CN107038269B - Numerical control machining machine tool optimization method based on X-shaped aviation thin-wall standard test piece - Google Patents

Numerical control machining machine tool optimization method based on X-shaped aviation thin-wall standard test piece Download PDF

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CN107038269B
CN107038269B CN201610928548.1A CN201610928548A CN107038269B CN 107038269 B CN107038269 B CN 107038269B CN 201610928548 A CN201610928548 A CN 201610928548A CN 107038269 B CN107038269 B CN 107038269B
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江小辉
张振亚
李郝林
刘晓
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University of Shanghai for Science and Technology
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Abstract

The invention relates to a numerical control machine tool optimization method based on an X-shaped aviation thin-wall standard part, which comprises the following specific steps: (1) carrying out finite element simulation cutting analysis on the part to be processed, and calculating the processing difficulty coefficient of the part to be processed; (2) dividing the processing difficulty of the part through a mapping relation table of the processing difficulty coefficient of the machine tool and the complexity grade difference of the workpiece; (3) the corresponding machine tool to be processed is selected by grading the processing difficulty coefficients of the parts to be processed, so that the machine tool with insufficient processing precision can be prevented from being used for processing, and the machine tool with too high precision can be prevented from being used. The method can meet the requirement of the machining precision of parts, and can greatly save cost and resources.

Description

Numerical control machining machine tool optimization method based on X-shaped aviation thin-wall standard test piece
Technical Field
The invention relates to a method for processing key parts of aerospace, automobile, ship, nuclear power and the like in multiple varieties and small batches, in particular to a method for comprehensively detecting the processing precision of a numerical control machine tool.
Background
The five-axis linkage numerical control machine tool plays a great role in the status and the whole technical level process of advanced manufacturing industries such as aerospace, automobile manufacturing and the like. Therefore, high requirements are put forward on the machining precision of the five-axis numerical control machine tool. The method for detecting the machining precision of the numerical control machine tool mainly comprises a direct measurement method and an indirect measurement method, wherein the direct measurement method is to directly measure the machining precision of the machine tool by using tools with various measurement precisions; the indirect measurement method is to process a standard mechanical test piece by a machine tool, to judge whether the machine tool to be processed meets the performance standard requirement by analyzing whether the processing precision of the standard test piece meets the precision standard of the standard test piece, and to compare the corresponding test standard value according to the specific processing precision to judge whether the comprehensive precision of the machine tool meets the performance standard requirement.
At present, the idea of error identification of a five-axis linkage numerical control machine tool is to evaluate the machining performance of the machine tool through detection and evaluation of machining errors of the machine tool. The error representation methods of five-axis numerical control machines which are commonly used at present mainly comprise the following methods:
method 1. error identification method based on cue instrument: the ball arm instrument consists of high-precision steel balls at two ends, one end of the ball arm instrument is fixed, and the other end of the ball arm instrument is connected with a high-precision displacement sensor. Two ends of the two steel balls are positioned through a magnetic suction seat with three-point positioning, one end of each steel ball is adsorbed on the main shaft, and the other end of each steel ball is adsorbed on the workbench, as shown in figure 3. The method is mainly used for measuring the two-cycle linkage precision of the numerical control machine tool, and can measure the two-axis linkage precision of X-Y, X-Z and Y-Z planes respectively. When the workbench does circular interpolation motion relative to the main shaft, a circular arc track simulating cutting is formed, the sensor collects the change of the distance between two steel balls and transmits the change to the computer, and through software diagnosis and analysis, the roundness precision of the two-shaft linkage is obtained, and various single errors such as reverse clearance, reverse jump, servo mismatching, proportion mismatching, straightness, verticality, periodic error, transverse clearance and the like are separated.
The method 2 comprises the following steps: an error method for detecting a test piece based on NAS979 (American national space navigation standard) comprises the following steps: the American NAS979 sets a cutting test standard of a round-diamond-square-shaped test piece, and the straightness of a machine tool along an X coordinate, the verticality among X, Y, Z coordinates, the straightness and the roundness of numerical control interpolation, the position precision of a hole on an X-Y plane and the like are respectively detected by trial cutting of the test piece. Because a linear relation exists between the error of each coordinate axis of the machine tool and the stroke, the standard can determine the corresponding size of the cutting test piece according to the stroke range of each coordinate of the machine tool. Therefore, the working precision of detecting the large stroke by using the small-size test piece is more reasonable
The method 3 comprises the following steps: the error identification method of the five-axis numerical control machine tool based on the S-shaped test piece comprises the following steps: machine tool factors which mainly affect machine tool machining are reversely traced based on normal errors of the S-shaped test piece obtained by measurement of the three-coordinate measuring machine, and the influence level of the main factors on the machine tool precision is further determined through a neural network. Therefore, the method can judge the precision of the machine tool, and can provide an optimization scheme of the precision of the machine tool when the precision of the machine tool does not meet the requirement, so that relevant factors influencing the precision of the machine tool can be adjusted in terms of magnitude, and the high-precision requirement of each part of the machine tool can be met.
The method 4 comprises the following steps: nine-line identification method based on laser interferometer: the essence of the nine-line identification method is that only the position errors and the straightness errors on nine straight lines in the coordinate system of the machine tool worktable are checked, and twenty-one basic geometric errors of the machine tool can be identified. Firstly, one of the three translational shafts is selected to move, the other two translational shafts are kept static, three straight lines are selected in a coordinate system of a workbench, displacement errors of points on the three straight lines are measured, a straightness error in one direction is measured while the other straight line error is measured, six linear equations are established based on the measured straightness errors and the displacement errors, six basic errors of the moving shafts can be obtained by solving an equation set, and twelve basic errors of the other two translational shafts can be solved by the same principle. And finally, 3 vertical errors can be obtained by reading the correction angle of the straight line deviating from the reference when the laser interferometer measures the straightness error.
In summary, the main disadvantages of the conventional identification method are: the current error identification method of the numerical control machine tool cannot fully reflect the cutting processing performance of the machine tool, and mainly identifies the static factors of the machine tool under the condition of no load and low speed. No matter which of the above identification methods is to process the part by the machine tool to be processed, the precision measurement is carried out on the part after the processing is finished, or the precision detection is carried out on the part in the processing process, so that the requirement of modern processing can not be met far.
Disclosure of Invention
Aiming at the defects, the invention provides a numerical control machine tool optimization method based on an X-shaped aviation thin-wall standard test piece.
The technical scheme of the invention is as follows: a numerical control machine tool optimization method based on an X-shaped aviation thin-wall standard part comprises the following specific steps:
(1) carrying out finite element simulation cutting analysis on the part to be processed, and calculating the processing difficulty coefficient of the part to be processed;
(2) and then, mapping a relation table of the machine tool machining difficulty coefficient and the workpiece complexity grade difference:
ΔM ΔC
class A ΔM≤1 ΔC≤0.5
Class B 1＜ΔM＜2 0.5＜ΔC＜0.8
Class C 2≤ΔM 0.8≤ΔC
Dividing the processing difficulty of the parts;
(3) the corresponding machine tool to be processed is selected by grading the processing difficulty coefficients of the parts to be processed, so that the machine tool with insufficient processing precision can be prevented from being used for processing, and the machine tool with too high precision can be prevented from being used.
The method for establishing the level difference mapping relation table of the machine tool machining difficulty coefficient and the workpiece complexity comprises the following steps:
(1) obtaining theoretical processing complexity coefficient of X-shaped aviation thin-wall standard part
The theoretical machining complexity coefficient consists of characteristic machining rigidity, an average value delta K, dynamic rigidity and an average value delta C,
wherein: characteristic work stiffness and mean value
Figure BDA0001137500750000031
Wherein ξ is a workpiece characteristic correlation coefficient, i and n are deformation nodes of a machined workpiece, j is X, Y, Z three directions, F is cutting force, and delta is deformation;
dynamic stiffness and mean value
Figure BDA0001137500750000041
Similarly, the dynamic stiffness and the average value of different working procedures in the processing process are calculated by using the method to obtain delta KAVeThe smaller the value is, the weaker the change of the whole dynamic stiffness is in processing, and the higher the processing complexity is;
in the formula: Δ KFFor the average stiffness, Δ K, of the features of the workpiece after machiningAVeThe average value of the sum of the feature structure rigidity of the processed workpiece in each procedure is obtained;
(2) establishing unique mapping relation between machine tool machining difficulty coefficient and workpiece complexity of each enterprise
Comparing the experimental result delta C' of the X-shaped aviation thin-wall standard part processed on all available machine tools of the enterprise, and comparing the theoretical delta C to obtain the machine tool processing difficulty coefficient
Figure BDA0001137500750000042
And finally constructing a unique mapping relation between the machine tool machining difficulty coefficient and the workpiece complexity of each enterprise.
The invention has the beneficial effects that: the comprehensive detection numerical control machine tool is subjected to deeper scientific research according to the characteristics of the X-shaped novel aviation thin-wall standard test piece, a new mathematical model is reestablished, finite element simulation cutting machining and analysis are firstly carried out on the X-shaped novel aviation thin-wall standard test piece, the machining complex coefficient of the X-shaped novel aviation thin-wall standard test piece is calculated, and the X-shaped novel aviation thin-wall standard test piece is machined by the corresponding numerical control machine tool according to the coefficient grading of the table 1. Correspondingly, when other thin-wall parts need to be machined in production, the parts can be subjected to simulated cutting machining and analysis firstly, the machining complex coefficient of the parts is calculated, the parts are graded according to the coefficient of the table 1, and the corresponding numerical control machine tool is adopted for machining.
Drawings
FIG. 1 is a flow chart of the machine tool difficulty coefficient and workpiece complexity determination of the present invention;
FIG. 2 is a schematic view of a surface a of a novel aviation thin-wall standard series test piece A based on an X shape;
FIG. 3 is a schematic b-surface view of a novel aviation thin-wall standard series test piece A based on an X shape;
FIG. 4 is a schematic diagram of a novel aviation thin-wall standard series test piece B based on an X shape.
Detailed Description
The design principles of the present invention are described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in figure 1, a numerical control machine tool optimization method based on an X-shaped aviation thin-wall standard part comprises the following specific steps:
(1) carrying out finite element simulation cutting analysis on the part to be processed, and calculating the processing difficulty coefficient of the part to be processed;
(2) and then, the level difference mapping relation table 1 of the machine tool processing difficulty coefficient and the workpiece complexity is used for:
TABLE 1
ΔM ΔC
Class A ΔM≤1 ΔC≤0.5
Class B 1＜ΔM＜2 0.5＜ΔC＜0.8
Class C 2≤ΔM 0.8≤ΔC
Dividing the processing difficulty of the parts;
(3) the corresponding machine tool to be processed is selected by grading the processing difficulty coefficients of the parts to be processed, so that the machine tool with insufficient processing precision can be prevented from being used for processing, and the machine tool with too high precision can be prevented from being used.
The method for establishing the level difference mapping relation table of the machine tool machining difficulty coefficient and the workpiece complexity comprises the following steps:
(1) obtaining theoretical processing complexity coefficient of X-shaped aviation thin-wall standard part
The theoretical machining complexity coefficient consists of characteristic machining rigidity, an average value delta K, dynamic rigidity and an average value delta C,
wherein: characteristic work stiffness and mean value
Figure BDA0001137500750000051
Wherein ξ is a workpiece characteristic correlation coefficient, i and n are deformation nodes of a machined workpiece, j is X, Y, Z three directions, F is cutting force, and delta is deformation;
dynamic stiffness and mean value
Figure BDA0001137500750000052
Similarly, the dynamic stiffness and the average value of different working procedures in the processing process are calculated by using the method to obtain delta KAVeThe smaller the value is, the weaker the change of the whole dynamic stiffness is in processing, and the higher the processing complexity is;
in the formula: Δ KFFor the average stiffness, Δ K, of the features of the workpiece after machiningAVeThe average value of the sum of the feature structural rigidity of the processed workpiece in each process is obtained.
(2) Establishing unique mapping relation between machine tool machining difficulty coefficient and workpiece complexity of each enterprise
Comparing the experimental result delta C' of the X-shaped aviation thin-wall standard part processed on all available machine tools of the enterprise, and comparing the theoretical delta C to obtain the machine tool processing difficulty coefficient
Figure BDA0001137500750000061
And finally constructing a unique mapping relation between the machine tool machining difficulty coefficient and the workpiece complexity of each enterprise.
Example (b):
as shown in figures 2 and 3, the cutting machining process of the X-shaped aviation thin-wall standard series test piece A based on the invention comprises the steps of firstly machining the surface B, turning 180 degrees after the surface B is machined, placing an X-shaped novel aviation thin-wall standard series support piece B in a cavity of the test piece A, and machining the surface a after the positioning is finished as shown in figure 4.
The machining complexity coefficient is composed of characteristic machining rigidity, an average value delta K, dynamic rigidity and an average value delta C, the characteristic machining rigidity and the average value are mainly used for evaluating the comprehensive deformation resistance of the part to be analyzed, and the smaller the coefficient is, the weaker the capacity is, and the higher the machining complexity is.
Characteristic processing stiffness and mean value: the machining simulation result of the X-shaped novel aviation thin-wall standard series test piece A is analyzed to obtain the rigidity sum of the deformation nodes of the thin-wall test piece, and the average value of the rigidity sum is calculated to evaluate the comprehensive deformation resistance of the part, wherein the smaller the coefficient is, the weaker the resistance is, and the higher the machining complexity is.
Characteristic processing stiffness and mean value:
Figure BDA0001137500750000062
wherein ξ is the correlation coefficient of workpiece characteristics (related to workpiece characteristics, etc.), i and n are the deformation nodes of the machined workpiece, j is X, Y, Z three directions, F is the cutting force, and delta is the deformation.
Dynamic stiffness and mean value:
similarly, the dynamic stiffness and the average value of different procedures (assuming n procedures) in the processing process are calculated by using the method to obtain the delta KAveAnd defining the workpiece processing complexity index as:
Figure BDA0001137500750000071
the theoretical machining complexity value of the standard part can be obtained by combining the mathematical model with a finite element method, the standard part is machined on all available machine tools of an enterprise at the moment, delta C' is tested in an experiment, and then the theoretical delta C is compared to obtain
Figure BDA0001137500750000072
The mapping relationship between the machine tool difficulty coefficient and the workpiece complexity level shown in table 1 can be obtained, and it should be noted that the specific value in table 1 is a reference value in a special case, for example, the case of a specific enterprise needs to be recalculated according to the method of the present invention. By establishing the mapping relation between the workpiece complex coefficient and the machine tool machining difficulty coefficient in the table 1, the invention can judge the workpiece machining capacity of the machine tool, thereby providing a selection basis for selecting which type and which machine tool are selected for the workpiece. The establishment of the mapping relation can determine the processing difficulty grades of all machine tools of an enterprise by the complexity calculation method based on the X-shaped aviation thin-wall part standard partThe specific method comprises the following steps: firstly, a finite element method is used, the theoretical machining complexity value of the standard part can be obtained, the experimental result delta C' of the standard part machined on all available machine tools of an enterprise is compared, and the theoretical delta C is compared, so that the theoretical machining complexity value of the standard part can be obtained
Figure BDA0001137500750000073
And finally constructing a unique mapping relation between the machine tool machining difficulty coefficient and the workpiece complexity of each enterprise.

Claims (1)

1. A numerical control machine tool selection method based on an X-shaped aviation thin-wall standard part is characterized by comprising the following specific steps:
1) carrying out finite element simulation cutting analysis on the part to be processed, and calculating the processing difficulty coefficient of the part to be processed;
2) establishing a mapping relation table of the machine tool machining difficulty coefficient and the workpiece complexity grade difference:
(1) obtaining theoretical processing complexity coefficient based on X-shaped aviation thin-wall standard part
The theoretical machining complexity coefficient consists of an average value delta K of the characteristic machining rigidity and an average value delta C of the dynamic rigidity,
wherein: average value of characteristic working stiffness
Figure FDA0002242991000000011
Wherein ξ is a workpiece characteristic correlation coefficient, i and n are deformation nodes of a machined workpiece, j is X, Y, Z three directions, F is cutting force, and delta is deformation;
average value of dynamic stiffness
Figure FDA0002242991000000012
Similarly, the method is used for calculating the average value of the dynamic stiffness of different working procedures in the machining process to obtain the delta KAVeThe smaller the value is, the weaker the change of the whole dynamic stiffness is in processing, and the higher the processing complexity is;
in the formula: Δ KFFor average stiffness of the machined workpiece features after machining，ΔKAVeThe average value of the characteristic structure rigidity of the processed workpiece in each procedure is obtained;
(2) establishing unique mapping relation between machine tool machining difficulty coefficient and workpiece complexity of each enterprise
Comparing the experimental result delta C' of the X-shaped aviation thin-wall standard part processed on all available machine tools of the enterprise, and comparing the theoretical delta C to obtain the machine tool processing difficulty coefficient
Figure FDA0002242991000000013
Finally, establishing a unique mapping relation between the machine tool machining difficulty coefficient and the workpiece complexity of each enterprise;
3) and then, mapping a relation table of the machine tool machining difficulty coefficient and the workpiece complexity grade difference:
ΔM ΔC class A ΔM≤1 ΔC≤0.5 Class B 1＜ΔM＜2 0.5＜ΔC＜0.8 Class C 2≤ΔM 0.8≤ΔC
Dividing the processing difficulty of the parts;
4) the corresponding machine tool to be processed is selected by grading the processing difficulty coefficients of the parts to be processed, so that the machine tool with insufficient processing precision can be prevented from being used for processing, and the machine tool with too high precision can be prevented from being used.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102789200A (en) * 2012-08-21 2012-11-21 南京航空航天大学 Integrated airplane structural part numerical-control machining capacity test model and application thereof
CN102819240A (en) * 2012-08-24 2012-12-12 电子科技大学 Processing quality defect area calculation method of five-axis linkage numerical control machine tool
CN104021242A (en) * 2014-05-15 2014-09-03 南京航空航天大学 Numerically-controlled machine tool machining capacity evaluation method based on part characteristics
CN105446264A (en) * 2015-12-24 2016-03-30 南京航空航天大学 Feature-based machine tool accuracy optimization design method
CN105488282A (en) * 2015-11-30 2016-04-13 南京航空航天大学 Cutting parameter segmentation and variable cutting depth optimizing method based on dynamic machining feature

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7209799B2 (en) * 2004-04-14 2007-04-24 Gm Global Technology Operations, Inc. Predictive modeling of machining line variation

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102789200A (en) * 2012-08-21 2012-11-21 南京航空航天大学 Integrated airplane structural part numerical-control machining capacity test model and application thereof
CN102819240A (en) * 2012-08-24 2012-12-12 电子科技大学 Processing quality defect area calculation method of five-axis linkage numerical control machine tool
CN104021242A (en) * 2014-05-15 2014-09-03 南京航空航天大学 Numerically-controlled machine tool machining capacity evaluation method based on part characteristics
CN105488282A (en) * 2015-11-30 2016-04-13 南京航空航天大学 Cutting parameter segmentation and variable cutting depth optimizing method based on dynamic machining feature
CN105446264A (en) * 2015-12-24 2016-03-30 南京航空航天大学 Feature-based machine tool accuracy optimization design method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"数控机床切削力误差建模与实时补偿研究";陈志俊;《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》;20080615(第06期);第B022-145页 *
"机床精度检测航空薄壁标准件的设计方法研究";江小辉等;《机械强度》;20170615;第621-628页 *

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