program, and then carry out automatic process based on feature and make a strategic decision, based on the result of feature representation automatic process decision-making;
(5) based on feature representation geological information and the technique information relevant with numerical control programming, simultaneously with the result of automatic process decision-making also as the foundation of automated programming, and then generate processing cutter rail automatically based on feature, based on the result of feature representation automated programming;
(6) handling relevant geometry and technique information based on feature representation with rearmounted, is foundation with the result of automated programming, considers the dynamic perfromance of digital control system, carries out handling based on the postposition of feature, based on the rearmounted result who handles of feature representation, formation numerical control program;
(7) based on relevant geological information and the technique information of feature representation process control, express the processing policy of variety of issue in the process, carry out the status monitoring of process based on feature, carry out the intelligent adaptive adjustment in the process, based on the data in the feature record process, use for process optimization;
(8) geological information and the technique information based on feature forms check point generation standard, optimize gauge head orientation vector and the detection track of the check point of online detection based on the geological information of feature, form the detection analysis result based on feature, as the foundation of optimizing processing technology;
(9) based on the sample storehouse in man-hour of feature construction processing parts, the geological information and the part man-hour that comprise the feature relevant with man-hour man-hour in the sample storehouse, with feature geometries information and technique information as input, make up neural network, the man-hour of prediction part to be processed under the prerequisite that does not have the part by numerical control program is as the foundation that part processing is externally offered and the production schedule is arranged;
(10) geological information and the technique information of adding feature in based on the numerical control program of feature, the dynamic perfromance of combining with digital control lathe and the cutting force in the working angles, the digital control processing man-hour of each operation of accurately predicting is as the foundation of lathe scheduling;
(11) construction feature geological information and technique information be to the mapping model of machine tool capability parameter, satisfies the performance parameter of the required lathe of processing parts based on signature analysis, and application oriented Machine Tool design is provided;
Described object-oriented and be based on the character representation method of body:
(1) defined feature body at first, the feature body comprises unique identification, the identified surface of feature and the seed identified surface that may become identified surface, and definition seed identified surface is because the development of feature in manufacture process, and feature might be decomposed;
(2) derive the feature class of each application based on the body of feature, class has been inherited the attribute of body, comprise geological information and technique information in the class, demand at the different application field, define different geological information and technique information, thereby realize the integrated and transmission of information of whole manufacturing process.
Described process decision step based on feature is:
(1) based on feature representation geological information and the technique information relevant with process decision, wherein geological information is: the maximum tool diameter that the physical dimension of feature, position, geometry, feature allow, and technique information is: material, blank form, process redundancy, machining precision etc.;
(2) carry out according to the order of lathe decision-making, processing sequence decision-making, job operation decision-making and cutting parameter decision-making based on the process decision of feature, form the automatic process result of decision according to process rule and process program;
(3) based on feature representation process decision result formats be: the per pass operation comprises some work steps, comprises some operations under each work step, and each operation is corresponding with a machining feature, contains action type, Processing Strategies and the cutting parameter of feature in the feature.
Described automatic programming method based on feature is:
(1) based on feature representation geological information and the technique information relevant with automated programming, wherein geological information is: the physical dimension of feature, required how much of drivings, cutter rail starting point and terminating point, the cutter rail of generation cutter rail are dodged how several, and technique information comprises: the tool-information of machining feature, the machining precision of feature, cutter rail strategy etc.;
(2) extract machining feature by feature identification, output comprises the feature recognition result of geological information and technique information;
(3) based on the geological information of feature recognition result and process decision result's technique information, generate the cutter rail of each machining feature automatically;
(4) generate the cutter location file of the processing cutter rail of each feature with the unit of being characterized as, according to different application demands, the corresponding information of mark.
Described rearmounted treatment step based on feature is:
(1) in numerical control program, handles relevant geological information and technique information based on feature representation with postposition, wherein geological information refers to: the type of the line in the geometry of feature: straight line, circular arc, conic section, free curve, and the type of face: plane, ruled surface, free form surface, the numerical control processing and operating type during technique information middle finger processing character pair;
(2) characteristic of the digital control system of considering in rearmounted the processing has comprised parameter types such as acceleration characteristic, interpolation precision characteristic, handle of a knife compensation control;
(3) based on feature geometries and technique information, the dynamic perfromance of combining with digital control system is carried out match to cutter location and is formed the polynomial interpolator method that digital control system is supported, and optimizes speed of feed to adapt to complicated digital control processing operating mode.
The described step of controlling based on the process of feature is:
(1) based on feature representation geological information and the technique information relevant with process control, wherein geological information is: corner information, driving geological information etc., and technique information is: process tool, cutting parameter, monitoring policy, detection strategy etc.;
(2) according to geological information and technique information and the processing policy of feature, carry out on-line monitoring, and realize the processing of monitoring result based on artificial intelligence approach;
(3) data of record in the process refer to the variety of issue such as tool wear, the flutter etc. that occur in the operate power, process of cutting parameter actual in the process, lathe; To be characterized as the above data of cellular organization.
Described online detection based on feature may further comprise the steps:
(1) based on feature representation geological information and the technique information relevant with detection, wherein geological information is: the theoretical geometric model of the Noodles type of detected characteristics, curvature variation, area, normal direction, middle machining state etc.; Technique information is: machining precision;
(2) make up the create-rule of the check point of detected characteristics based on the geological information of feature and technique information, generate the check point of detected characteristics based on feature, and consider that the intermediateness optimization of detected characteristics detects track, generate to detect file;
(3) probe is installed and is carried out online detection, record testing result and analysis result, and organize based on feature, feed back to technology department, as the foundation of process optimization.
Described processing technology based on feature is optimized geological information and the technique information that model is considered feature, and wherein, geological information refers to: the physical dimension of feature, feature constitute thickness and the height of geometric element, and these factors are important consideration of rigidity.
In the described accurate machining period Forecasting Methodology based on feature, geological information refers to angle position, the corner size of feature; Technique information refers to chipping allowance, cutting-in, cuts wide, speed of feed; The dynamic perfromance of lathe comprises acceleration, acceleration, the turn performance of lathe, dynamic perfromance based on geological information, technique information and lathe, actual feed in the predicting machine bed motion process, and in conjunction with prediction of Turning Force with Artificial since cutting force the variation of the speed of feed that causes of variation, and then the accurately predicting speed of feed, how much cutter paths are calculated machining period then.
Details are as follows:
Shown in Fig. 1-5.
A kind of complex parts digital control processing manufacture method and system based on feature, its overall procedure is example with a typical two-sided aircraft structure as shown in Figure 1, and the specific implementation process based on the manufacture method of feature is described.
As shown in Figure 2, the feature of aircraft structure can be divided into groove 1, muscle 2, hole 3, profile 4, cavity feature be designated P0001, the muscle feature be designated R0001, hole characteristic be designated H0001, contour feature be designated F0001, the numeral of alphabetical back is determined in other sequence number of place feature class according to each feature.Subcharacter after each feature decomposition sign is: the groove characteristics of decomposition be designated ZP0001-001, the muscle characteristics of decomposition be designated ZR0001-001, the hole characteristics of decomposition be designated ZH0001-001, the profile characteristics of decomposition be designated ZF0001-001; The identified surface of cavity feature is the web face, the seed identified surface is the side, the identified surface of muscle feature is the muscle end face, the seed identified surface is the muscle side, the identified surface of hole characteristic is wall surface of the hole, and the seed identified surface is hole end face up and down, for contour feature, the contoured surface of mark maximum is identified surface, and its lap is all as the seed identified surface.
Mainly be that example illustrates whole process below with the cavity feature, all the other features are all similar.
The class of the body of defined feature and each application feature: defined feature body at first, the feature body comprises unique identification, the identified surface of feature and the seed identified surface that may become identified surface, definition seed identified surface is that feature might be decomposed owing to the development of feature in manufacture process; All features are all carried out unique identification according to feature classification and sequence number, i.e. signature identification=feature classification+sequence number identifies instrument as feature association between different application with this; If feature is decomposed in manufacture process, the sub-sequence number of signature identification=decomposition sign+feature classification+sequence number+decompose then; For example among Fig. 2 the groove 1 of typical two-sided aircraft structure be designated P0001, the identified surface of this cavity feature is the web face of cavity feature;
Derive the feature class of each application based on the body of feature, class has been inherited the attribute of body, comprises geological information and technique information in the class, at the demand in different application field, define different geological information and technique information, thereby realize the integrated and transmission of information of whole manufacturing process.Main several application characterizing definitions are as follows:
The geological information of the cavity feature relevant with process decision is: the center of groove web, the area of groove, the degree of depth, knuckle radius, the maximum tool diameter that allows, the inclination maximum of machined surface; Technique information is: material, blank form are finish forge spare, roughing surplus, machining precision;
The geological information of the cavity feature relevant with automated programming is: the degree of depth of cavity feature, the drive surface of cavity feature process operation, all side wall surfaces such as sidewall processing, cutter rail starting point and terminating point, middle boss is dodged how several, and technique information comprises: the advance and retreat cutter strategy of the process tool of cavity feature, precision, cutter rail, cutting strategy etc.;
Carry out feature identification based on the method for holographic attribute face edge graph, extract geological information and the technique information of part from three-dimensional digital model, the information that does not comprise in the three-dimensional digital model is obtained by associated documents; The feature recognition result is with XML(eXtensible Markup Language, extendible SGML) document form preserves, and makes things convenient for calling of subsequent step.
Be example with Fig. 2 typical case aircraft structure cavity feature 1, recognition result is shown in the XML file of Fig. 5, comprising the sequence number of groove, groove axially, the knuckle radius of groove, the base angle radius, all layers of cavity feature, the summit of each layer, floor height, corner and the base angle radius of layer, web face, side, end face and the base angle face information such as unique identification value in cad model;
Based on feature representation geological information and the technique information relevant with process decision, for the automatic process decision-making provides complete information support.Because it is corresponding related with the static state between the machining information that the machining feature of extracting from part model only is geometry in particular, dynamically associates and exist between each process segment.For avoiding only adopting static machining feature, influence process decision result's accuracy, the feature machining status of processes is constructed, used the process decision method based on the feature machining process status, realize the dynamic and global optimization of process program.At last, the result who makes a strategic decision based on the feature representation automatic process.
A, feature machining status of processes building method are made up of three steps based on the feature destination file of automatic identification: structural attitude processing technology scenario-frame, extract status monitoring position and calculated characteristics process state.
Structural attitude processing technology scenario-frame refers to extract the technological procedure structure relevant with this feature and typical process parameter from typical part level process program.
Extracting the status monitoring positional information refers to according to feature machining process program structure, extract corresponding machining area, and carry out geometric analysis, obtain to be reflected in the crucial cutting position point of feature cutting state under the process program, as IPS(Interim Process State, middle machining state) machining state calculate input.
Calculated characteristics process state is after determining calculating location, and the combined process parameter information calculates the cutting state of crucial cutting position point, finally finishes the structure of feature machining process status.
B, carry out according to the order of lathe decision-making, machining feature ordering decision-making, job operation decision-making and cutting parameter decision-making based on the process decision of feature machining process status.
On the basis of feature machining process status, at first message structure has been carried out layering, conclude longitudinal restraint, horizontal two kinds of relationship types, subordinate, correspondence, quote, in proper order, transmit, restriction totally six kinds of restriction relations the complicated technology relation of process state inside is described.Set up process state process constraint relationship analysis method, decision-making and the optimization of process program is converted into analysis and the processing of six kinds of relations.Process decision method based on the process state can be the reasoning foundation with the process state of reality, and when pursuing local optimum, has taken into account the global optimization of feature machining.
Machining feature ordering decision-making is divided into the ordering of lathe level, the ordering of clamping level and operation level ordering in the process decision.Incorporate in the sequencer procedure with the form of rule by processing knowledge and experience, solved the machining feature ordering difficult problem that the crossing feature that comprises the free shape curved surface causes well.
C, based on feature representation process decision result formats be: the per pass operation comprises some work steps, comprises some operations under each work step, and each operation is corresponding with a machining feature, contains action type, Processing Strategies and the cutting parameter of feature in the feature.
Be that example illustrates the process decision result with the positive finishing of typical aircraft structure shown in Figure 2.Finishing comprises four work steps altogether: finish-milling muscle top, groove web, interior type, and the finish-milling corner, outline is milled in boring.Because use is same as cutter finish-milling muscle top, groove web and interior type, so they are placed in the same work step the corresponding corresponding machining feature of each work step.Processing sequence between feature is arranged according to the order of type, corner, hole, profile in muscle, groove web, the groove.The ordering of same feature is sorted according to the shortest principle in empty cutter track footpath.Groove 1 is the 4th processing in all cavity features.
Be example description operation type and the cutting parameter result of decision with groove 1, this action type is Pocketing, be arranged in 1, the 4 Pocketing operation of work step, the Processing Strategies of this groove is served as reasons, and introversion is outer processes, cutting-in 2mm, cut wide 10mm, high speed fillet 0.5mm, speed of feed 2500mm/min, speed of mainshaft 9000r/min, the cutting number of plies is 2 layers.
Optimize model based on the feature construction processing technology, realize the optimization of many cutting parameters group.The process optimization model comprises geological information, tool-information, lathe information and the workpiece material information of feature.In cut, cutting speed, cutting depth, speed of feed are decided to be the cutting data three elements.Therefore process optimization mainly refers to obtain optimum cutting speed, cutting depth and speed of feed.In addition, also comprise Selection of Cutting etc.The target of process optimization is working (machining) efficiency, crudy and processing cost.Set up corresponding objective function according to optimization aim, i.e. high efficiency objective function, crudy highest goal function and minimum manufacturing cost objective function.
The major influence factors of high efficiency objective function has: operation cutting time, tool change time, clamping time and other non-cutting times.The principal element that wherein influences the operation cutting time is: cutting depth, cutting width, cutting speed, speed of feed etc.; The factor that influences tool change time is tool change time and number of changing knife, and tool life has influenced number of changing knife again.These factors are set up corresponding influence coefficient, unified by all factors are integrated, set up high efficiency objective function;
The influence factor of crudy highest goal function has: part material performance, cutting speed, speed of feed etc.Be similarly influence factor and set up influence coefficient, set up objective function.
The influence factor of minimum manufacturing cost objective function has: the blank expense, in the cutting time required expense, the cost of charp tool, clamping in the time required expense and in non-cutting time required expense.For influence factor is set up influence coefficient, finally set up the least cost objective function.
According to three optimization aim functions cutting parameter and cutter are optimized.The constraint condition that needs to consider in optimizing process is:
1. security constraint, namely cutting force, the cutting moment of torque etc. can not surpass the allowed band of lathe, cutter, anchor clamps and workpiece;
2. ability constraint, namely the speed of mainshaft, speed of feed, main shaft acceleration, feeding acceleration etc. can not surpass the performance index of lathe;
3. qualitative restrain, behind the process optimization, workpiece must satisfy given machining precision and surface quality;
4. vibration constraint, in the process, tangible vibration can not appear in cutter, workpiece, lathe.
Foundation is based on the cutting parameter storehouse of feature, and storage is used for the automatic process decision references based on the cutting parameter of feature process optimization model optimization.
At last, application-oriented field makes up the cutting test test piece three-dimensional model based on feature, as the master body of checking processing technology.Wherein three bit models comprise characteristic feature and typical combination thereof.
Based on feature representation geological information and the technique information relevant with numerical control programming, simultaneously with the result of automatic process decision-making also as the foundation of automated programming, and then generate processing cutter rail automatically based on feature, based on the result of feature representation automated programming; Based on the automatic numerical control programmed algorithm of feature comprise mainly that activation bit obtains, the generation of cutter rail, machining simulation and rearmounted the processing.
Activation bit comprises driving geological information, processing mode and parameter information, feeding mode and parameter information three classes.Wherein, processing mode and parameter, feeding mode and parameter directly provide by data modes such as numeral, letters, drive in the geological information that then lies in machining feature for how much, need the treated data mode that cutter rail generating algorithm can directly be used that be converted to.
How much differences of the required driving of different characteristic processing cutter rail generating algorithm.Be processed as example at this with typical groove web, set forth the process of machining feature reconstruct.
Processing mode and parameter mainly comprise: cutter rail type (Tool Path Style), cutting direction (Direction Of Cut), machining precision (Machining Tolerance), cutter (Tool), tool diameter D, tooth radius R c at the bottom of the cutter, the long Lc of sword, cutter work length L, cut wide (Radial Distance Between Path), cutting-in (Axial Maximum Depth Of Cut), radius of corner (Corner Radius), fillet circular arc central angle (Limit Angle), the tangential development length of fillet (Extra Segment overlap), transition arc radius (Transition Radius), transition angle (Transition Angle), transition line length (Transition Length), the fillet rate of deceleration (Reduction Rate), the minimum central angle (Minimum Angle) in fillet angle, fillet deceleration maximum radius (Maximum Radius), fillet deceleration preset distance (Distance Before Corner), fillet are slowed down and are cancelled distance (Distance After Corner).
Feeding mode and parameter mainly comprise: feed mode (Approach), screw diameter (Horizontal Safety Distance), spiral height (Vertical Safety Distance), helix angle (Ramping Angle), feed velocity (Approach Feed Speed), withdrawing mode (Retract), withdrawing circular arc central angle (Angular Sector), the withdrawing circular arc is towards (Orientation), withdrawing arc radius (Radius), withdrawing speed (Retract Feed Speed), process velocity (Machining Feed Speed), the speed of mainshaft (Spindle Speed).
Employing generates how much of drivings based on the method for ring analysis.This method solved web finishing zone create in after many, the cutting of the classification situation of curve cutting line segment accept or reject and judge complexity and the problem of easily makeing mistakes; Can to close the angle, open and close the angle and deposit and altogether the cavity feature web machining area of types such as side create and unifiedly calculate, the algorithm versatility is good, has improved the efficient of web finishing programming effectively.These method concrete steps are as follows:
1. with the outer shroud border of the broad sense side of cavity feature and broad sense bottom surface to the projection of web face;
2. obtain comprising the planar surface encloses profile of straight line, circular arc and SPL by projection;
3. approach drop shadow curve's section with straight-line segment, all straight-line segments are asked to hand over cut apart;
4. for the needs of ring analysis vector rotation, the planar surface encloses profile is separated into a series of straight-line segments, and asks friendship to cut apart the planar surface encloses profile that generation only comprises straight-line segment to it;
5. adopt the straight-line segment rotary process that the planar surface encloses profile that only comprises straight-line segment is analyzed, obtain the ring of all sealings;
6. search target ring in encircling from all;
7. calculate web finishing zone according to the target ring, obtain driving how much.
By above activation bit, utilize ripe cutter rail algorithm to generate numerical control program.
The type of inserting line in numerical control program, for different cutter location segmentations, i.e. straight line, circular arc, conic section, free curve, and the type of face, it is the plane, ruled surface, geological informations such as free form surface, the technique informations such as numerical control processing and operating type when inserting the processing character pair simultaneously; The characteristic of the digital control system of considering in rearmounted the processing has comprised parameter types such as acceleration characteristic, interpolation precision characteristic, handle of a knife compensation control; Based on feature geometries and technique information, the dynamic perfromance of combining with digital control system is carried out match to cutter location and is formed the polynomial interpolator method that digital control system is supported, and optimizes speed of feed to adapt to complicated digital control processing operating mode.Postposition processing based on feature comprises following steps:
Step 4, the curve of match and original some position are compared, calculate error of fitting, if error of fitting overshoot value, then the segment of curve of match finishes, directly produce corresponding NC program in the typical curve program of substitution good correspondence prepared in advance, re-enter discrete cutter rail point position then and carry out match, namely repeated for second step to the 4th step, till entire curve Duan Jun has corresponding NC program output.
All be the cutter rail point position of dispersing in step 2, the some position file, algorithm reads a position successively, constantly generates the NC program.Whenever read a discrete point position system and can judge that all establishing current input point position is Pi, system can judge that Pi point position cutter rail of living in is straight line or curve according to how much keywords of the residing driving of Pi.
If Pi was in straight line or circular arc cutter rail after step 3, process were judged, and the some position before the Pi is in curve (non-circular arc) cutter rail, then export Pi curve interpolating NC program before, export the corresponding straight line of Pi or circular interpolation NC program then, change step 2 then.The program example of NC program cathetus and circular interpolation is as follows:
Step 4, if some position belongs to the part of curve cutter rail, need to continue input point position Pi, more than 5, carry out match at the discrete cutter rail point that belongs to segment of curve up to the some position for the treatment of match, obtain the conventionally form equation of conic section.
The conventional expression way of equation of conic section is Ax2+Bxy+Cy2+Dx+Ey+1=0, and x, y are that parameter and A, B, C, D, E are real number, and A, B, C are all non-vanishing.Comprised ellipse under this form, three kinds of forms of hyperbolic curve and para-curve.
Utilize this equation that least square fitting is carried out in a position, to obtain each coefficient in the equation.
Step 5, because the requirement difference of different NC system NC program input need be carried out the parameter conversion to equation.
Step 6, error is carried out in matched curve judge.When increased the match point position, matched curve may increase with the error of putting interdigit, at this moment need begin matched curve with new some position.The error judgement adopts discrete cutter rail point position to represent to the minor increment of institute's matched curve.If the error of match, is then exported the last error that generates greater than the error that sets with the corresponding conic section interpolation of interior matched curve NC program, and returns step 2.
Step 7, if the error of match less than the error that sets, is then returned step 2, add new some position again.If new some position belongs to straight line cutter rail, change step 3 over to, judge whether to export the conic section NC program of last match, and output linear interpolation NC program; If new some position belongs to curve cutter rail, then change step 4 over to, continue matched curve.
Generation comprises geological information based on the process control documents of feature representation: corner information, driving geological information etc., and technique information: process tool, cutting parameter, monitoring policy, detection strategy etc.; Geological information and technique information and processing policy according to feature carry out on-line monitoring, and realize the processing of monitoring result based on artificial intelligence approach; The data of record in the process refer to the variety of issue such as tool wear, the flutter etc. that occur in the operate power, process of cutting parameter actual in the process, lathe; To be characterized as the above data of cellular organization.Step 1, based on the feature organization geometry processes information relevant with process, form the process control information.Based on feature organization geometry processes information comprise characteristic type, use cutter, feed strategy, cut wide, cutting-in, the speed of mainshaft,
Based on feature representation geological information and the technique information relevant with detection, wherein geological information is: the theoretical geometric model of the Noodles type of detected characteristics, curvature variation, area, normal direction, middle machining state etc.; Technique information is: machining precision; Make up the create-rule of the check point of detected characteristics based on the geological information of feature and technique information, generate the check point of detected characteristics based on feature, and consider that the intermediateness optimization of detected characteristics detects track, generate to detect file; Probe is installed is carried out online detection, record testing result and analysis result, and organize based on feature, feed back to technology department, as the foundation of process optimization.Described online test method based on feature representation, this method may further comprise the steps:
Step 4, according to geological information and the technique information of feature of input, create under the canonical frame at online detection check point, judge the type of detected characteristics, if " axle " or plane regular body are counted and the definite method that distributes according to equidistant detection; If curved surface then utilizes the automatic creation method of curved surface check point based on the curvature transformation matrices, generate online detection check point automatically; To the detected characteristics that has special detection to require, need manually the detection data point of automatic generation to be intervened processing, to obtain satisfied check point data;
Step 5, based on detected state, by detecting three grades of operation levels, detected characteristics level, feature self level, carry out the online detection path planning of the unit of being characterized as;
Step 6, postposition processing is carried out in the detection path of previous step planning, to obtain detected characteristics and detect data, make in the check point data coordinates of design under the coordinate system by coordinate transform and to transform under the detection coordinates system, generate detection NC code and characterization processes file according to routing information and lathe configuration information simultaneously, transfer to numerically-controlled machine and carry out online detection;
Step 7, utilize contact electricity touch gauge head to measure the actual coordinate of check point, the acquisition actual coordinate is carried out error compensation, obtain final needed correction data;
Step 8, will compare through the data of error compensation and the theoretical value in the online detection policy paper, analyze, handle according to the requirement in the online detection policy paper, draw complete testing result and final quality assessment conclusion, and then whether the assessment processing parts meet the demands and adjust strategy, and testing result also can be used as the foundation of process optimization.
In the neural network Forecasting Methodology in man-hour based on feature, geological information and technique information are respectively: geological information refers to the quantity of entire area, part height, groove, muscle, hole, contour feature; The material of technique information nulling spare, process required lathe.Neural network Forecasting Methodology in man-hour based on feature may further comprise the steps:
A. about the muscle feature: for the horizontal bar top, the characteristic parameter that influences its process time is the degree of the processing drive wire on horizontal bar top; For the diagonal bar top, the characteristic parameter that influences its process time is the processing drive wire length on diagonal bar top and the angle of inclination on diagonal bar top; For arc muscle top, influence the characteristic parameter of its process time and be the processing drive wire length on arc muscle top; Figure 2 shows that typical muscle top feature synoptic diagram;
B. about cavity feature: for groove web feature, the characteristic parameter that influences its process time is the area of web, wherein also is used as the processing of web face if contain boss in the groove; For type in the groove, the characteristic parameter that influences its process time is interior type drive wire length and groove height; For the groove corner, the characteristic parameter that influences its process time is knuckle radius, corner angle, the corner degree of depth;
C. about profile: the characteristic parameter that influences its process time is the girth of parts profile and the height of profile;
D. about circular hole: the characteristic parameter that influences its process time is the degree of depth of radius and the circular hole of circular hole;
E. about non-circular hole: the characteristic parameter that influences its process time is the degree of depth of girth and the non-circular hole of non-circular hole;
Above feature and the identification of parameter use characteristic thereof realize and save as characteristic information.
For type feature, hole characteristic in muscle top feature, the groove, the cutting parameter that influences its process time is: rotating speed, speed of feed, cutting-in;
For groove web feature, groove corner feature, contour feature, non-circular hole feature, the cutting parameter that influences its process time is: rotating speed, speed of feed, cut wide, cutting-in;
Step 4, carry out cutting simulation experiment, to be characterized as research object, based on the cutting parameter of typical parts machining process and refinement, by the size characteristic of continuous transform characteristics, form storehouse process time based on feature the process time of the feature of record different size in the process of the test.The selection principle of characteristic parameter is as follows:
A, muscle top: 1) for the horizontal bar top, its processing drive wire length range is decided to be 10～1500mm, and the processing drive wire length of the horizontal bar top sample of choosing from experimental result is in above-mentioned scope, and the data that penguin is chosen satisfy the requirement of normal distribution; 2) push up for diagonal bar, its processing drive wire length range is decided to be 10～200mm, the inclination angle scope is decided to be 10～80 °, and processing drive wire length and the inclination angle scope of choosing diagonal bar top sample from experimental result are in above-mentioned scope, and the data of choosing satisfy the requirement of normal distribution; 3) for arc muscle top, its processing drive wire length range is decided to be the arc muscle top sample that 10～100mm chooses from experimental result processing drive wire length is in above-mentioned scope, and the data of choosing satisfy the requirement of normal distribution; Each class muscle top feature is got 50 groups of sample datas;
B, groove: 1) for the groove web, its areal extent is orientated 0.001～0.1m as
2, the groove web sample area parameter of choosing is distributed in this scope with reference to normal distribution; 2) for type in the groove, type drive wire length range is decided to be 100～1200mm in the groove, and the moldeed depth degree is decided to be 3～100mm in the groove, and drive wire length and the degree of depth of type feature are distributed in described scope according to normal distribution in the groove of choosing, and get 60 groups of data altogether; 3) for the groove corner, the knuckle radius data are decided to be 6mm, 10mm, 12mm, 16mm, 20mm, the angle data are decided to be 30 °, 45 °, 60 °, 75 °, 90 °, 120 °, the corner depth range is decided to be 3～100mm, depth data satisfies the normal distribution requirement, gets 160 groups of groove corner sample datas altogether;
C, profile: the girth scope is 1000～6000mm, and the profile height is decided to be 20～180mm, and the girth of the profile of choosing and profile altitude information are distributed in above-mentioned scope according to normal distribution, chooses 40 groups of sample datas;
D, circular hole: pore diameter range is decided to be 8～80mm, the hole depth scope is decided to be 3～50mm, the diameter data in the hole of choosing is decided to be: 8mm, 10mm, 20mm, 30mm, 60mm, 80mm, and depth data in above-mentioned scope, is respectively got 50 groups of data by normal distribution under different machining precision situations;
E, non-circular hole: the girth scope of non-circular hole is decided to be 10～1000mm, and the non-circular hole degree of depth is decided to be 3～50mm, and the girth of the non-circular hole of choosing and depth data are distributed in this scope according to normal distribution, chooses 50 groups of sample datas;
Step 5, set up feature and feature machining corresponding time relationship, each category feature is all set up the feature example of some.Form characteristic feature sample storehouse.
Step 6, based on the characteristic feature sample storehouse of setting up, set up corresponding with it BP network to be characterized as object.The input node of BP network is the geometrical characteristic parameter of each category feature, relevant cutting parameter and other possible influence factors, and the output of BP network namely is the machining period of each category feature.The BP network that sample training by the feature samples storehouse goes out each category feature row labels of going forward side by side is preserved.
For predicting the process time of new part example, at first obtain the feature of this part, with the input as the corresponding BP neural network that has trained of above-mentioned feature geometries parameter and cutting parameter, predict, the output of BP network namely is the process time of feature, at last the total process time that obtains part process time by all features of part that add up.
In the accurate machining period Forecasting Methodology based on feature, geological information refers to angle position, the corner size of feature; Technique information refers to chipping allowance, cutting-in, cuts wide, speed of feed; The dynamic perfromance of lathe comprises acceleration, acceleration, the turn performance of lathe, dynamic perfromance based on geological information, technique information and lathe, actual feed in the predicting machine bed motion process, and in conjunction with prediction of Turning Force with Artificial since cutting force the variation of the speed of feed that causes of variation, and then the accurately predicting speed of feed, how much cutter paths are calculated machining period then.Accurate machining period Forecasting Methodology based on feature may further comprise the steps:
Lathe property information is the form storage with knowledge base, is divided into three ingredients: lathe storehouse, digital control system storehouse and speed control method storehouse.The lathe storehouse comprises the performance parameter of lathe title, the employed digital control system of lathe, lathe such as peak power, maximal rate, peak acceleration etc.
Step 4, because the complicacy that the cutting speed local value arranges in the numerical control programming, present numerical control programming only is provided with local cutting speed at key position such as corner etc., overall cutting speed is then used in other positions, because cutting-in is cut wide inhomogeneously in the actual processing, the intelligent adjustment module of modern machine such as Artis can optimize cutting speed in process.
The cutting power demand is by cutting force and cutting speed decision, that is:
p=fv
P is the cutting power demand in the formula, and f is cutting force, and v is cutting speed.
Cutting force with cutting speed, cutting-in, cut wide, cutting material, cutter material is relevant with processing mode, but its calculating index cutting force computation model.
In the process of prediction, cutting speed be to calculate the speed of gained according to the kinetic characteristic of lathe, and cutting-in need be extracted from characteristic information, and actual cutting widely has difference with setting value in the feature in the actual cut process, need be according to the machining state calculating of part.In order to improve counting yield, the calculating of cutting power is only calculated just now on the ground that might go wrong, according to the characteristics of feature machining, because the processing of muscle feature end face and cavity feature web is after roughing, surplus is bigger, and it is higher the big likelihood ratio of power demand to occur, as calculating object.Obtain the cutting power of working angles, the power ratio that can provide with rated output and lathe, if the power that the power ratio lathe that calculates can provide is little, then can be according to the value operation of NC program setting, otherwise, needing to adjust, method of adjustment is: according to the computing formula of power, reduce speed gradually, up to meeting the demands.
Step 5, carry out accurate operation level based on feature and calculate man-hour, the process time of numerical control operation is by forming the process time of each work step.
The cutter rail of feature comprises quick knife edge rail, advance and retreat cutter cutter rail and bite rail, so also be made up of quick positioning time, advance and retreat cutter time and cutting time the process time of feature.
The cutter rail of feature is made up of quick knife edge rail, advance and retreat cutter cutter rail and bite rail, and the knife edge rail is simple fast, and the theory of computation time gets final product.The advance and retreat cutter changes comparatively complicated, if utilize the analytic function of lathe property to calculate, efficient is lower, because the advance and retreat cutter is the part of feature cutter rail strategy, belong to the knowledge and experience of feature carrying, every kind of feature has corresponding advance and retreat cutter strategy, analyzes by experiment, draw the scale-up factor of actual process time with the theoretical time of every kind of feature advance and retreat cutter, the time that can draw advance and retreat cutter part.
The part that the present invention does not relate to prior art that maybe can adopt same as the prior art is realized.
Claims (10)
1. complex parts digital control processing manufacture method based on feature, it is characterized in that at first reading in the three-dimensional digital model of part to be processed, secondly, carry out automated characterization identification, from the three-dimensional digital model that reads in, extract geological information and the technique information of part to be processed, the information that does not comprise in the three-dimensional digital model is by obtaining with the supporting related process file of digital-to-analogue, and geometry and the technique information that extracts carried out the characterization mark, all features are all carried out unique identification according to feature classification and sequence number, be signature identification=feature classification+sequence number, identify instrument as feature association between different application with this; If feature is decomposed in manufacture process, signature identification=decompositions sign+feature classification+sequence number+decompose sub-sequence number then, thus realize with the carrier of feature as the manufacturing knowledge and experience effective integration of realization information; The 3rd, for the validity of maintenance feature in whole manufacturing process and the continuity of feature development, adopt object-oriented and be the body of defined feature and the class of each application feature based on the method representation feature of body, adopt the method for holographic attribute face edge graph to carry out feature identification; At last, the process process optimization generates the nc program of complex parts automatically, drives numerically controlled processing equipment and finishes whole process.
2. method according to claim 1, it is characterized in that after carrying out automated characterization identification and finishing again based on the feature construction processing technology optimize model, based on feature carry out the automatic process decision-making, based on feature carry out the automatic numerical control programming, based on feature carry out rearmountedly handling, based on feature carry out process control, based on feature carry out online detection, based on feature construction processing parts man-hour the sample storehouse and construction feature geological information and technique information to the mapping model of machine tool capability parameter;
(1) describedly should comprise the geological information of feature, information, lathe information and the workpiece material information of cutter when optimizing model based on the feature construction processing technology, that optimizes model is constrained to cutting force and vibration, optimizing the Model Optimization target is working (machining) efficiency, crudy and processing cost, the project of optimizing is cutting-in, cuts wide and speed of feed, sets up the cutting parameter storehouse based on feature; Application-oriented field makes up the cutting test test piece three-dimensional model based on feature, comprises characteristic feature and typical combination thereof, as the master body of checking processing technology;
It is (2) described that to carry out automatic process decision-making based on feature be to provide complete information support for the automatic process decision-making, based on feature representation process rule and process program, and then carry out automatic process based on feature and make a strategic decision, based on the result of feature representation automatic process decision-making;
(3) describedly carry out automatic numerical control programming based on feature and refer to the result of automatic process decision-making also as the foundation of automated programming, and then generate processing cutter rail automatically based on feature, based on the result of feature representation automated programming;
(4) described to carry out rearmounted the processing based on feature be that result with automated programming is foundation, considers the dynamic perfromance of digital control system, carries out handling based on the postposition of feature, based on the rearmounted result who handles of feature representation, forms nc program;
It is (5) described that to carry out process control based on feature be for expressing the processing policy of the problem that occurs in the process, comprise the status monitoring that carries out process based on feature, carry out the intelligent adaptive adjustment in the process, based on the data in the feature record process, use for process optimization;
(6) describedly carry out online detection based on feature and refer to that forming check point based on the geological information of feature and technique information generates standard, gauge head orientation vector and detection track when optimizing online detection based on the geological information of feature, form the detection analysis result based on feature, as the foundation of optimizing processing technology;
(7) the described geological information that comprises the feature relevant with man-hour man-hour in the sample storehouse and part man-hour based on feature construction processing parts, with feature geometries information and technique information as input, make up neural network, the man-hour of prediction part to be processed under the prerequisite that does not have the part by numerical control program is as the foundation that part processing is externally offered and the production schedule is arranged; The geological information and the technique information that in based on the numerical control program of feature, add feature, the dynamic perfromance of combining with digital control lathe and the cutting force in the working angles, the digital control processing man-hour of each operation of accurately predicting is as the foundation of lathe scheduling;
(9) described construction feature geological information and technique information are based on the performance parameter that signature analysis satisfies the required lathe of processing parts to the mapping model of machine tool capability parameter, and application oriented lathe correlation parameter design reference is provided.
3. method according to claim 2 is characterized in that described step of carrying out the automatic process decision-making based on feature is:
(1) based on feature representation geological information and the technique information relevant with process decision, wherein geological information is: the maximum tool diameter that the physical dimension of feature, position, geometry, feature allow, and technique information is: material, blank form, process redundancy and machining precision;
(2) carry out according to the order of lathe decision-making, processing sequence decision-making, job operation decision-making and cutting parameter decision-making based on the process decision of feature, form the automatic process result of decision according to process rule and process program;
(3) based on feature representation process decision result formats be: the per pass operation comprises some work steps, comprises some operations under each work step, and each operation is corresponding with a machining feature, contains action type, Processing Strategies and the cutting parameter of feature in the feature.
4. method according to claim 2 is characterized in that described automatic numerical control programmed method based on feature is:
(1) based on feature representation geological information and the technique information relevant with automated programming, wherein geological information is: the physical dimension of feature, required how much of drivings, cutter rail starting point and terminating point and the cutter rail of generation cutter rail dodge how much, and technique information comprises: the tool-information of machining feature, the machining precision of feature and cutter rail strategy;
(2) extract machining feature by feature identification, output comprises the feature recognition result of geological information and technique information;
(3) based on the geological information of feature recognition result and process decision result's technique information, generate the cutter rail of each machining feature automatically;
(4) generate the cutter location file of the processing cutter rail of each feature with the unit of being characterized as, according to different application demands, the corresponding information of mark.
5. method according to claim 2 is characterized in that described rearmounted treatment step based on feature comprises:
(1) in numerical control program, handles relevant geological information and technique information based on feature representation with postposition, wherein geological information refers to: the type of the line in the geometry of feature: straight line, circular arc, conic section, free curve, and the type of face: plane, ruled surface, free form surface, the numerical control processing and operating type during technique information middle finger processing character pair;
(2) characteristic of the digital control system of considering in rearmounted the processing has comprised acceleration characteristic, interpolation precision characteristic and handle of a knife compensation control parameter type;
(3) based on feature geometries and technique information, the dynamic perfromance of combining with digital control system is carried out match to cutter location and is formed the polynomial interpolator method that digital control system is supported, and optimizes speed of feed to adapt to complicated digital control processing operating mode.
6. method according to claim 2 is characterized in that the step of described process control based on feature comprises:
(1) based on feature representation geological information and the technique information relevant with process control, wherein geological information is: corner information and driving geological information, and technique information is: process tool, cutting parameter, monitoring policy and detection strategy;
(2) according to geological information and technique information and the processing policy of feature, carry out on-line monitoring, and realize the processing of monitoring result based on artificial intelligence approach;
(3) data of record in the process refer to the tool wear and the flutter that occur in the operate power, process of cutting parameter actual in the process, lathe; To be characterized as the above data of cellular organization.
7. method according to claim 2 is characterized in that described online detection based on feature may further comprise the steps:
(1) based on feature representation geological information and the technique information relevant with detection, wherein geological information is: the theoretical geometric model of the Noodles type of detected characteristics, curvature variation, area, normal direction and middle machining state; Technique information is: machining precision;
(2) make up the create-rule of the check point of detected characteristics based on the geological information of feature and technique information, generate the check point of detected characteristics based on feature, and consider that the intermediateness optimization of detected characteristics detects track, generate to detect file;
(3) probe is installed and is carried out online detection, record testing result and analysis result, and organize based on feature, feed back to technology department, as the foundation of process optimization.
8. method according to claim 2, it is characterized in that described geological information and the technique information of considering feature based on the processing technology optimization model of feature, wherein, geological information refers to: as the physical dimension of the feature of rigidity important consideration, thickness and the height that feature constitutes geometric element.
9. method according to claim 2, it is characterized in that described based on feature construction processing parts man-hour sample storehouse time institute foundation geological information comprise angle position, corner size etc.; Technique information comprises chipping allowance, cutting-in, cuts wide main speed of feed; The dynamic perfromance of lathe comprises acceleration, acceleration and the turn performance of lathe, dynamic perfromance based on geological information, technique information and lathe, actual feed in the predicting machine bed motion process, and in conjunction with prediction of Turning Force with Artificial because the variation of the speed of feed that the variation of cutting force causes, and then the accurately predicting speed of feed, calculate machining period based on cutter path then.
10. method according to claim 1 is characterized in that, described object-oriented and be based on the character representation method of body:
(1) defined feature body at first, the feature body comprises unique identification, the identified surface of feature and the seed identified surface that may become identified surface, and definition seed identified surface is because the development of feature in manufacture process, and feature might be decomposed;
(2) derive the feature class of each application based on the body of feature, class has been inherited the attribute of body, comprise geological information and technique information in the class, demand at the different application field, define different geological information and technique information, thereby realize the integrated and transmission of information of whole manufacturing process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310103134.1A CN103235556B (en) | 2013-03-27 | 2013-03-27 | The complex parts digital control processing manufacture method of feature based |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310103134.1A CN103235556B (en) | 2013-03-27 | 2013-03-27 | The complex parts digital control processing manufacture method of feature based |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103235556A true CN103235556A (en) | 2013-08-07 |
| CN103235556B CN103235556B (en) | 2015-08-19 |
Family
ID=48883602
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201310103134.1A Active CN103235556B (en) | 2013-03-27 | 2013-03-27 | The complex parts digital control processing manufacture method of feature based |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103235556B (en) |
Cited By (55)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103439917A (en) * | 2013-09-02 | 2013-12-11 | 南京航空航天大学 | Cutting force prediction method based on features |
| CN103699055A (en) * | 2013-12-24 | 2014-04-02 | 沈阳飞机工业（集团）有限公司 | Intelligent numerical control machining programming system and intelligent numerical control machining programming method for aircraft structural parts |
| CN103926879A (en) * | 2014-05-06 | 2014-07-16 | 南京航空航天大学 | Aviation engine crankcase feature recognition method |
| CN104331020A (en) * | 2014-11-12 | 2015-02-04 | 南京航空航天大学 | Feature-based skin mirror-image milling technology decision method |
| CN104360634A (en) * | 2014-11-12 | 2015-02-18 | 南京航空航天大学 | Skin mirror image milling numerical control program fast generating method based on features |
| CN104570948A (en) * | 2015-01-16 | 2015-04-29 | 南京航空航天大学 | Hole feature machining method for spaceflight sheet part |
| CN105242639A (en) * | 2015-11-03 | 2016-01-13 | 南京航空航天大学 | Numerical control machining feature customizing method |
| CN105549537A (en) * | 2015-12-23 | 2016-05-04 | 上海电机学院 | Assembly parameterization and automation numerical control processing method based on material object scanning |
| CN105759724A (en) * | 2016-03-31 | 2016-07-13 | 孙丽君 | Intelligent programming method |
| CN106200558A (en) * | 2016-08-26 | 2016-12-07 | 大连大学 | Nc program based on model generates method |
| CN106514129A (en) * | 2017-01-03 | 2017-03-22 | 南京航空航天大学 | Numerical control programming nonuniform margin configuration method based on machining characteristic intermediate state rigidity |
| CN106815438A (en) * | 2017-01-17 | 2017-06-09 | 成都飞机工业（集团）有限责任公司 | A kind of Flight Structures NC Machining milling efficiency main affecting factors extracting method |
| CN106938404A (en) * | 2016-01-05 | 2017-07-11 | 财团法人工业技术研究院 | Dynamic workpiece planar tilt method for measurement |
| TWI600988B (en) * | 2016-11-15 | 2017-10-01 | Machining expert system for CNC machine tool and controller processing parameters generation method | |
| CN107247445A (en) * | 2017-06-29 | 2017-10-13 | 南京航空航天大学 | Consider the knife rail mapping method of approximate error |
| CN107272594A (en) * | 2016-04-04 | 2017-10-20 | 菲尔斯系统有限责任公司 | Plan method and its program and system of execution that the computer system of technique is performed |
| CN107562015A (en) * | 2017-08-29 | 2018-01-09 | 沈阳航空航天大学 | A kind of process geometrical model construction method based on NC Machining Program |
| CN107584334A (en) * | 2017-08-25 | 2018-01-16 | 南京航空航天大学 | A kind of complex structural member numerical control machining cutter status real time monitor method based on deep learning |
| CN107728580A (en) * | 2017-10-18 | 2018-02-23 | 南京航空航天大学 | A kind of geological information technique information monitoring information correlating method towards adaptive machining |
| CN108027604A (en) * | 2015-09-29 | 2018-05-11 | 西门子公司 | For optimally controlling the method and control device of lathe |
| CN108228996A (en) * | 2017-12-28 | 2018-06-29 | 重庆平伟汽车科技股份有限公司 | A kind of system and method for the device to hole automated programming based on hole characteristic |
| CN108268012A (en) * | 2018-02-01 | 2018-07-10 | 南京航空航天大学 | A kind of machine tool system remodeling method of Parametric drive production |
| CN108520139A (en) * | 2018-03-30 | 2018-09-11 | 北京理工大学 | A kind of various dimensions Fixture Design knowledge component construction method |
| CN108985617A (en) * | 2018-07-11 | 2018-12-11 | 广东人励智能工程有限公司 | A kind of product manufacturing process dispatching method and system based on intelligence manufacture |
| CN109375583A (en) * | 2018-11-16 | 2019-02-22 | 中国航发西安动力控制科技有限公司 | For the numerical control program preparation method of hole in piece part system manufacturing feature |
| CN109725600A (en) * | 2017-10-30 | 2019-05-07 | 发那科株式会社 | Preprocessor device, processing program generation method, CNC system of processing and computer-readable information recording medium |
| CN109773589A (en) * | 2018-12-29 | 2019-05-21 | 深度创新科技（深圳）有限公司 | Method and device, the equipment of on-line measurement and processing guiding are carried out to workpiece surface |
| CN110096033A (en) * | 2019-05-09 | 2019-08-06 | 江苏南高智能装备创新中心有限公司 | A method of check and correction numerical control program precision |
| CN110100213A (en) * | 2016-12-26 | 2019-08-06 | 三菱电机株式会社 | Manufacturing procedure generating apparatus, manufacturing procedure generation method and program |
| CN110458606A (en) * | 2019-07-19 | 2019-11-15 | 西北工业大学 | Nonstandard cutter price expectation method based on deep learning |
| CN111353255A (en) * | 2018-12-05 | 2020-06-30 | 财团法人工业技术研究院 | Automatic generation system of processing parameters |
| CN111679630A (en) * | 2020-06-30 | 2020-09-18 | 中国航发动力股份有限公司 | Quick programming method for numerical control machining of engine case |
| CN111768099A (en) * | 2020-06-29 | 2020-10-13 | 山东浪潮通软信息科技有限公司 | Model part-based task decomposition method in scientific research and production mode |
| CN111813048A (en) * | 2020-06-30 | 2020-10-23 | 中国航发动力股份有限公司 | Function integration numerical control program generation method, system, device and readable storage medium |
| CN111861147A (en) * | 2020-06-29 | 2020-10-30 | 北京航空航天大学 | Digital detection method and system for whole process of manufacturing complex parts |
| CN112327755A (en) * | 2020-11-16 | 2021-02-05 | 广州傲创智能科技有限公司 | Automatic frame identification method for die carrier |
| CN112364447A (en) * | 2020-09-30 | 2021-02-12 | 深圳市为汉科技有限公司 | Method and related device for generating part model |
| CN112355713A (en) * | 2020-09-24 | 2021-02-12 | 北京航空航天大学 | Method, device and equipment for predicting cutting force based on image |
| CN112884246A (en) * | 2021-03-17 | 2021-06-01 | 成都永峰科技有限公司 | Working hour prediction method for airplane structural member machining process |
| CN113190888A (en) * | 2021-04-20 | 2021-07-30 | 东风柳州汽车有限公司 | Process documentation method, device, equipment and storage medium |
| EP3859466A1 (en) * | 2020-01-30 | 2021-08-04 | DENTSPLY SIRONA Inc. | Dental machining system for predicting the machining time for manufacturing a dental restoration/appliance |
| CN113341882A (en) * | 2021-06-28 | 2021-09-03 | 成都飞机工业（集团）有限责任公司 | Numerical control process design and optimization method based on processing knowledge |
| WO2021185661A1 (en) * | 2020-03-20 | 2021-09-23 | Dentsply Sirona Inc. | Dental machining system for generating process parameters of the machining |
| CN113485249A (en) * | 2021-06-22 | 2021-10-08 | 哈尔滨东安汽车动力股份有限公司 | Rapid profiling operation method for new sample of automobile engine part |
| CN113811904A (en) * | 2019-05-08 | 2021-12-17 | 西门子股份公司 | Generating assembly plans and three-dimensional assembly models |
| CN113850289A (en) * | 2021-08-17 | 2021-12-28 | 中国电子科技集团公司第二十九研究所 | Method and system for measuring and calculating working procedure working hour quota of radio frequency microwave part product |
| CN114660994A (en) * | 2022-05-25 | 2022-06-24 | 中科航迈数控软件(深圳)有限公司 | Decision optimization method, system and related equipment for machining process of numerical control machine tool |
| CN114675599A (en) * | 2022-05-27 | 2022-06-28 | 中科航迈数控软件(深圳)有限公司 | Machining feature sorting method and system of numerical control machine tool |
| CN114714151A (en) * | 2022-06-09 | 2022-07-08 | 成都飞机工业（集团）有限责任公司 | Method, device and equipment for planning measurement point positions of web and storage medium |
| CN115618530A (en) * | 2022-12-05 | 2023-01-17 | 大方智造(天津)科技有限公司 | Round hole feature recognition processing method based on 3D model |
| US11644813B2 (en) | 2017-10-30 | 2023-05-09 | Fanuc Corporation | Numerical controller, CNC machine tool, numerical control method, and computer-readable information storage medium |
| WO2023151523A1 (en) * | 2022-02-09 | 2023-08-17 | 深圳市爱云信息科技有限公司 | Deep learning programming method and system based on digital twin daas platform |
| CN116974239A (en) * | 2023-09-22 | 2023-10-31 | 深圳市艾姆克斯科技有限公司 | Processing track control method and system based on cnc engraving and milling machine |
| CN117930787A (en) * | 2024-03-21 | 2024-04-26 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
| CN118151616A (en) * | 2024-05-09 | 2024-06-07 | 南京航空航天大学 | Technological parameter self-adaptive adjusting system for superplastic forming diffusion connection machining process |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008059518A (en) * | 2006-09-04 | 2008-03-13 | Mitsubishi Heavy Ind Ltd | Post-processor development support system and cam system using the same |
| CN101464676A (en) * | 2009-01-08 | 2009-06-24 | 上海交通大学 | System and method for part process feature detection in three-coordinate numerical control machining |
| WO2010116265A1 (en) * | 2009-04-07 | 2010-10-14 | Politecnico Di Milano | Method for automatically partitioning a part program into fundamental operations |
| CN102411333A (en) * | 2011-11-18 | 2012-04-11 | 上海交通大学 | Fast numerical control machining process system for complex parts of airplane |
| CN102799144A (en) * | 2012-08-21 | 2012-11-28 | 南京航空航天大学 | Numerical control processing program transplanting method based on characteristics |
-
2013
- 2013-03-27 CN CN201310103134.1A patent/CN103235556B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008059518A (en) * | 2006-09-04 | 2008-03-13 | Mitsubishi Heavy Ind Ltd | Post-processor development support system and cam system using the same |
| CN101464676A (en) * | 2009-01-08 | 2009-06-24 | 上海交通大学 | System and method for part process feature detection in three-coordinate numerical control machining |
| WO2010116265A1 (en) * | 2009-04-07 | 2010-10-14 | Politecnico Di Milano | Method for automatically partitioning a part program into fundamental operations |
| CN102411333A (en) * | 2011-11-18 | 2012-04-11 | 上海交通大学 | Fast numerical control machining process system for complex parts of airplane |
| CN102799144A (en) * | 2012-08-21 | 2012-11-28 | 南京航空航天大学 | Numerical control processing program transplanting method based on characteristics |
Cited By (88)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103439917A (en) * | 2013-09-02 | 2013-12-11 | 南京航空航天大学 | Cutting force prediction method based on features |
| CN103699055A (en) * | 2013-12-24 | 2014-04-02 | 沈阳飞机工业（集团）有限公司 | Intelligent numerical control machining programming system and intelligent numerical control machining programming method for aircraft structural parts |
| CN103699055B (en) * | 2013-12-24 | 2016-08-17 | 沈阳飞机工业（集团）有限公司 | Aircraft structure intelligent numerical control machining prgraming system and method |
| CN103926879B (en) * | 2014-05-06 | 2016-04-06 | 南京航空航天大学 | Aero-engine casing characteristic recognition method |
| CN103926879A (en) * | 2014-05-06 | 2014-07-16 | 南京航空航天大学 | Aviation engine crankcase feature recognition method |
| CN104331020B (en) * | 2014-11-12 | 2017-02-15 | 南京航空航天大学 | Feature-based skin mirror-image milling technology decision method |
| CN104360634A (en) * | 2014-11-12 | 2015-02-18 | 南京航空航天大学 | Skin mirror image milling numerical control program fast generating method based on features |
| CN104331020A (en) * | 2014-11-12 | 2015-02-04 | 南京航空航天大学 | Feature-based skin mirror-image milling technology decision method |
| CN104360634B (en) * | 2014-11-12 | 2017-02-15 | 南京航空航天大学 | Skin mirror image milling numerical control program fast generating method based on features |
| CN104570948A (en) * | 2015-01-16 | 2015-04-29 | 南京航空航天大学 | Hole feature machining method for spaceflight sheet part |
| CN104570948B (en) * | 2015-01-16 | 2017-07-11 | 南京航空航天大学 | Space flight plate parts hole characteristic processing method |
| CN108027604A (en) * | 2015-09-29 | 2018-05-11 | 西门子公司 | For optimally controlling the method and control device of lathe |
| CN105242639A (en) * | 2015-11-03 | 2016-01-13 | 南京航空航天大学 | Numerical control machining feature customizing method |
| CN105549537A (en) * | 2015-12-23 | 2016-05-04 | 上海电机学院 | Assembly parameterization and automation numerical control processing method based on material object scanning |
| CN106938404B (en) * | 2016-01-05 | 2019-03-22 | 财团法人工业技术研究院 | Dynamic workpiece plane incline measuring method |
| CN106938404A (en) * | 2016-01-05 | 2017-07-11 | 财团法人工业技术研究院 | Dynamic workpiece planar tilt method for measurement |
| CN105759724A (en) * | 2016-03-31 | 2016-07-13 | 孙丽君 | Intelligent programming method |
| CN107272594A (en) * | 2016-04-04 | 2017-10-20 | 菲尔斯系统有限责任公司 | Plan method and its program and system of execution that the computer system of technique is performed |
| CN106200558A (en) * | 2016-08-26 | 2016-12-07 | 大连大学 | Nc program based on model generates method |
| CN106200558B (en) * | 2016-08-26 | 2018-10-02 | 大连大学 | Nc program generation method based on model |
| TWI600988B (en) * | 2016-11-15 | 2017-10-01 | Machining expert system for CNC machine tool and controller processing parameters generation method | |
| CN110100213A (en) * | 2016-12-26 | 2019-08-06 | 三菱电机株式会社 | Manufacturing procedure generating apparatus, manufacturing procedure generation method and program |
| CN110100213B (en) * | 2016-12-26 | 2022-06-24 | 三菱电机株式会社 | Machining process generation device and machining process generation method |
| CN106514129A (en) * | 2017-01-03 | 2017-03-22 | 南京航空航天大学 | Numerical control programming nonuniform margin configuration method based on machining characteristic intermediate state rigidity |
| CN106815438B (en) * | 2017-01-17 | 2020-08-04 | 成都飞机工业（集团）有限责任公司 | Method for extracting main influence factors of numerical control milling efficiency of aircraft structural part |
| CN106815438A (en) * | 2017-01-17 | 2017-06-09 | 成都飞机工业（集团）有限责任公司 | A kind of Flight Structures NC Machining milling efficiency main affecting factors extracting method |
| CN107247445B (en) * | 2017-06-29 | 2019-04-12 | 南京航空航天大学 | Consider the knife rail mapping method of approximate error |
| CN107247445A (en) * | 2017-06-29 | 2017-10-13 | 南京航空航天大学 | Consider the knife rail mapping method of approximate error |
| CN107584334A (en) * | 2017-08-25 | 2018-01-16 | 南京航空航天大学 | A kind of complex structural member numerical control machining cutter status real time monitor method based on deep learning |
| CN107562015A (en) * | 2017-08-29 | 2018-01-09 | 沈阳航空航天大学 | A kind of process geometrical model construction method based on NC Machining Program |
| CN107562015B (en) * | 2017-08-29 | 2020-03-24 | 沈阳航空航天大学 | Process geometric model construction method based on numerical control machining programming |
| CN107728580A (en) * | 2017-10-18 | 2018-02-23 | 南京航空航天大学 | A kind of geological information technique information monitoring information correlating method towards adaptive machining |
| CN107728580B (en) * | 2017-10-18 | 2019-10-22 | 南京航空航天大学 | A kind of geological information-technique information-monitoring information correlating method towards adaptive machining |
| CN109725600B (en) * | 2017-10-30 | 2021-04-13 | 发那科株式会社 | Post-processor device, machining program generating method, CNC machining system, and computer-readable information recording medium |
| US11644813B2 (en) | 2017-10-30 | 2023-05-09 | Fanuc Corporation | Numerical controller, CNC machine tool, numerical control method, and computer-readable information storage medium |
| CN109725600A (en) * | 2017-10-30 | 2019-05-07 | 发那科株式会社 | Preprocessor device, processing program generation method, CNC system of processing and computer-readable information recording medium |
| CN108228996A (en) * | 2017-12-28 | 2018-06-29 | 重庆平伟汽车科技股份有限公司 | A kind of system and method for the device to hole automated programming based on hole characteristic |
| CN108268012A (en) * | 2018-02-01 | 2018-07-10 | 南京航空航天大学 | A kind of machine tool system remodeling method of Parametric drive production |
| CN108268012B (en) * | 2018-02-01 | 2021-01-01 | 南京航空航天大学 | Machine tool system transformation method for parametric drive production |
| CN108520139B (en) * | 2018-03-30 | 2021-06-15 | 北京理工大学 | Construction method of multi-dimensional tool design knowledge component |
| CN108520139A (en) * | 2018-03-30 | 2018-09-11 | 北京理工大学 | A kind of various dimensions Fixture Design knowledge component construction method |
| CN108985617A (en) * | 2018-07-11 | 2018-12-11 | 广东人励智能工程有限公司 | A kind of product manufacturing process dispatching method and system based on intelligence manufacture |
| CN108985617B (en) * | 2018-07-11 | 2021-07-13 | 广东人励智能工程有限公司 | Product production flow scheduling method and system based on intelligent manufacturing |
| CN109375583A (en) * | 2018-11-16 | 2019-02-22 | 中国航发西安动力控制科技有限公司 | For the numerical control program preparation method of hole in piece part system manufacturing feature |
| CN111353255A (en) * | 2018-12-05 | 2020-06-30 | 财团法人工业技术研究院 | Automatic generation system of processing parameters |
| CN109773589A (en) * | 2018-12-29 | 2019-05-21 | 深度创新科技（深圳）有限公司 | Method and device, the equipment of on-line measurement and processing guiding are carried out to workpiece surface |
| CN109773589B (en) * | 2018-12-29 | 2021-09-28 | 深度创新科技（深圳）有限公司 | Method, device and equipment for online measurement and machining guidance of workpiece surface |
| CN113811904A (en) * | 2019-05-08 | 2021-12-17 | 西门子股份公司 | Generating assembly plans and three-dimensional assembly models |
| CN110096033A (en) * | 2019-05-09 | 2019-08-06 | 江苏南高智能装备创新中心有限公司 | A method of check and correction numerical control program precision |
| CN110458606A (en) * | 2019-07-19 | 2019-11-15 | 西北工业大学 | Nonstandard cutter price expectation method based on deep learning |
| EP3859466A1 (en) * | 2020-01-30 | 2021-08-04 | DENTSPLY SIRONA Inc. | Dental machining system for predicting the machining time for manufacturing a dental restoration/appliance |
| WO2021152125A1 (en) * | 2020-01-30 | 2021-08-05 | Dentsply Sirona Inc. | Dental machining system for predicting the machining time for manufacturing a dental restoration/appliance |
| CN115243640A (en) * | 2020-03-20 | 2022-10-25 | 登士柏西诺德公司 | Dental machining system for generating process parameters for machining |
| WO2021185661A1 (en) * | 2020-03-20 | 2021-09-23 | Dentsply Sirona Inc. | Dental machining system for generating process parameters of the machining |
| CN111861147A (en) * | 2020-06-29 | 2020-10-30 | 北京航空航天大学 | Digital detection method and system for whole process of manufacturing complex parts |
| CN111768099A (en) * | 2020-06-29 | 2020-10-13 | 山东浪潮通软信息科技有限公司 | Model part-based task decomposition method in scientific research and production mode |
| CN111768099B (en) * | 2020-06-29 | 2024-05-14 | 浪潮通用软件有限公司 | Task decomposition method based on model part in scientific research production mode |
| CN111813048A (en) * | 2020-06-30 | 2020-10-23 | 中国航发动力股份有限公司 | Function integration numerical control program generation method, system, device and readable storage medium |
| CN111679630A (en) * | 2020-06-30 | 2020-09-18 | 中国航发动力股份有限公司 | Quick programming method for numerical control machining of engine case |
| CN111813048B (en) * | 2020-06-30 | 2022-04-26 | 中国航发动力股份有限公司 | Function integration numerical control program generation method, system, device and readable storage medium |
| CN112355713A (en) * | 2020-09-24 | 2021-02-12 | 北京航空航天大学 | Method, device and equipment for predicting cutting force based on image |
| CN112364447A (en) * | 2020-09-30 | 2021-02-12 | 深圳市为汉科技有限公司 | Method and related device for generating part model |
| CN112364447B (en) * | 2020-09-30 | 2023-10-27 | 深圳市为汉科技有限公司 | Part model generation method and related device |
| CN112327755B (en) * | 2020-11-16 | 2021-09-14 | 广州傲创智能科技有限公司 | Method for automatically identifying frame of die carrier |
| CN112327755A (en) * | 2020-11-16 | 2021-02-05 | 广州傲创智能科技有限公司 | Automatic frame identification method for die carrier |
| CN112884246A (en) * | 2021-03-17 | 2021-06-01 | 成都永峰科技有限公司 | Working hour prediction method for airplane structural member machining process |
| CN112884246B (en) * | 2021-03-17 | 2024-03-22 | 成都永峰科技有限公司 | Working hour prediction method for aircraft structural member machining procedure |
| CN113190888A (en) * | 2021-04-20 | 2021-07-30 | 东风柳州汽车有限公司 | Process documentation method, device, equipment and storage medium |
| CN113190888B (en) * | 2021-04-20 | 2022-10-28 | 东风柳州汽车有限公司 | Process documentation method, device, equipment and storage medium |
| CN113485249A (en) * | 2021-06-22 | 2021-10-08 | 哈尔滨东安汽车动力股份有限公司 | Rapid profiling operation method for new sample of automobile engine part |
| CN113341882B (en) * | 2021-06-28 | 2022-06-14 | 成都飞机工业（集团）有限责任公司 | Numerical control process design and optimization method based on processing knowledge |
| CN113341882A (en) * | 2021-06-28 | 2021-09-03 | 成都飞机工业（集团）有限责任公司 | Numerical control process design and optimization method based on processing knowledge |
| WO2023272836A1 (en) * | 2021-06-28 | 2023-01-05 | 成都飞机工业（集团）有限责任公司 | Numerical control process design and optimization method based on machining knowledge |
| CN113850289A (en) * | 2021-08-17 | 2021-12-28 | 中国电子科技集团公司第二十九研究所 | Method and system for measuring and calculating working procedure working hour quota of radio frequency microwave part product |
| CN113850289B (en) * | 2021-08-17 | 2023-05-05 | 中国电子科技集团公司第二十九研究所 | Working procedure working hour quota measuring and calculating method and system for radio frequency microwave product |
| WO2023151523A1 (en) * | 2022-02-09 | 2023-08-17 | 深圳市爱云信息科技有限公司 | Deep learning programming method and system based on digital twin daas platform |
| CN114660994B (en) * | 2022-05-25 | 2022-08-23 | 中科航迈数控软件(深圳)有限公司 | Numerical control machine tool machining process decision optimization method, system and related equipment |
| CN114660994A (en) * | 2022-05-25 | 2022-06-24 | 中科航迈数控软件(深圳)有限公司 | Decision optimization method, system and related equipment for machining process of numerical control machine tool |
| CN114675599B (en) * | 2022-05-27 | 2022-08-26 | 中科航迈数控软件(深圳)有限公司 | Machining feature sorting method and system of numerical control machine tool |
| CN114675599A (en) * | 2022-05-27 | 2022-06-28 | 中科航迈数控软件(深圳)有限公司 | Machining feature sorting method and system of numerical control machine tool |
| CN114714151A (en) * | 2022-06-09 | 2022-07-08 | 成都飞机工业（集团）有限责任公司 | Method, device and equipment for planning measurement point positions of web and storage medium |
| CN115618530B (en) * | 2022-12-05 | 2023-03-10 | 大方智造(天津)科技有限公司 | Round hole feature recognition processing method based on 3D model |
| CN115618530A (en) * | 2022-12-05 | 2023-01-17 | 大方智造(天津)科技有限公司 | Round hole feature recognition processing method based on 3D model |
| CN116974239A (en) * | 2023-09-22 | 2023-10-31 | 深圳市艾姆克斯科技有限公司 | Processing track control method and system based on cnc engraving and milling machine |
| CN116974239B (en) * | 2023-09-22 | 2023-12-01 | 深圳市艾姆克斯科技有限公司 | Processing track control method and system based on cnc engraving and milling machine |
| CN117930787A (en) * | 2024-03-21 | 2024-04-26 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
| CN117930787B (en) * | 2024-03-21 | 2024-06-11 | 南京航空航天大学 | Technological parameter optimization method for numerical control machine tool machining |
| CN118151616A (en) * | 2024-05-09 | 2024-06-07 | 南京航空航天大学 | Technological parameter self-adaptive adjusting system for superplastic forming diffusion connection machining process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103235556B (en) | 2015-08-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103235556B (en) | The complex parts digital control processing manufacture method of feature based | |
| Mali et al. | A comprehensive review of free-form surface milling–Advances over a decade | |
| Lee et al. | Cut distribution and cutter selection for sculptured surface cavity machining | |
| CN104759942B (en) | A kind of milling deformation on-line measurement of thin-walled parts and compensation processing method | |
| Spence et al. | A solid modeller based milling process simulation and planning system | |
| CN102866671B (en) | Large-arc ruled surface numerical control machining cutter path planning method | |
| CN100435055C (en) | Method for planning smooth and non-interference tool route of 5-axis numerical control machining | |
| Young et al. | A five-axis rough machining approach for a centrifugal impeller | |
| CN102629289B (en) | Automatic generation method of plunge milling toolpath for corner features | |
| CN103365243B (en) | Method for rapidly generating corner side milling process path | |
| US9164503B2 (en) | Method of optimizing toolpaths using medial axis transformation | |
| WO2014045383A1 (en) | Machining-step determining method and machining-step design device | |
| Waiyagan et al. | Intelligent feature based process planning for five-axis mill-turn parts | |
| CN104181865B (en) | Annular feed path planning method of integral impeller rough machining | |
| Lasemi et al. | A freeform surface manufacturing approach by integration of inspection and tool path generation | |
| CN105880953A (en) | Method for processing aviation blade | |
| CN112558550B (en) | Method for machining special-shaped threads by CAM software | |
| CN108594764A (en) | A kind of constant scallop-height cutter-contact point trace generation method of triangle grid model | |
| Chuang et al. | Integrated rough machining methodology for centrifugal impeller manufacturing | |
| Prat et al. | Modeling and analysis of five-axis milling configurations and titanium alloy surface topography | |
| Lim et al. | Integrated planning for precision machining of complex surfaces—III. Compensation of dimensionai errors | |
| Lim et al. | Integrated planning for precision machining of complex surfaces. Part 2: Application to the machining of a turbine blade die | |
| Joo et al. | Efficient feature-based process planning for sculptured pocket machining | |
| El-Midany et al. | Optimal CNC plunger selection and toolpoint generation for roughing sculptured surfaces cavity | |
| Zarkti et al. | Towards an Automatic-optimized tool selection for milling process, based on data from Sandvik Coromant |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200331 Address after: 215104 No. 7, North Guandu Road, Yuexi Street, Wuzhong River, Suzhou City, Jiangsu Province Patentee after: JIANGSU MAIXINLIN AVIATION TECHNOLOGY Co.,Ltd. Address before: Yudaojie Baixia District of Nanjing City, Jiangsu Province, No. 29 210016 Patentee before: Nanjing University of Aeronautics and Astronautics |
|
| TR01 | Transfer of patent right |