Screw tap parameter optimization method

Abstract

The invention discloses a screw tap parameter optimization method, and relates to the technical field of screw tap machining. According to the method, the problem of irrationality of key parameter design in traditional screw tap design is solved. Comprising the following steps of S1, determining a value range of geometric parameters of a to-be-optimized screw tap model, and performing data processing on the geometric parameters, S2, obtaining a fitting equation of the geometric parameters subjected to data processing in the screw tap model and correspondingly generated performance parameters through an orthogonal test, s3, comprehensively analyzing performance parameters generated by the geometric parameters after data processing, and carrying out weight distribution; and S4, optimizing the geometric parameters after data processing and the performance parameters after weight distribution through a multi-island genetic algorithm. Geometric parameters of the screw tap are optimized through the screw tap parameter optimization method, equipment, medium, product and device based on the multi-island genetic algorithm, and the performance of the screw tap in the actual machining process is improved.

Description

Technical Field

[0001] The invention relates to the technical field of tap processing, and in particular to a parameter optimization technology in the tap processing technology. Background Art

[0002] In the tap design process, the rake angle, clearance angle and number of teeth are key parameters. If the rake angle is not selected properly, the cutting edge strength may be weakened, the risk of tool breakage may be increased, and the cutting efficiency and processing quality may be affected. If the rake angle is too large, it may easily cause vibration and reduce processing accuracy; if the rake angle is too small, the cutting friction may be increased, the tool wear may be accelerated, and even cutting difficulties may occur. If the clearance angle is too small, the friction between the cutting edge and the machined surface of the workpiece may be increased, resulting in increased cutting force, increased cutting temperature, accelerated tool wear, and even damage to the cutting edge. Although a large rake angle can reduce friction, it may weaken the cutting edge strength and reduce durability. If the number of teeth is too large, the cutting resistance and load may be increased, resulting in increased cutting force, increased temperature, accelerated tap wear, and even fracture, while affecting the surface quality and accuracy of the thread. If the number of teeth is too small, the cutting amount per tooth may be increased, the cutting edge wear may be aggravated, the durability may be reduced, and the cutting process may not be smooth enough.

[0003] Tap processing involves nonlinear factors and multi-objective optimization issues, such as reducing cutting force, reducing tool wear and increasing tool life. Traditional tap design relies on engineering experience and test data, which is not only time-consuming and costly, but also difficult to obtain the global optimal solution. If the parameter design is unreasonable, the performance of the system or product will not meet the expected standards, increase energy consumption, reduce work efficiency, and require additional resources to correct design defects. This will increase production costs. In addition, the reliability and stability of the product will also be affected, shortening its service life, increasing subsequent maintenance costs, and even causing safety hazards. Summary of the invention

[0004] The invention aims to solve the irrationality of key parameter design in traditional tap design, and further proposes a tap parameter optimization method.

[0005] The technical solution adopted by the present invention to solve the above technical problems includes the following steps:

[0006] S1: Determine the value range of the geometric parameters of the model of the tap to be optimized, and perform data processing on the geometric parameters, wherein the geometric parameters include the tap rake angle γ p 、Tap back angle α p and number of teeth of chip cone N;

[0007] S2: obtaining, by orthogonal test, fitting equations of the geometric parameters after data processing in the tap model and the corresponding performance parameters, wherein the corresponding performance parameters include cutting force F, cutting temperature T and tool wear rate μ;

[0008] S3: Comprehensively analyze the performance parameters generated by the geometric parameters after data processing and assign weights;

[0009] S4: Optimize the geometric parameters after data processing and the performance parameters after weight allocation through multi-island genetic algorithm;

[0010] The processed geometric parameters are encoded into genetic data, and chromosome individuals are randomly generated to form n2 populations;

[0011] Substitute the gene data carried by the chromosome individuals into the fitting equation to obtain the corresponding performance parameters;

[0012] Calculate the fitness of the obtained performance parameters;

[0013] Perform traditional genetic operations on chromosome individuals in each population according to their fitness;

[0014] Randomly select e chromosome individuals for population migration to generate n2 new populations;

[0015] Selecting elite individuals in the new population, wherein the elite individuals are chromosome individuals with the greatest fitness;

[0016] S5: Determine whether the selected elite individual meets the termination condition. If the selected elite individual does not meet the termination condition, repeat S4 to perform gene iteration. If the selected elite individual meets the termination condition, decode the selected elite individual and perform denormalization processing.

[0017] S6: Output the denormalized parameters as the optimal geometric parameters of the tap.

[0018] Furthermore, the value range of the geometric parameters of the tap model to be optimized in S1 is:

[0019]

[0020] Among them, γ pmin For γ p The minimum value of the range, γ pmax For γ p The maximum value of the range, α pmin is α p The minimum value of the range, α pmax is α p The maximum value of the range, N min is the minimum value of the range of N values, N max The maximum value of the range of N values.

[0021] Furthermore, the method of performing data processing on the geometric parameters in S1 is dimensionless normalization processing, and the normalization processing formula is:

[0022]

[0023] Where x represents any geometric parameter to be normalized, x new represents the result of normalization, x max Indicates the maximum value of the range of x values, x min Indicates the minimum value of the range, where x new The value range is [0, 1], and the precision is set to 10 -3 .

[0024] Furthermore, the fitting equation of the geometric parameters after data processing in S2 and the corresponding performance parameters is:

[0025]

[0026] Among them, g1(γ p ,α p ,N) is the fitting equation corresponding to the cutting force F, g2(γ p ,α p ,N) is the fitting equation corresponding to the tool wear rate μ, g3(γ p ,α p ,N) is the fitting equation corresponding to the cutting temperature T.

[0027] Furthermore, the weight allocation method in S3 is implemented by a weight matrix, and the weight matrix is:

[0028] B=[A1 A2 A3],

[0029] A1 is the weight distribution of cutting force, A2 is the weight distribution of cutting temperature, and A3 is the weight distribution of tool wear rate, where A1+A2+A3=1.

[0030] Furthermore, the performance indicator function generated by the weight matrix is:

[0031] W=A1F+A2T+A3μ,

[0032] Furthermore, the genetic data encoding method in S4 is to convert the tap rake angle γ p 、Tap back angle α p , the number of teeth N of the chip cone is encoded as X1, X2, X3, then each chromosome individual is {X1, X2, X3}, where the values ​​of X1, X2 and X3 are between [0, 1], and the precision is set to 10 -3 , the number of populations is n2, and the number of chromosome individuals in each population is n1.

[0033] Furthermore, the fitness described in S4 is calculated by a fitness function, and the fitness function is:

[0034]

[0035] Where R is the fitness and W is the performance indicator function.

[0036] Furthermore, the traditional genetic operation in S4 includes gene selection, gene crossover and gene mutation. The gene selection is implemented by roulette selection. The probability of a chromosome individual in the population being selected is proportional to the fitness function of the chromosome individual. The selection formula is:

[0037]

[0038] Among them, P i is the probability of the i-th gene being selected, R i is the fitness of the i-th chromosome individual;

[0039] Sort the chromosome individuals of each generation in each population in reverse order by adaptation: The corresponding chromosome individual sequence is Let chromosome individuals G1 and G2, G3 and G4, ..., and Crossover is performed according to the gene crossover probability. The gene crossover probability formula is:

[0040]

[0041] Among them, P c max represents the upper bound of the probability of gene crossover, P c min represents the lower bound of the probability of gene crossover, R max Represents the maximum value of population fitness, R avg Represents the average fitness of the population, R c max Indicates the maximum fitness among the parents participating in the crossover, c0 is 9.903438,

[0042] Gene crossover is achieved by two-point crossover, where crossover points are set between X1 and X2, and between X2 and X3, and the chromosomes of two chromosome individuals are exchanged between the two set crossover points.

[0043] The gene mutation is achieved by basic bit mutation, and the gene mutation probability formula is:

[0044]

[0045] Among them, P m max represents the upper bound of the probability of gene mutation, P mmin represents the lower bound of the probability of gene mutation, R max Represents the maximum value of population fitness, R avg Represents the average fitness of the population, R m Represents the fitness of the individual with mutated chromosome, c0 is 9.903438.

[0046] Furthermore, the termination condition in S5 is that when the fitness of the elite individual and the fitness of the population no longer increase, or the number of gene iterations t reaches a preset number of iterations T, the elite individual is decoded and denormalized, and the denormalized parameters are output as the optimal geometric parameters of the tap.

[0047] Compared with the prior art, the present invention has the following effects:

[0048] The tap geometry parameters were optimized by tap parameter optimization method, which improved the tap performance in the actual machining process. The tool wear rate, cutting force and chip temperature of the tap in the cutting process were reduced by about 15% compared with those before optimization.

[0049] The thread surface roughness of the tap actually processed by the tap optimized by the tap parameter optimization method is reduced, the dimensional accuracy is improved, and the processing cycle is shortened. At the same time, the durability of the tap is enhanced, the service life is extended, and the frequency of shutdown and replacement due to tap replacement is reduced;

[0050] Compared with the traditional genetic algorithm, the tap parameter optimization method can process more data and communicate data between populations, which can reduce the probability of the geometric parameters and performance parameters of the tap falling into the local optimal solution;

[0051] The tap parameter optimization method adopts an adaptive multi-island genetic algorithm, which accelerates the convergence speed by 10% to 20% compared with the ordinary multi-island genetic algorithm, greatly improving the calculation speed of the optimal solution of the geometric parameters and performance parameters of the tap. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 Flow chart of the tap parameter optimization method;

[0054] Figure 2 Schematic diagram of the genetic crossover strategy for the tap parameter optimization method;

[0055] Figure 3 Schematic diagram of the genetic mutation strategy for the tap parameter optimization method;

[0056] Figure 4Schematic diagram of population migration strategy for tap parameter optimization method;

[0057] Figure 5 Schematic diagram of the number of teeth N on the chip cone part of the tap geometric parameters;

[0058] Figure 6 The tap rake angle γ is the tap geometric parameter p and tap relief angle α p Schematic diagram of . DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely explain the technical solutions in the embodiments of the present invention. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict, and the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0060] In the description of the present invention, unless otherwise clearly specified and limited, the terms "connected", "connected", and "fixed" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] A method for optimizing tap parameters, see Figure 1 , the specific steps are as follows:

[0062] S1: Determine the value range of the geometric parameters of the model of the tap to be optimized, and perform data processing on the geometric parameters, wherein the geometric parameters include the tap rake angle γ p 、Tap back angle α p and number of teeth N of chip cone, see Figure 5 and Figure 6 ;

[0063] S2: obtaining, by orthogonal test, fitting equations of the geometric parameters after data processing in the tap model and the corresponding performance parameters, wherein the corresponding performance parameters include cutting force F, cutting temperature T and tool wear rate μ;

[0064] S3: Comprehensively analyze the performance parameters generated by the geometric parameters after data processing and assign weights;

[0065] S4: Optimize the geometric parameters after data processing and the performance parameters after weight allocation through multi-island genetic algorithm;

[0066] The processed geometric parameters are encoded into genetic data, and chromosome individuals are randomly generated to form n2 populations;

[0067] Substitute the gene data carried by the chromosome individuals into the fitting equation to obtain the corresponding performance parameters;

[0068] Calculate the fitness of the obtained performance parameters;

[0069] Perform traditional genetic operations on chromosome individuals in each population according to their fitness;

[0070] Randomly select e chromosome individuals for population migration, see Figure 4 , generate n2 new populations;

[0071] Selecting elite individuals in the new population, wherein the elite individuals are chromosome individuals with the greatest fitness;

[0072] S5: Determine whether the selected elite individual meets the termination condition. If the selected elite individual does not meet the termination condition, repeat S4 to perform gene iteration. If the selected elite individual meets the termination condition, decode the selected elite individual and perform denormalization processing.

[0073] S6: Output the denormalized parameters as the optimal geometric parameters of the tap.

[0074] Specifically, Figure 4 In the above, the population migration is to randomly select e chromosome individuals from population 1 and migrate them to population 4, randomly select e chromosome individuals from population 2 and migrate them to population 1, randomly select e chromosome individuals from population 3 and migrate them to population 2, and randomly select e chromosome individuals from population 4 and migrate them to population 3.

[0075] The value range of the geometric parameters of the tap model to be optimized in S1 is:

[0076]

[0077] Among them, γ pmin For γ p The minimum value of the range, γ pmax For γ p The maximum value of the range, α pmin is α p The minimum value of the range, α pmax is α p The maximum value of the range, N min is the minimum value of the N value range, N max The maximum value of the range of N values.

[0078] The method of data processing of the geometric parameters in S1 is dimensionless normalization processing, and the normalization processing formula is:

[0079]

[0080] Where x represents any geometric parameter to be normalized, x new represents the result of normalization, x max Indicates the maximum value of the range of x values, x min Indicates the minimum value of the range, where x new The value range is [0, 1], and the precision is set to 10 -3 .

[0081] The fitting equations of the geometric parameters after data processing described in S2 and the corresponding performance parameters are:

[0082]

[0083] Among them, g1(γ p ,α p ,N) is the fitting equation corresponding to the cutting force F, g2(γ p ,α p ,N) is the fitting equation corresponding to the tool wear rate μ, g3(γ p ,α p ,N) is the fitting equation corresponding to the cutting temperature T.

[0084] The weight distribution method in S3 is implemented by a weight matrix, and the weight matrix is:

[0085] B=[A1 A2 A3],

[0086] Among them, A1 is the weight distribution of cutting force, A2 is the weight distribution of cutting temperature, and A3 is the weight distribution of tool wear rate, where A1+A2+A3=1. While considering the comprehensive weight of each performance, external factors in machining are also considered, including workpiece material and machining technology, which can make the comprehensive weight closer to the actual situation of the machining process.

[0087] The performance index function generated by the weight matrix is:

[0088] W=A1F+A2T+A3μ.

[0089] The fitness described in S4 is calculated by the fitness function, which is:

[0090]

[0091] Among them, R is the fitness and W is the performance indicator function.

[0092] The genetic data encoding method in S4 is to convert the tap front angle γ p 、Tap back angle α p , the number of teeth N of the chip cone is encoded as X1, X2, X3, then each chromosome individual is {X1, X2, X3}, where the values ​​of X1, X2 and X3 are between [0, 1], and the precision is set to 10 -3 , the number of populations is n2, and the number of chromosome individuals in each population is n1.

[0093] The traditional genetic operations described in S4 include gene selection, gene crossover and gene mutation. The gene selection is implemented by roulette selection. The probability of a chromosome individual in the population being selected is proportional to the fitness function of the chromosome individual. The selection formula is:

[0094]

[0095] Among them, P i is the probability of the i-th gene being selected, R i is the fitness of the i-th chromosome individual;

[0096] Sort the chromosome individuals of each generation in each population in reverse order by adaptation: The corresponding chromosome individual sequence is Let chromosome individuals G1 and G2, G3 and G4, ..., and By performing crossover according to the probability of gene crossover and arranging chromosome individuals according to fitness, the probability of gene crossover of chromosome individuals with low fitness can be increased and individuals with high fitness can be performed at a lower probability of gene crossover, so that chromosome individuals with high fitness can be retained to the next generation, which can reduce the algorithm operation time.

[0097] The formula for gene crossover probability is:

[0098]

[0099] Among them, P c max represents the upper bound of the probability of gene crossover, P c min represents the lower bound of the probability of gene crossover, R max Represents the maximum value of population fitness, R avg Represents the average fitness of the population, R c max Indicates the maximum fitness among the parents participating in the crossover, c0 is 9.903438,

[0100] Gene crossover is achieved by two-point crossover, see Figure 2, on the chromosomes of the two parent individuals, select two different positions, crossover point 1 and crossover point 2, as gene crossover points. The gene crossover points divide the chromosome individuals into three segments. Exchange the chromosomes of the two parent individuals between the gene crossover points and generate two offspring chromosome individuals;

[0101] The gene mutation is achieved by basic bit mutation, see Figure 3 , randomly select part of the chromosome of the chromosome individual for mutation, and its gene X2 gene mutates to X2″. After the gene mutation, the gene X2″ of the offspring will replace the original gene X2. Randomly mutate part of the genes in the chromosome individual to obtain the chromosome individual after gene crossover, the gene mutation probability formula is:

[0102]

[0103] Among them, P m max represents the upper bound of the probability of gene mutation, P m min represents the lower bound of the probability of gene mutation, R max Represents the maximum value of population fitness, R avg Represents the average fitness of the population, R m Represents the fitness of the individual with mutated chromosome, c0 is 9.903438.

[0104] The termination condition in S5 is that when the fitness of the elite individual and the fitness of the population no longer increase, or the number of gene iterations t reaches the preset number of iterations T, the elite individual is decoded and denormalized, and the denormalized parameters are output as the optimal geometric parameters of the tap.

[0105] Obviously, the embodiments of the present invention disclosed above are only used to help illustrate the present invention. The embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. According to the content of this specification, many modifications and changes can be made. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. It is not necessary and impossible to exhaustively list all implementation methods here.

Claims

1. A method for optimizing tap parameters, characterized in that: The specific steps are as follows: S1: Determine the value range of the geometric parameters of the model of the tap to be optimized, and perform data processing on the geometric parameters, wherein the geometric parameters include the tap rake angle γ p 、Tap back angle α p and number of teeth of chip cone N; S2: obtaining, by orthogonal test, fitting equations of the geometric parameters after data processing in the tap model and the corresponding performance parameters, wherein the corresponding performance parameters include cutting force F, cutting temperature T and tool wear rate μ; S3: Comprehensively analyze the performance parameters generated by the geometric parameters after data processing and assign weights; S4: Optimize the geometric parameters after data processing and the performance parameters after weight allocation through multi-island genetic algorithm; The processed geometric parameters are encoded into genetic data, and chromosome individuals are randomly generated to form n2 populations; Substitute the gene data carried by the chromosome individuals into the fitting equation to obtain the corresponding performance parameters; Calculate the fitness of the obtained performance parameters; Perform traditional genetic operations on chromosome individuals in each population according to their fitness; Randomly select e chromosome individuals for population migration to generate n2 new populations; Selecting elite individuals in the new population, wherein the elite individuals are chromosome individuals with the greatest fitness; S5: Determine whether the selected elite individual meets the termination condition. If the selected elite individual does not meet the termination condition, repeat S4 to perform gene iteration. If the selected elite individual meets the termination condition, decode the selected elite individual and perform denormalization processing. S6: Output the denormalized parameters as the optimal geometric parameters of the tap.

2. A tap parameter optimization method according to claim 1, characterized in that: The value range of the geometric parameters of the tap model to be optimized in S1 is: Among them, γ pmin For γ p The minimum value of the range, γ pmax For γ p The maximum value of the range, α pmin is α p The minimum value of the range, α pmax is α p The maximum value of the range, N min is the minimum value of the range of N values, N max The maximum value of the range of N values.

3. A tap parameter optimization method according to claim 1, characterized in that: The method of data processing of the geometric parameters in S1 is dimensionless normalization processing, and the normalization processing formula is: Where x represents any geometric parameter to be normalized, x new represents the result of normalization, x max Indicates the maximum value of the range of x values, x min Indicates the minimum value of the range, where x new The value range is [0, 1], and the precision is set to 10 -3 .

4. A tap parameter optimization method according to claim 1, characterized in that: The fitting equations of the geometric parameters after data processing described in S2 and the corresponding performance parameters are: Among them, g1(γ p ,α p ,N) is the fitting equation corresponding to the cutting force F, g2(γ p ,α p ,N) is the fitting equation corresponding to the tool wear rate μ, g3(γ p ,α p ,N) is the fitting equation corresponding to the cutting temperature T.

5. The method for optimizing tap parameters according to claim 1, characterized in that: The weight distribution method in S3 is implemented by a weight matrix, and the weight matrix is: B=[A1 A2 A3], Among them, A1 is the weight distribution of cutting force, A2 is the weight distribution of cutting temperature, A3 is the weight distribution of tool wear rate, and A1+A2+A3=1.

6. A tap parameter optimization method according to claim 5, characterized in that: The performance index function generated by the weight matrix is: W=A1F+A2T+A3μ.

7. The method for optimizing tap parameters according to claim 1, characterized in that: The genetic data encoding method in S4 is to convert the tap front angle γ p 、Tap back angle α p , the number of teeth N of the chip cone is encoded as X1, X2, X3, then each chromosome individual is {X1, X2, X3}, where the values ​​of X1, X2 and X3 are between [0, 1], and the precision is set to 10 -3 , the number of populations is n2, and the number of chromosome individuals in each population is n1.

8. The method for optimizing tap parameters according to claim 6, characterized in that: The fitness described in S4 is calculated by the fitness function, which is: Among them, R is the fitness and W is the performance indicator function.

9. The method for optimizing tap parameters according to claim 5, characterized in that: The traditional genetic operations described in S4 include gene selection, gene crossover and gene mutation. The gene selection is implemented by roulette selection. The probability of a chromosome individual in the population being selected is proportional to the fitness function of the chromosome individual. The selection formula is: Among them, P i is the probability of the i-th gene being selected, R i is the fitness of the i-th chromosome individual; Sort the chromosome individuals of each generation in each population in reverse order by adaptation: The corresponding chromosome individual sequence is Let chromosome individuals G1 and G2, G3 and and Crossover is performed according to the gene crossover probability. The gene crossover probability formula is: Among them, P c max represents the upper bound of the probability of gene crossover, P c min represents the lower bound of the probability of gene crossover, R max Represents the maximum value of population fitness, R avg Represents the average fitness of the population, R c max Indicates the maximum fitness among the parents participating in the crossover, c0 is 9.903438, Gene crossover is achieved by two-point crossover, where crossover points are set between X1 and X2, and between X2 and X3, and the chromosomes of two chromosome individuals are exchanged between the two set crossover points. The gene mutation is achieved by basic bit mutation, and the gene mutation probability formula is: Among them, P m max represents the upper bound of the probability of gene mutation, P m min represents the lower bound of the probability of gene mutation, R max Represents the maximum value of population fitness, R avg Represents the average fitness of the population, R m Represents the fitness of the individual with mutated chromosome, c0 is 9.903438.

10. The method for optimizing tap parameters according to claim 1, characterized in that: The termination condition in S5 is that when the fitness of the elite individual and the fitness of the population no longer increase, or the number of gene iterations t reaches the preset number of iterations T, the elite individual is decoded and denormalized, and the denormalized parameters are output as the optimal geometric parameters of the tap.

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