Cutter arrangement optimization method
By optimizing tool layout through drilling, scoring, and genetic algorithms, the problems of high computational load and regional optimal solutions in existing technologies are solved, achieving efficient tool layout and improved machining efficiency under various requirements.
Patent Information
- Application Number
- CN202511161374.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-19
- Publication Date
- 2025-11-07
AI Technical Summary
Existing tool optimization algorithms require a lot of computation, are prone to getting stuck in the optimal solution in a region, are difficult to meet various machining requirements, and cannot maximize the machining efficiency of the drill bag.
By employing drilling algorithms, scoring algorithms, and genetic algorithms, the tool arrangement is optimized by calculating the tool holder position, tool information, and plate hole information in real time. The optimal tool arrangement is then generated by combining weight parameters and genetic algorithms.
It enables the rapid calculation of optimal tool arrangement based on different needs, improves drilling efficiency, meets various processing requirements, and reduces the amount of calculation and the complexity of modifying algorithms.
Smart Images
Figure CN120911296A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of numerical control drilling processing, in particular to a tool arrangement optimization method. BACKGROUND
[0002] Numerical control drilling is a common device in furniture production process, mainly used for drilling, slotting, milling and other processes on plate processing. With the rapid development of numerical control technology, drilling equipment has evolved from the original single-sided processing equipment to multi-sided processing equipment, which can process up to six sides of the plate at the same time. This has new requirements for the speed and accuracy of drilling processing. Among them, how to maximize the processing efficiency of drilling, the layout of tools on the drilling is crucial. In the past, it was manually laid out by the processing experience of the operator or used basic tool optimization algorithms, which could not meet the actual processing needs.
[0003] At present, the common tool optimization algorithm has the following shortcomings:
[0004] 1. A large number of calculations are required: Assuming that the drilling has 16 tool holders and 50 tools can be installed in any tool holder, there are 50 combinations of tool arrangement, and it is impossible to try all of them;
[0005] 2. It is easy to fall into a regional optimal solution: there are a large number of possibilities for tool arrangement, and the solution found by using general optimization methods may only be a regional optimal solution rather than a global optimal solution.
[0006] 3. It is difficult to meet various demand situations: the pros and cons of tool arrangement have multiple scoring methods, such as the most common scoring method of less processing time, but there are some processes that cannot be processed or processed slowly but can be processed all processes, which is not suitable for this scoring method, such as patent CN118364661B, the tool layout combination weight depends only on the number of drill holes. Therefore, when facing different needs, the algorithm may need to be modified significantly. SUMMARY
[0007] To solve the above problems, the present application provides a tool arrangement optimization method, which can calculate the best tool arrangement in real time according to the tool holder position, tool information and plate hole information, guide the operator to layout the tools on the drilling, effectively improve the processing efficiency of the drilling, and in different demand situations, only need to add or modify the weight setting of the scoring algorithm, to meet the new needs.
[0008] According to one aspect of the present application, a tool arrangement optimization method is provided, comprising the following steps:
[0009] S1: Drilling algorithm: sequentially taking each tool holder as the center, trying to match each hole, establishing all formulas, finding out the combination that can be drilled at one time, and selecting the optimal formula to drill first;
[0010] S2: Scoring algorithm: selecting weight parameters and setting the weight of each weight parameter;
[0011] S3: Genetic algorithm: taking each tool holder of the drill package as a gene, taking the available tools as gene species, and taking the gene index of the available tool diameter to sequentially increase from 0;
[0012] The genetic algorithm includes the following steps
[0013] a: generating a gene pool and randomly selecting multiple initial genes from the gene pool;
[0014] b: mating each initial gene to generate offspring genes from the father gene and the mother gene;
[0015] c: according to the mutation rate, sequentially determining whether each gene remains the same or mutates into another gene;
[0016] d: eliminating unsuitable genes, recording the best gene, and converting it into a tool arrangement.
[0017] In some embodiments, in step S1, each formula consists of a central hole and one or more adjacent holes. It is beneficial to describe the composition of the formula.
[0018] In some embodiments, in step S2, the weight parameters selected are the total number of drilling holes, the NC file processing time, and the total movement distance. It is beneficial to describe one way of selecting weight parameters.
[0019] In some embodiments, in step S2, the weight parameters selected are the number of unprocessable holes, the number of hole turning and milling types, and the total number of drilling holes. It is beneficial to describe another way of selecting weight parameters.
[0020] In some embodiments, the weight of the unprocessable hole number is the largest, the weight of the turning and milling type number is the second largest, and the weight of the total drilling hole number is the lowest. It is beneficial to describe the weight order of the above weight parameters.
[0021] In some embodiments, in step S3, the available tools are formed by duplicating N tools from each tool in the original tool, where N is the tool inventory. It is beneficial to describe the formation method of the available tools.
[0022] In some embodiments, in step a, the initial genes cannot be repeated. It is beneficial to describe the restriction of selecting initial genes.
[0023] In some embodiments, in step b, the source of each initial gene is randomly assigned to be a paternal or maternal homologous gene, and the higher the score of a gene combination, the higher the probability of being selected as parents. It is beneficial to describe the way to select initial genes.
[0024] In some embodiments, in step b, when x>k, the kth gene of the offspring is selected from the paternal gene, and the xth gene of the maternal is also the same gene, then the xth gene must also be selected from the paternal gene, and recursive checking is performed until there is no x or x≤k. It is beneficial to further describe the algorithm for selecting initial genes.
[0025] In some embodiments, in step c, if the mutation object does not exist in the current genome, the gene is mutated, and if the mutation object exists in the current genome, the gene is exchanged with the mutation object. It is beneficial to further describe the specific way of mutation. BRIEF DESCRIPTION OF DRAWINGS
[0026] Figure 1 A flowchart of a tool arrangement optimization method according to an embodiment of the present application;
[0027] Figure 2 A flowchart of the genetic algorithm is shown. Figure 1 DETAILED DESCRIPTION
[0028] The present application will be further described in detail below with reference to the accompanying drawings.
[0029] As shown in the figure, the tool arrangement optimization method mainly includes the following S1-S3 steps. Figure 1
[0030] S1: Drilling algorithm.
[0031] The drilling algorithm is an algorithm for obtaining tool configuration according to tool holder position, tool information, and plate hole information.
[0032] The flow of the drilling algorithm is as follows:
[0033] Each hole is tried to be matched in sequence with each tool holder as the center, so as to establish all formulas and find out combinations that can be drilled at one time, and then the optimal formula is selected to drill first.
[0034] Each formula is composed of a center hole and one or more adjacent holes.
[0035] The drilling algorithm is not the focus of the present application, and the related algorithm can be selected and designed from existing mature technologies, as long as it can achieve the above-mentioned effect.
[0036] S2: Scoring algorithm.
[0037] The scoring algorithm is used to score the calculation results of the drilling algorithm.
[0038] The scoring algorithm can set relevant weight parameters, that is, select some relevant parameters and set the weight of each parameter, and the parameter can be called a weight parameter.
[0039] Among them, the total drilling times, NC file processing time, total moving distance, etc. can be used as weight parameters.
[0040] Taking the number of unprocessable holes, the number of hole-milling types, and the total number of drilling as weight parameters as an example, the weight can be set as follows:
[0041] 1. The number of unprocessable holes: the weight is the largest, and in principle, the process cannot be processed;
[0042] 2. The number of hole-milling types: the weight is the second largest, and the processing time of milling is much larger than that of drilling;
[0043] 3. The total number of drilling: the weight is the lowest, and the more the number of drilling, the longer the time.
[0044] S3: Genetic algorithm.
[0045] The genetic algorithm is used to find the highest score arrangement according to the scoring algorithm result.
[0046] In the genetic algorithm, each tool holder of the drill pack is regarded as a gene, and the available tool is regarded as a gene type.
[0047] Among them, the formation of the available tool is: according to the original tool (limiting the processing direction to positive and negative X, Y, Z), each tool generates N copies of the tool, where N represents the tool inventory, which is set by the operator.
[0048] For example, the original tool diameter is: 5, 5, 8, 9, and N = 2, then the available tool diameter is: 5, 5, 8, 9, 5, 5, 8, 9.
[0049] The correspondence between the gene and the tool is: the gene index of the available tool diameter starts from 0 and increases sequentially.
[0050] For example, the available tool diameter is: 5, 5, 8, 9, 5, 5, 8, 9, and the gene index is: 0, 1, 2, 3, 4, 5, 6, 7. The initial gene is 0, 1, 2, 3, corresponding to the original tool with a tool diameter of 5, 5, 8, 9, and after several generations of evolution, a child gene may be generated, which is 0, 1, 2, 3, corresponding to the available tool with a tool diameter of 5, 5, 5, 5.
[0051] As shown in Figure 2 , the genetic algorithm mainly includes the following steps:
[0052] a: create a gene pool, and randomly select multiple initial genes from the gene pool, in which each initial gene cannot be repeated.
[0053] For example, the gene pool is 0, 1, 2, 3, 4, 5, 6, 7, the last gene is 7, and a total of 4 non-repeated initial genes are selected, which are the 0th, 1st, 2nd and 3rd initial genes. The steps of selecting initial genes are as follows:
[0054] 1. The 0th initial gene is randomly selected from 0 to 7 in the gene pool, if 5 is selected, then the gene pool is 0, 1, 2, 3, 4, 7, 6, 5, wherein 5 and 7 are exchanged;
[0055] 2. The 1st initial gene is randomly selected from 0 to 6 in the gene pool, if 3 is selected, then the gene pool is 0, 1, 2, 6, 4, 7, 3, 5, wherein 3 and 6 are exchanged;
[0056] 3. The 2nd initial gene is randomly selected from 0 to 5 in the gene pool, if 4 is selected, then the gene pool is 0, 1, 2, 6, 7, 4, 3, 5, wherein 4 and 7 are exchanged;
[0057] 4. The 3rd initial gene is randomly selected from 0 to 4 in the gene pool, if 2 is selected, then the gene pool is 0, 1, 7, 6, 2, 4, 3, 5, wherein 2 and 7 are exchanged.
[0058] b: mating: mating each initial gene to generate offspring genes from the father gene and the mother gene.
[0059] In this step, the source of each initial gene is randomly selected as the gene at the same position of the father or the mother, wherein the higher the score of the gene combination, the higher the probability of being selected as the parent.
[0060] Wherein, when x>k, the kth gene of the offspring selects the father gene, if the xth gene of the mother is also the same gene, then the xth gene must also select the father gene to avoid gene repetition, and recursively check until there is no x or x≤k.
[0061] For example:
[0062] The father gene is 0, 7, 2, 5, and the mother gene is 7, 5, 1, 0, and the selection of the offspring gene x, x, x, x (0th, 1st, 2nd and 3rd genes respectively) has the following steps:
[0063] 1. The 0th gene of the offspring randomly selects the father's gene (such as 0), then the offspring gene is 0, x, x, x, and the following 2-4 checks are made under this premise;
[0064] 2. The 3rd gene of the mother is also 0, and 3>0 is true, so the 3rd gene must also select the father's gene (such as 5), and the current gene of the offspring is 0, x, x, 5;
[0065] 3. The 1st gene of the mother is also 5, and 1>0 is true, so the 1st gene must also select the father's gene (such as 7), and the current gene of the offspring is 0, 7, x, 5;
[0066] 4. The 0th gene of the mother is also 7, but 0>0 is false, so the 0th gene of the offspring does not need to be further checked;
[0067] 5. The 1st gene of the offspring has been determined and does not need to be selected;
[0068] 6. The 2nd gene of the offspring selects the mother's gene (such as 1), and the father does not have gene 1, so it does not need to be further checked, and the current gene of the offspring is 0, 7, 1, 5;
[0069] 7. The 3rd gene of the offspring has been determined and does not need to be selected;
[0070] 8. All of the offspring have been determined, and the process ends.
[0071] c: Mutation: According to the mutation rate set by the operator, each gene is sequentially determined to remain unchanged or mutate into another gene.
[0072] If the mutation object does not exist in the current gene group, the gene mutates, and if the mutation object exists in the current gene group, the gene and the mutation object are exchanged.
[0073] d: Elimination: Eliminate unsuitable genes and record the best gene in the process, that is, the highest score arrangement, and convert it into a tool arrangement.
[0074] The tool arrangement optimization method in the application mainly has the following beneficial effects:
[0075] 1. The best tool arrangement can be calculated in real time according to the tool holder position, tool information and plate hole information, guiding the operator to arrange the tools on the drill package, and effectively improving the processing efficiency of the drill package;
[0076] 2. In different demand situations, only the weight setting of the scoring algorithm needs to be added or modified to meet new demands;
[0077] 3. When the weight setting cannot meet the demand, the drilling algorithm needs to be modified;
[0078] 4. The optimization algorithm does not need to be modified.
[0079] The above merely describes some embodiments of the present application. For those skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which are within the protection scope of the present application.
Claims
1. A method of tool arrangement optimization, characterized by: The method comprises the following steps S1: drilling hole algorithm: sequentially taking each tool holder as the center, trying to match each hole, establishing all formulas, finding out the combination that can be drilled at one time, and selecting the optimal formula to drill first; S2: scoring algorithm: selecting weight parameters and setting the weight of each weight parameter; S3: genetic algorithm: taking each tool holder of the drill package as a gene, taking the available tools as a gene type, and taking the gene index of the tool diameter to sequentially increase from 0; The genetic algorithm comprises the following steps a: generating a gene pool and randomly selecting multiple initial genes from the gene pool; b: mating each initial gene to generate offspring genes from the father gene and the mother gene; c: according to the mutation rate, sequentially determining whether each gene remains unchanged or mutates into another gene; d: eliminating unsuitable genes, recording the best gene, and converting it into a tool arrangement.
2. The method of tool arrangement optimization according to claim 1, characterized in that: In step S1, each formula is composed of a central hole and one or more adjacent holes.
3. The method of tooling arrangement optimization of claim 1, wherein: In step S2, the weight parameters selected are the total number of drilling holes, the NC file processing time, and the total movement distance.
4. The method of tooling arrangement optimization of claim 1, wherein: In step S2, the weight parameters selected are the number of unprocessable holes, the number of hole turning and milling types, and the total number of drilling holes.
5. A method of tooling arrangement optimization according to claim 4, characterized in that: The weight of the number of unprocessable holes is the largest, the weight of the number of turning and milling types is the second largest, and the weight of the total number of drilling holes is the lowest.
6. The method of tooling arrangement optimization of claim 1, wherein: In step S3, the available tools are formed by duplicating N tools from each tool in the original tool set, where N is the tool inventory.
7. The method of tooling arrangement optimization of claim 1, wherein: In step a, the initial genes cannot be repeated.
8. The method of tooling arrangement optimization of claim 1, wherein: In step b, the source of each initial gene is randomly selected as the gene at the same position of the father or the mother, and the higher the score of the gene combination, the higher the probability of being selected as the parents.
9. The method of tooling arrangement optimization of claim 1, wherein: In step b, when x>k, the kth gene of the offspring selects the father gene, and the xth gene of the mother is also the same gene, then the xth gene must also select the father gene, and recursive checking is performed until there is no x or x≤k.
10. The method of tooling arrangement optimization of claim 1, wherein: In step c, if the mutation object does not exist in the current gene group, the gene mutates, and if the mutation object exists in the current gene group, the gene exchanges positions with the mutation object.