CN116984924A - Intelligent machining unit cutter requirement optimization method - Google Patents
Intelligent machining unit cutter requirement optimization method Download PDFInfo
- Publication number
- CN116984924A CN116984924A CN202311254915.0A CN202311254915A CN116984924A CN 116984924 A CN116984924 A CN 116984924A CN 202311254915 A CN202311254915 A CN 202311254915A CN 116984924 A CN116984924 A CN 116984924A
- Authority
- CN
- China
- Prior art keywords
- tool
- cutter
- tools
- time
- cutters
- 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 70
- 238000005457 optimization Methods 0.000 title claims abstract description 26
- 238000003754 machining Methods 0.000 title claims description 59
- 238000004519 manufacturing process Methods 0.000 claims abstract description 43
- 230000008569 process Effects 0.000 claims description 31
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000007792 addition Methods 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 abstract description 3
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000009826 distribution Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000004075 alteration Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q3/00—Devices holding, supporting, or positioning work or tools, of a kind normally removable from the machine
- B23Q3/155—Arrangements for automatic insertion or removal of tools, e.g. combined with manual handling
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q3/00—Devices holding, supporting, or positioning work or tools, of a kind normally removable from the machine
- B23Q3/155—Arrangements for automatic insertion or removal of tools, e.g. combined with manual handling
- B23Q3/15503—Processes characterized by special sequencing of operations or the like, e.g. for optimizing tool changing time or capacity in tool storage
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Mechanical Engineering (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Operations Research (AREA)
- Primary Health Care (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Manufacturing & Machinery (AREA)
- Educational Administration (AREA)
- Multi-Process Working Machines And Systems (AREA)
Abstract
The application discloses an intelligent processing unit cutter requirement optimization method, which comprises the following steps: determining the initial cutter number and optimizing the initial cutter requirement to obtain the final cutter number. The determination of the initial number of cutters is based on the types of cutters in the scheduling task sheet and the corresponding use time of the cutters, and is determined by calculating the number of cutters which need to be added in the cutter library to minimize the maximum finishing time. The application obtains the number of the added tools needed by various tools by comparing the number of the initial tools with the minimum number of the tools in the production scheduling task list, reduces the number of the added tools, judges whether the optimizing effect is met or not by the influence of the change of the number of the tools on the maximum finishing time, comprehensively considers the maximum finishing time and the tool cost for optimizing the tool requirements, and has obvious optimizing effect.
Description
Technical Field
The application relates to the field of cutter configuration in high-precision part machining, in particular to an intelligent machining unit cutter requirement optimization method which can simultaneously consider the minimum maximum finishing time and cutter cost and can be used for real-time cutter requirement configuration of a multi-machine tool machining unit.
Background
Along with the increase of market competition pressure and production cost in the manufacturing industry, the traditional production mode is difficult, and the improvement of the intelligent level of the manufacturing industry is urgent. In this context, enterprises introduce intelligent processing units to cope with the production modes of small batches of various varieties, which diversify and structure the demands of manufacturing processing on tools, while tools are a very important processing resource in manufacturing workshops and occupy one quarter of the processing and manufacturing costs. Therefore, the configuration of the tool resources needs to be optimized. When the intelligent processing unit is manufactured in a workshop to carry out production and processing tasks, firstly, all cutter types and the number of corresponding cutters required by processing the batch of parts are acquired through a production scheduling task list, all cutters are placed in a machine tool magazine and a central cutter magazine of the intelligent processing unit, and finally, the cutters are conveyed to a designated machine tool through a cutter adjusting mechanism to be processed.
In the above-described intelligent machining unit machining process, the number of tools is determined by the tools required for machining the batch of parts by the production job ticket. If redundancy of cutter configuration causes cost increase, the cutter configuration is less likely to generate order delay. Therefore, the cutter requirement scheme needs to be optimized, the phenomenon that the number of cutters is unreasonable is reduced, the requirement of the cutters is adjusted in real time, the improvement of the cutter utilization rate is realized, the cutter cost is reduced, and the processing efficiency of the intelligent processing unit is improved.
The application of the patent number CN114055224A discloses a tool magazine management system for an intelligent numerical control machining center, which enables a plurality of machine tools to share one tool through arranging the intelligent tool magazine management system, and can intelligently select the tool according to the machining condition and the abrasion degree of the tool; the intelligent tool magazine management system can also determine the minimum quantity data of each tool according to the condition that a plurality of machine tools use tools and the time of processing programs and the speed of tool switching, when calculating the number of tools, the difference between the use time of the tools on the machine tools and the total time of tool carrying switching is considered, if the use time is larger than the difference, one tool is added, otherwise, the tools are not added, so that the minimum quantity data of the tools is reduced as much as possible. However, the foregoing application defines that m machine tools use one tool at a time, and that the ordering of the tools on the multiple machine tools is the same, which is difficult to achieve in practical production. In addition, in actual production, the production scheme of the processing unit is not constant, and a small amount of insert sheets, temporary adjustment of yield and other conditions often occur, so that the tool configuration method disclosed by the application cannot realize real-time adjustment of tool configuration. A heuristic algorithm is proposed in an algorithm overview for solving the scheduling problem of a flexible job shop, can simulate and calculate to obtain the maximum completion time, and is applied to the task scheduling of the shop, but is not used for guiding the optimization of the cutter requirements.
Disclosure of Invention
In order to solve the technical problems that in the traditional cutter distribution, the cutter utilization rate is low, the cutter use number is unreasonable and the real-time adjustment of cutter configuration is difficult to realize, the application provides an intelligent cutter demand optimization method for a processing unit, which starts with two aspects of cutter distribution scheme and cutter number optimization, reasonably distributes cutters for all machine tools of a processing center, reduces unreasonable cutter use and improves the production efficiency of the processing unit.
In order to achieve the technical purpose, the application adopts the following technical scheme:
an intelligent machining unit cutter requirement optimization method, comprising the following steps:
s1, determining the types of tools required by machining and the minimum number of tools of each type through a production scheduling task list and a corresponding part process machining program, and initializing the types of the tools in a tool library and the corresponding use machining time;
s2, real-time monitoring of the completion condition of the production scheduling task list, and determining the minimum tool use time and the corresponding machine tool number of the tools in all the processing units at the current processing time and the unused tool types in the tool library;
s3, judging whether a waiting sequence of the machine tool corresponding to the minimum tool time is empty, if the waiting sequence is not empty, reading the next tool data needed in the task sequence of the machine tool, serving as the current tool data of the machine tool, updating the tool use time of the machine tool into the processing time of the next tool, judging whether the tool needs to be added according to the existing tools of a tool library, and if not, maintaining the current tool data and the original tool use time;
s4, subtracting the minimum tool time from the tool use time of all the processing units; reading the waiting sequences of all the machine tools, judging whether the waiting sequences of all the machine tools are empty, if not, turning to step S2; otherwise, go to step S5;
s5, counting the types and the numbers of the added tools required by the maximum finishing time to obtain the number of the initial tools required by the rest processing process;
s6, obtaining the number of the added cutters of various cutters under the condition of minimizing the maximum finishing time by calculating the difference value between the initial cutter number obtained in the step S5 and the minimum cutter number obtained in the step S1, and arranging the difference values from large to small to obtain a descending difference value sequenceWherein->Representing the number of additions required for the k-th type of tool;
s7, let k=1;
s8, sequentially subtracting 1 from the number of cutter types corresponding to the kth type, and calculating the maximum finishing time in a simulation way under the condition that the number of the cutter types is subtracted by 1 each time until the fluctuation of the maximum finishing time after subtracting 1 exceeds the preset fluctuation threshold value compared with the fluctuation of the corresponding minimum value;
s9, making k=k+1, repeating the step S8 until k=n+1, and outputting the final optimized number of tools, wherein the data of all types of tools reach the minimum value;
s10, returning to the step S2 until the production scheduling task list is completed.
Further, in step S1, the process of determining the kind of the tool required for machining and the minimum number of tools per kind of tool by scheduling the task sheet includes:
when the workshop processing is scheduled in the intelligent processing system, the machine tool, the parts and the cutters are allocated, D is the required cutter type set,wherein->Represents the kth tool; let M be the set of machine tools used for machining, +.>Wherein->Indicating the ith machine tool, < >>；
G codes of the process are obtained through a scheduling task list of the intelligent processing system, and the G codes are uploaded to determine the types of the cutters, the minimum cutter number and the cutter service time.
Further, in step S1, in the tool placement process, tools required for p processes before machining are placed in a tool magazine of the machine tool, which is closer to the machine tool, and the rest tools are placed in a central tool magazine, where p is a positive integer.
Further, in step S3, the process of determining whether to add a tool according to the existing tools in the tool magazine includes the following steps:
s31, reading a next tool required in a task sequence of the machine tool, judging whether the tool exists in a tool library of the machine tool, if so, selecting the tool in the tool library of the machine tool, and ending the flow without adding a new tool, otherwise, turning to the step S32;
s32, judging whether the cutter exists in the central cutter library, if so, borrowing the cutter, and not adding a new cutter, ending the flow, otherwise, turning to the step S33;
s33, judging whether the cutter exists in the cutter libraries of the rest machine tools, if so, borrowing the cutter, not adding a new cutter, ending the flow, otherwise, turning to the step S34;
s34, adding a new tool into a tool library of the machine tool.
Further, in step S8, the preset rise threshold is 1%.
Further, in step S8, a heuristic algorithm is adopted to calculate the maximum completion time.
Compared with the prior art, the application has the following beneficial effects:
firstly, the intelligent processing unit cutter requirement optimization method can automatically trigger according to the process G code after the processing unit production scheduling scheme and the process G code are generated, and has the advantage of high intelligence.
Secondly, according to the intelligent processing unit cutter requirement optimization method, a heuristic algorithm is adopted to calculate the maximum finishing time, and cutter requirements are reasonably optimized by means of the maximum finishing time.
Thirdly, according to the intelligent processing unit cutter requirement optimization method, after the processing unit cutters are optimized, cutter configuration can be reasonably distributed according to real-time production scheduling information, processing efficiency is effectively improved, redundant cutters in processing can be automatically screened, and cutter cost is greatly reduced.
Fourth, according to the intelligent processing unit cutter requirement optimization method, various data processed in real time are collected when the processing unit is optimized, a process database is built, and effective traceability of processing data is achieved.
Fifthly, according to the intelligent processing unit cutter requirement optimization method, a processing unit can acquire real-time information of all cutters in processing, wherein the real-time information comprises information such as the current cutter position and cutter processing time; the processing state is analyzed by collecting processing data of the processing unit in real time, and the cutter requirement is optimized based on the production line state.
Drawings
FIG. 1 is a flow chart of an intelligent machining unit cutter requirement optimization method of the application;
FIG. 2 is a schematic diagram of a processing unit to which the method of the present application is applicable;
FIG. 3 is a schematic illustration of a specific technical route of the method of the present application.
Detailed Description
Embodiments of the present application are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1 and 3, the application discloses an intelligent processing unit cutter requirement optimization method, which comprises the following steps:
s1, determining the types of tools required by machining and the minimum number of tools of each type through a production scheduling task list and a corresponding part process machining program, and initializing the types of the tools in a tool library and the corresponding use machining time;
s2, real-time monitoring of the completion condition of the production scheduling task list, and determining the minimum tool use time and the corresponding machine tool number of the tools in all the processing units at the current processing time and the unused tool types in the tool library;
s3, judging whether a waiting sequence of the machine tool corresponding to the minimum tool time is empty, if the waiting sequence is not empty, reading the next tool data needed in the task sequence of the machine tool, serving as the current tool data of the machine tool, updating the tool use time of the machine tool into the processing time of the next tool, judging whether the tool needs to be added according to the existing tools of a tool library, and if not, maintaining the current tool data and the original tool use time;
s4, subtracting the minimum tool time from the tool use time of all the processing units; reading the waiting sequences of all the machine tools, judging whether the waiting sequences of all the machine tools are empty, if not, turning to step S2; otherwise, go to step S5;
s5, counting the types and the numbers of the added tools required by the maximum finishing time to obtain the number of the initial tools required by the rest processing process;
s6, obtaining the number of the added cutters of various cutters under the condition of minimizing the maximum finishing time by calculating the difference value between the initial cutter number obtained in the step S5 and the minimum cutter number obtained in the step S1, and arranging the difference values from large to small to obtain a descending difference value sequenceWherein->Representing the number of additions required for the k-th type of tool;
s7, let k=1;
s8, sequentially subtracting 1 from the number of cutter types corresponding to the kth type, and calculating the maximum finishing time in a simulation way under the condition that the number of the cutter types is subtracted by 1 each time until the fluctuation of the maximum finishing time after subtracting 1 exceeds the preset fluctuation threshold value compared with the fluctuation of the corresponding minimum value;
s9, making k=k+1, repeating the step S8 until k=n+1, and outputting the final optimized number of tools, wherein the data of all types of tools reach the minimum value;
s10, returning to the step S2 until the production scheduling task list is completed.
In order to realize reasonable distribution of the cutters and optimization of cutter requirements, the application adopts the following technical route: after the workshop processing unit scheduling scheme is generated, a process G code is generated, wherein the process G code comprises the type of the tools required by processing and the minimum number of the tools, and meanwhile, a tool requirement optimizing method is automatically triggered to optimize the tool requirement. Let D be the set of desired tool types,wherein->Represents the kth tool; let M be the set of machine tools used for machining, +.>Wherein->The machine tool of the i-th machine tool is shown,；
after the minimum number of tools is determined according to the process G code, the machining center tool type and the corresponding time of use are initialized. And finishing the above steps to finish the initialization of the optimization of the cutter requirements.
Determining the minimum tool use time of the tools in all processing units at the current processing time on the basis of initialized tool data, recording the minimum tool use time as t, and simultaneously solving the machine tool number corresponding to the processing time of the tools. If the machine tool machining waiting sequence is not empty, the next tool in the task sequence needs to be recorded>Next, it will be determined whether or not a new tool needs to be added by whether or not the tool exists in the tool magazine (in the present application, when the position of the tool magazine is not specified, it represents the tool magazine and the center tool magazine including all the machine tools). When the machining unit performs machining, the tools in the tool libraries and the central tool libraries of the machine tool are preferentially used, and when the tools in the tool libraries and the central tool libraries of the machine tool do not exist, the tools are preferentially selected to be borrowed from the tool libraries of the other machine tools. When the required tools cannot be obtained in both cases, a tool is added to the tool library.
Changing tools required in the processing unitAfter that, the processing unit is configured to,at this time, the tool service time of the machining position is updated to be a new toolThe machining time is obtained by subtracting the minimum tool time t at the current moment from the tool time of all machining positions, so that the number of currently existing machine tools with working procedures to be machined (namely, the machine tools with other machining tasks in a waiting sequence) can be obtained, and if the number is smaller than 1, the initial tool number is obtained; if the number is greater than 1, returning to the previous step to continue the operation of distributing and adding the cutters.
Preferably, according to the intelligent processing unit cutter requirement optimization method, when processing information of the cutter is obtained from the processing unit, the processing time and the residual service life of the cutter are obtained through big data prediction, and the prediction result has the characteristics of high accuracy and high practicability. Since the production schedule is not fixed, the remaining life of the tool as initially calculated is not necessarily always adequate for the production needs. In consideration of the above, the application also provides management of the remaining lives of the tools in the tool library and the center tool library of the machine tool, for example, when judging whether the tools meeting the requirement of the next working procedure exist in the tool library, the application can compare the working procedure time length with the remaining lives of the tools to judge whether the tools need to be added. When the residual life of one cutter in the cutter library is difficult to be qualified for any next working procedure task, the cutter is taken as a scrapped cutter. Because the cutter requirement optimization method of the application optimizes in real time according to the current actual production scheduling task, the optimal control of the number and types of cutters required by the working procedure can be realized by only increasing the judgment of the working procedure time length and the cutter residual life in the cutter searching process and obtaining whether the cutters are required to be added according to the judgment result.
And comparing the number of the initial cutters with the minimum number of the cutters to obtain a difference value of the two cutter numbers, and arranging the difference value according to the decreasing order to obtain initial data S of cutter optimization. Wherein the method comprises the steps of，Wherein->Indicating the number of additions required for the kth tool.
Sequentially reducing the number of the cutters corresponding to each cutter type in the series S by one, adopting a heuristic algorithm to simulate and calculate to obtain the maximum finishing time, and optimizing cutter requirements according to the change of the maximum finishing time into two different schemes:
if the fluctuation of the maximum finishing time is larger than 1%, the reduction of the number of cutters has a larger influence on the production efficiency, namely, the increase of the maximum finishing time is larger, the cutter requirements cannot be further optimized, and the number of cutters is not reduced; if the fluctuation of the maximum finishing time is less than 1%, the reduction of the number of the cutters is smaller in machining efficiency, so that the number of the cutters corresponding to the cutters can be reduced, the cost of the cutters can be reduced on the basis of ensuring the machining efficiency, and the cutter requirement is optimized.
After the optimization of the requirements of all the tools is completed, the final number of tools will be obtained. The number of the final cutters ensures the stability of production efficiency, greatly improves the utilization rate of the cutters, and has great significance for optimizing the cutter requirements of the processing unit.
As shown in fig. 2, an actual machining unit is composed of four numerical control machining tools and a central tool magazine, and the tool magazines, namely the tool magazines of the machine tools, are also present in the four numerical control machining tools. The tool magazine and the machine tool magazine in the machining unit can be distributed mutually, and the tool can be conveyed to various positions for machining through the manipulator in the machining unit, so that the time for changing the tool in machining can be greatly reduced, and the tool changing efficiency is improved. When the processing unit needs to process a batch of parts, the intelligent scheduling system performs intelligent scheduling according to the types, the quantity and the shipment period of the processed parts. And according to the requirements of the intelligent scheduling task list on the cutters, acquiring the types of the required cutters and the number of the cutters, and placing the corresponding cutters in the central cutter library and the machine tool cutter library. And according to the requirements of the intelligent scheduling task list on the cutters, acquiring the types of the required cutters and the number of the cutters, and placing the corresponding cutters in the central cutter library and the machine tool cutter library. In the process of placing the tools, the tools required by machine tool machining are placed in a tool magazine of the machine tool, which is close to the machine tool, and the rest tools are placed in the tool magazine of the center. For example, in the tool placement process, tools required for p steps before machining are placed in a tool magazine of a machine tool close to the machine tool, the remaining tools are placed in a central tool magazine, and p is a positive integer. The mode can comprehensively consider the convenience of tool taking and the utilization rate of tools. At the moment, the cutter requirement optimizing method is automatically triggered, the cutter type and the corresponding service time are initialized according to the process G code, meanwhile, the cutter requirement optimizing method can acquire the information of the residual service life, the rated service life and the like of the cutter according to big data, and the cutter is equipped before machining.
The specific implementation method of the optimization method comprises the following steps: and acquiring tool configuration information and initializing the type and corresponding service time of the tool before the machining unit performs machining. After the initialization of the cutter information is completed, the processing unit starts to process, determines the minimum cutter time in the current four machine tools and records the corresponding machine tools, and the machine tools are recorded as。
In machine toolsOn the premise that the machining sequence is not empty, the tool required by the next machining sequence of the machine tool is prepared in advance, and the tool is quickly changed to the machining position by the manipulator after machining is completed. In the process, whether the handle tool exists in the central tool magazine and the machine tool magazine or not is judged, and if the tool cannot be called out at the processing moment, a new tool needs to be added in the tool magazine.
In machine toolsAfter a new tool is replaced, the tool use time of the machine tool is updated to the machining time of the new tool, the minimum tool use time of the tools is subtracted from the machining position tool use time of the rest of the machine tools, and finally whether machining is performed by the machine tool is judgedAnd judging whether the machining task is finished or not, and obtaining the initial cutter number.
After all tools required by four machine tools to be processed are obtained, and after the initial number of tools is calculated, the number of tool difference values is calculated，/>Wherein->Represents the number of additions required for the kth cutter, will +.>The corresponding cutter is reduced by one and the maximum finishing time is calculated in a simulation mode. Finally, whether the reduction of the cutters is reasonable or not is judged through the change of the maximum finishing time, and the number of the final cutters after the cutter requirements are optimized can be obtained after all the cutters are optimized.
In practical application, the intelligent processing unit cutter requirement optimization method can acquire real-time information of all cutters in processing, including information such as current cutter position, cutter processing time and the like; the processing state is analyzed by collecting processing data of the processing unit in real time, and the cutter requirement is optimized based on the production line state. The cutter configuration of the processing unit is used as an important ring in a production scheduling plan, and the reasonable configuration is carried out on the cutters, so that the extra production and manufacturing time caused by cutter replacement is reduced, and the production efficiency is improved; in addition, the cost of the cutter in the processing unit is not negligible, and in the production and manufacture, the cutter is used as an important production resource to restrict the production. Preferably, the application can also collect various data processed in real time when the processing unit is optimized, and construct a process database to realize effective traceability of processing data.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (6)
1. The intelligent machining unit cutter requirement optimization method is characterized by comprising the following steps of:
s1, determining the types of tools required by machining and the minimum number of tools of each type through a production scheduling task list and a corresponding part process machining program, and initializing the types of the tools in a tool library and the corresponding use machining time;
s2, real-time monitoring of the completion condition of the production scheduling task list, and determining the minimum tool use time and the corresponding machine tool number of the tools in all the processing units at the current processing time and the unused tool types in the tool library;
s3, judging whether a waiting sequence of the machine tool corresponding to the minimum tool time is empty, if the waiting sequence is not empty, reading the next tool data needed in the task sequence of the machine tool, serving as the current tool data of the machine tool, updating the tool use time of the machine tool into the processing time of the next tool, judging whether the tool needs to be added according to the existing tools of a tool library, and if not, maintaining the current tool data and the original tool use time;
s4, subtracting the minimum tool time from the tool use time of all the processing units; reading the waiting sequences of all the machine tools, judging whether the waiting sequences of all the machine tools are empty, if not, turning to step S2; otherwise, go to step S5;
s5, counting the types and the numbers of the added tools required by the maximum finishing time to obtain the number of the initial tools required by the rest processing process;
s6, obtaining the number of the added cutters of various cutters under the condition of minimizing the maximum finishing time by calculating the difference value between the initial cutter number obtained in the step S5 and the minimum cutter number obtained in the step S1, and arranging the difference values from large to small to obtain a descending difference value sequenceWherein->Representing the number of additions required for the k-th type of tool;
s7, let k=1;
s8, sequentially subtracting 1 from the number of cutter types corresponding to the kth type, and calculating the maximum finishing time in a simulation way under the condition that the number of the cutter types is subtracted by 1 each time until the fluctuation of the maximum finishing time after subtracting 1 exceeds the preset fluctuation threshold value compared with the fluctuation of the corresponding minimum value;
s9, making k=k+1, repeating the step S8 until k=n+1, and outputting the final optimized number of tools, wherein the data of all types of tools reach the minimum value;
s10, returning to the step S2 until the production scheduling task list is completed.
2. The intelligent machining unit cutter requirement optimizing method according to claim 1, wherein the process of determining the kind of cutters required for machining and the minimum number of cutters per kind of cutter by scheduling a job ticket in step S1 includes:
when the workshop processing is scheduled in the intelligent processing system, the machine tool, the parts and the cutters are allocated, D is the required cutter type set,wherein->Represents the kth tool; let M be the set of machine tools used for machining, +.>Wherein->The machine tool of the i-th machine tool is shown,；
g codes of the process are obtained through a scheduling task list of the intelligent processing system, and the G codes are uploaded to determine the types of the cutters, the minimum cutter number and the cutter service time.
3. The intelligent machining unit tool demand optimizing method according to claim 1, wherein in the tool placement process in step S1, tools required for p processes before machining are placed in a tool magazine of a machine tool which is closer to the machine tool, and the remaining tools are placed in a central tool magazine, wherein p is a positive integer.
4. The intelligent machining unit tool demand optimizing method according to claim 3, wherein in step S3, the process of judging whether the tool needs to be added according to the existing tools of the tool magazine comprises the steps of:
s31, reading a next tool required in a task sequence of the machine tool, judging whether the tool exists in a tool library of the machine tool, if so, selecting the tool in the tool library of the machine tool, and ending the flow without adding a new tool, otherwise, turning to the step S32;
s32, judging whether the cutter exists in the central cutter library, if so, borrowing the cutter, and not adding a new cutter, ending the flow, otherwise, turning to the step S33;
s33, judging whether the cutter exists in the cutter libraries of the rest machine tools, if so, borrowing the cutter, not adding a new cutter, ending the flow, otherwise, turning to the step S34;
s34, adding a new tool into a tool library of the machine tool.
5. The intelligent machining unit cutter requirement optimization method according to claim 1, wherein in step S8, the preset expansion threshold is 1%.
6. The intelligent machining unit cutter requirement optimization method according to claim 1, wherein in step S8, a heuristic algorithm is adopted to calculate the maximum finishing time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311254915.0A CN116984924B (en) | 2023-09-27 | 2023-09-27 | Intelligent machining unit cutter requirement optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311254915.0A CN116984924B (en) | 2023-09-27 | 2023-09-27 | Intelligent machining unit cutter requirement optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116984924A true CN116984924A (en) | 2023-11-03 |
| CN116984924B CN116984924B (en) | 2023-12-01 |
Family
ID=88523589
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311254915.0A Active CN116984924B (en) | 2023-09-27 | 2023-09-27 | Intelligent machining unit cutter requirement optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116984924B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001062671A (en) * | 1999-08-26 | 2001-03-13 | Canon Inc | Schedule operation method for machine tool |
| JP2003145379A (en) * | 2001-11-08 | 2003-05-20 | Enshu Ltd | Method of rearranging tool in tool magazine and method of rearranging tools in a plurality of tool magazines |
| US20080281463A1 (en) * | 2006-01-18 | 2008-11-13 | Suh Suk Hwan | Method of Non-Linear Process Planning and Internet-Based Step-Nc System Using the Same |
| CN101533274A (en) * | 2009-01-12 | 2009-09-16 | 上海理工大学 | Method for dispatching NC machine tool processing tasks and cutting tools |
| CN101770222A (en) * | 2010-03-09 | 2010-07-07 | 江南大学 | Method for online managing cutting tool of numerically controlled lathe |
| US20120143574A1 (en) * | 2010-11-30 | 2012-06-07 | Krishna Vepa | Process tool chemical and gas usage optimization |
| US20200027042A1 (en) * | 2017-05-05 | 2020-01-23 | DeHart Consulting, LLC | System and method for time based, demand-pull real time dispatching |
| CN110991860A (en) * | 2019-11-28 | 2020-04-10 | 王解法 | Method for establishing cutter scheduling model |
| CN111340345A (en) * | 2020-02-20 | 2020-06-26 | 中北大学 | Cutter scheduling method based on improved particle swarm optimization |
| CN111761394A (en) * | 2019-04-02 | 2020-10-13 | 富泰华工业（深圳）有限公司 | Intelligent tool magazine device and tool management method |
| CN113705978A (en) * | 2021-08-03 | 2021-11-26 | 西安交通大学 | Static and dynamic integrated decision-making method and system for multi-machine task cutter |
| CN114912706A (en) * | 2022-06-01 | 2022-08-16 | 哈尔滨工业大学 | Cutter matching method based on particle swarm optimization |
| CN115293436A (en) * | 2022-08-12 | 2022-11-04 | 南京维拓科技股份有限公司 | Flexible production scheduling method based on particle swarm optimization model |
| CN116540659A (en) * | 2023-07-04 | 2023-08-04 | 成都飞机工业（集团）有限责任公司 | Large complex product workshop scheduling method, system, equipment and medium |
-
2023
- 2023-09-27 CN CN202311254915.0A patent/CN116984924B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001062671A (en) * | 1999-08-26 | 2001-03-13 | Canon Inc | Schedule operation method for machine tool |
| JP2003145379A (en) * | 2001-11-08 | 2003-05-20 | Enshu Ltd | Method of rearranging tool in tool magazine and method of rearranging tools in a plurality of tool magazines |
| US20080281463A1 (en) * | 2006-01-18 | 2008-11-13 | Suh Suk Hwan | Method of Non-Linear Process Planning and Internet-Based Step-Nc System Using the Same |
| CN101533274A (en) * | 2009-01-12 | 2009-09-16 | 上海理工大学 | Method for dispatching NC machine tool processing tasks and cutting tools |
| CN101770222A (en) * | 2010-03-09 | 2010-07-07 | 江南大学 | Method for online managing cutting tool of numerically controlled lathe |
| US20120143574A1 (en) * | 2010-11-30 | 2012-06-07 | Krishna Vepa | Process tool chemical and gas usage optimization |
| US20200027042A1 (en) * | 2017-05-05 | 2020-01-23 | DeHart Consulting, LLC | System and method for time based, demand-pull real time dispatching |
| CN111761394A (en) * | 2019-04-02 | 2020-10-13 | 富泰华工业（深圳）有限公司 | Intelligent tool magazine device and tool management method |
| CN110991860A (en) * | 2019-11-28 | 2020-04-10 | 王解法 | Method for establishing cutter scheduling model |
| CN111340345A (en) * | 2020-02-20 | 2020-06-26 | 中北大学 | Cutter scheduling method based on improved particle swarm optimization |
| CN113705978A (en) * | 2021-08-03 | 2021-11-26 | 西安交通大学 | Static and dynamic integrated decision-making method and system for multi-machine task cutter |
| CN114912706A (en) * | 2022-06-01 | 2022-08-16 | 哈尔滨工业大学 | Cutter matching method based on particle swarm optimization |
| CN115293436A (en) * | 2022-08-12 | 2022-11-04 | 南京维拓科技股份有限公司 | Flexible production scheduling method based on particle swarm optimization model |
| CN116540659A (en) * | 2023-07-04 | 2023-08-04 | 成都飞机工业（集团）有限责任公司 | Large complex product workshop scheduling method, system, equipment and medium |
Non-Patent Citations (3)
| Title |
|---|
| 孙栋;李郝林;: "数控机床任务调度与刀具分配网络化管理系统", 精密制造与自动化, no. 04 * |
| 曹阳明;周亚勤;杨建国;刘凯强;: "考虑刀具约束的作业车间调度研究", 现代制造工程, no. 12 * |
| 王卫楠;楼佩煌;: "FMS刀具管理优化技术研究与实验系统开发", 制造技术与机床, no. 04 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116984924B (en) | 2023-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109359884B (en) | Aviation complex equipment structural member workshop operation planning and rescheduling method | |
| CN110955206B (en) | Order scheduling and distribution scheduling method and system | |
| CN111966050A (en) | AMMAS-GA nesting algorithm-based dual-resource mold job shop scheduling optimization method | |
| CN111738578B (en) | Discrete type workshop scheduling method under dynamic environment | |
| EP0755537B1 (en) | Priority queue filtering system and method of operation | |
| CN103942610B (en) | The polymorphic configuration optimization method of reconfigurable manufacturing system of task based access control | |
| CN108171372B (en) | Multi-variety production batch scheduling method with preparation time | |
| CN112561225B (en) | Flexible job shop scheduling method based on marker post co-evolution algorithm | |
| CN100401219C (en) | Recombinative production line scheduling method based on genetic algorithm | |
| EP2610696A1 (en) | Process design/production planning device | |
| CN115471133A (en) | Workshop comprehensive scheduling system based on order management and rolling optimization scheduling | |
| CN111144710A (en) | Construction and dynamic scheduling method of sustainable hybrid flow shop | |
| CN111291969B (en) | Automobile reordering method based on genetic algorithm | |
| CN109615115B (en) | Task reliability-oriented integrated production task scheduling method | |
| CN113139720B (en) | Casting shop production scheduling optimization method with learning effect | |
| CN116984924B (en) | Intelligent machining unit cutter requirement optimization method | |
| CN113962540A (en) | Maintenance task scheduling method and related equipment | |
| Graver et al. | A tool provisioning problem in an FMS | |
| Turkcan et al. | Due date and cost-based FMS loading, scheduling and tool management | |
| CN111050992B (en) | Method and device for computer-aided optimization of the tool occupancy of a magazine position by tools within at least one magazine for a machine tool | |
| JP7261639B2 (en) | Production control system and production control method | |
| CN114442578B (en) | Cutter joint dynamic scheduling method for complex-profile intelligent production unit task | |
| CN110705844A (en) | Robust optimization method of job shop scheduling scheme based on non-forced idle time | |
| JPH09183044A (en) | Preparation of production planning | |
| CN114019922A (en) | Electronic precision part flexible workshop scheduling method based on particle swarm annealing algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |