A gear hobbing process parameter optimization method and system based on multi-objective whale optimization algorithm

Abstract

The present invention relates to the field of gear hobbing technology, and in particular to a gear hobbing process parameter optimization method and system. The method comprises: S1, the number of hob heads k, the hob diameter d1, the spindle speed n, the feed rate f z As the process parameter variables to be optimized; construct the objective function with the minimum processing energy consumption, minimum processing error and minimum processing cost as the optimization objectives; construct a multi-objective optimization function model for gear hobbing; S2, use the multi-objective whale optimization algorithm to iteratively optimize the process parameter variables to be optimized input in the multi-objective optimization function model for gear hobbing, and obtain multiple process parameter solution sets; S3, evaluate and sort all the obtained process parameter solution sets through the approximate ideal solution sorting method based on entropy weight, and obtain the optimal process parameter solution. The present invention considers multiple target factors of the actual gear hobbing process production link in multiple dimensions, can improve production efficiency and reduce production costs under the premise of ensuring processing quality, and has high actual production guidance value.

Description

Technical Field

[0001] The present invention relates to the technical field of gear hobbing processing, and in particular to a gear hobbing process parameter optimization method and system. Background Art

[0002] The goal of carbon neutrality places higher demands on gear processing. During gear hobbing, unreasonable process parameters will seriously affect the gear processing energy consumption, processing errors, and processing costs. For example, in gear hobbing, the rotation of the tool, the rotation of the workpiece, and the meshing movement between them all consume energy. Unreasonable process parameters, such as excessive cutting speeds, excessive feed rates, or inappropriate hob parameters, may lead to increased energy consumption of each axis of the machine tool and may also affect the roughness of the machined surface, thereby affecting the processing errors. In addition, if the process parameters are set improperly, it will seriously affect the material cost, maintenance cost, or tool replacement cost, etc., causing the overall production cost to increase.

[0003] Therefore, optimizing gear hobbing process parameters has become a key consideration in gear manufacturing. In actual production, gear hobbing process parameters are mostly determined by process personnel based on experience, which is highly subjective. Several optimization methods exist in the prior art for optimizing gear hobbing process parameters, but these methods lack a comprehensive approach that considers factors such as machining energy consumption, machining errors, and machining costs. Furthermore, these optimization methods are often less scientific and have limited effectiveness, making them infeasible for factories to directly apply to meet production needs. Summary of the Invention

[0004] The present invention provides a gear hobbing process parameter optimization method and system based on a multi-objective whale optimization algorithm, which considers multiple target factors of the actual gear hobbing process production link in multiple dimensions. It can improve production efficiency and reduce production costs while ensuring processing quality, and has high actual production guidance value.

[0005] In order to achieve the above-mentioned purpose, the technical means adopted by the present invention are as follows: a gear hobbing process parameter optimization method based on a multi-objective whale optimization algorithm, the method comprising:

[0006] S1, the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z as process parameter variables to be optimized; construct an objective function with the minimum machining energy consumption model, minimum machining error model and minimum machining cost model as optimization targets; and construct a multi-objective optimization function model for gear hobbing;

[0007] The processing energy consumption model E=E s +E e +E c , where E s is the energy consumption during standby period, E e is the energy consumption during the no-load period, Ec is the energy consumption during the cutting period;

[0008] The machining error model Q=w1δ x +w2δ y , where δ x is the tooth direction error, δ y is the tooth profile error, w1 and w2 are weight coefficients;

[0009] The processing cost model C=C1+C2+C3+C4+C5, where C1 is the blank cost, C2 is the tool wear cost, C3 is the machine tool wear cost, C4 is the labor cost, and C5 is the energy cost;

[0010] S2. Using a multi-objective whale optimization algorithm, iteratively optimizing the process parameter variables to be optimized input into the multi-objective optimization function model of gear hobbing to obtain multiple process parameter solution sets;

[0011] S3. Evaluate and sort all the obtained process parameter solution sets through the approximate ideal solution sorting method based on entropy weight to obtain the optimal process parameter solution.

[0012] As a preferred embodiment of the present invention, S1 further includes a constraint setting step, specifically: based on the process parameter value range, tool manufacturer, machine tool performance requirements and processing conditions, variable constraints are proposed for the multi-objective optimization function model of gear hobbing. The variable constraints are specifically:

[0013]

[0014] Among them, R a is the surface roughness, and r is the radius of the hob.

[0015] As a preferred embodiment of the present invention, in S2, the specific steps of iteratively optimizing the process parameters using the multi-objective whale optimization algorithm include:

[0016] S21. Initialize the whale population size N, the number of targets obj, the number of iterations t, the maximum number of iterations T, the crossover probability pc, the mutation probability pm, the upper limit ub and the lower limit lb of the search space;

[0017] S22, using the objective function as the fitness function, and calculating the fitness and position vectors of all individuals;

[0018] S23, determine the non-dominated solutions in the population and save them into the Pareto solution set;

[0019] S24, calculating the crowding degree and selecting an optimal individual according to the crowding degree;

[0020] S25. Use optimization search mechanism to optimize the algorithm;

[0021] S26, determine the new non-dominated solution in the population and save it to the Pareto solution set, and remove the dominated solutions in the Pareto solution set;

[0022] S27. Determine whether the number of iterations is greater than a set threshold. If so, stop the iteration and obtain the process parameter solution set. Otherwise, re-execute step S24.

[0023] As a preferred embodiment of the present invention, in step S25, the execution steps of the optimization search mechanism include:

[0024] S251. Generate a random number in the range of (0,1). If the random number is less than 0.5, use the surround contraction strategy to update the individual position; if the random number is greater than or equal to 0.5, use the spiral bubble predation strategy to update the individual position.

[0025] As a preferred embodiment of the present invention, step S25 includes step S252, setting the coefficient variable A. When |A| is less than 1, the shrinking and surrounding predation strategy is selected according to the best individual to update the individual and calculate the individual fitness; when |A| is greater than 1, the random walk predation strategy is selected to update the individual and calculate the individual fitness.

[0026] When |A| < 1, at this stage, local search is performed within the current solution space region, which allows for a refined search within known good regions. When |A| > 1, new regions of the solution space are explored, which helps avoid being trapped in local optimal solutions.

[0027] As a preferred embodiment of the present invention, in S3, the specific steps of obtaining the optimal process parameter solution include:

[0028] S31, the number of hob heads k, hob diameter d1, spindle speed n and feed rate f in the process parameter solution set z As the evaluation sample; taking the optimization target as the evaluation index; obtaining the characteristic index and constructing the judgment matrix;

[0029] S32, performing standardization processing on the characteristic indicators;

[0030] S33, calculating the proportion of each characteristic index in each sample, that is, the characteristic index variation;

[0031] S34, calculating the information entropy of each characteristic indicator and determining the weight of each characteristic indicator;

[0032] S35. Construct a weighted normalized matrix of evaluation indicators;

[0033] S36. Use TOPSIS method to determine the positive ideal solution Z j + and negative ideal solution Z j -, calculate the distance D between the positive and negative ideal solutions i + 、D i - ;

[0034] S37, calculate relative closeness S i , according to the relative closeness S i Sort all process parameter solution sets and select the solution in the first-rank process parameter solution set as the optimal process parameter solution.

[0035] As a preferred embodiment of the present invention, in S31, obtaining characteristic indicators and constructing a judgment matrix includes: defining the jth indicator of the i-th object among the m evaluation samples and the n evaluation indicators as the characteristic indicator X ij , construct the judgment matrix X=(X ij )m×n(i=1,2,···,m; j=1,2,···,n).

[0036] As a preferred embodiment of the present invention, in S37, selecting the solution in the first-rank process parameter solution set as the optimal process parameter solution includes:

[0037]

[0038] Among them, S i is the relative closeness, D i + is the positive ideal solution distance, D i - is the negative ideal solution distance.

[0039] On the other hand, the present invention also provides a gear hobbing process parameter optimization system based on a multi-objective whale optimization algorithm, the system comprising: a multi-objective optimization function model construction module for constructing an objective function with the number of hob heads k, the hob diameter d1, the spindle speed n, and the feed rate fz as process parameter variables to be optimized, and with minimum machining energy consumption, minimum machining error, and minimum machining cost as optimization objectives, thereby constructing a gear hobbing multi-objective optimization function model;

[0040] The whale optimization module is used to input the process parameter variables to be optimized into the multi-objective optimization function model of gear hobbing, and iteratively search for the optimal solution through the multi-objective whale optimization algorithm to obtain multiple process parameter solution sets;

[0041] The evaluation and decision module is used to evaluate and sort all the obtained process parameter solution sets through the approximate ideal solution sorting method based on entropy weight to obtain the optimal process parameter solution.

[0042] On the other hand, the present invention further provides an electronic device, comprising a processor and a memory;

[0043] The processor is connected to the memory;

[0044] The memory is used to store executable program code;

[0045] The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the aforementioned gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm.

[0046] In summary, the present invention has the following beneficial effects:

[0047] Based on the process parameters to be optimized, the present invention establishes a multi-objective optimization function model for gear hobbing processing, conducts a multi-dimensional comprehensive consideration of processing energy consumption, processing error and processing cost, utilizes the multi-objective whale optimization algorithm's better global optimization ability and adaptive adjustment strategy to realize iterative optimization of process parameters, and utilizes the entropy-based approximate ideal solution sorting method to score and decide on the optimized process parameter solution set, guiding enterprises to select appropriate process parameter solutions, which can help enterprises achieve the goal of reducing costs and increasing efficiency on the manufacturing cost side, and has strong practicality and wide applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of this specification, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0049] Figure 1 This is the flow chart of the gear hobbing process parameter optimization method;

[0050] Figure 2 This is the architecture diagram of the gear hobbing process parameter optimization system. DETAILED DESCRIPTION

[0051] The following is an explanation and description of the technical solutions of the embodiments of the present invention in conjunction with the drawings of the embodiments of the present invention. However, the following embodiments are only preferred embodiments of the present invention and are not exhaustive. Based on the embodiments in the implementation manner, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0052] Throughout this specification, the claims, and the accompanying drawings, the terms "first," "second," and so forth are used to distinguish between different items, not to describe a particular order. Furthermore, the term "comprises" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may include other steps or elements inherent to the process, method, product, or apparatus.

[0053] Example 1:

[0054] like Figure 1 As shown, first, the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z As the process parameter variables to be optimized; the objective function is constructed with the minimum processing energy consumption, minimum processing error and minimum processing cost as the optimization goals; and a multi-objective optimization function model for gear hobbing is constructed;

[0055] Among them, processing energy consumption E=E s +E e +E c , processing error Q = w1δ x +w2δ y , processing cost C=C1+C2+C3+C4+C5;

[0056] Among them, E s is the energy consumption during standby period, E e is the energy consumption during the no-load period, E c is the energy consumption during cutting; δ x is the tooth direction error, δ y is the tooth profile error, w1 and w2 are weight coefficients; C1 is the blank cost, C2 is the tool loss cost, C3 is the machine tool loss cost, C4 is the labor cost, and C5 is the energy cost.

[0057] At the same time, objective processing conditions are used as constraints. Specifically, variable constraints are proposed for the multi-objective optimization function model of gear hobbing based on the process parameter value range, tool manufacturer, machine tool performance requirements, and processing conditions. The variable constraints are specifically as follows:

[0058]

[0059] Among them, R a is the surface roughness, and r is the radius of the hob.

[0060] After the multi-objective optimization function model of gear hobbing is obtained, the number of hob heads k, hob diameter d1, spindle speed n, feed rate f are obtained. zThe original values ​​of the four process parameter variables to be optimized form the original data set, which is input into the multi-objective optimization function model of gear hobbing, and then the multi-objective whale optimization algorithm is used for iterative optimization.

[0061] The specific steps are as follows: generating a whale population based on the four obtained process parameter variables to be optimized;

[0062] S21 initializes the whale population size N, the number of targets obj, the number of iterations t, the maximum number of iterations T, the crossover probability pc, the mutation probability pm, the upper limit ub and the lower limit lb of the search space;

[0063] S22, using the objective function as the fitness function, and calculating the fitness and position vectors of all individuals;

[0064] S23, determine the non-dominated solutions in the population and save them into the Pareto solution set;

[0065] S24, calculating the crowding degree and selecting an optimal individual according to the crowding degree;

[0066] S251. Generate a random number in the range of (0,1). If the random number is less than 0.5, use the surround contraction strategy to update the individual position and go to S252. If the random number is greater than or equal to 0.5, use the spiral bubble predation strategy to update the individual position and calculate the fitness. The fitness calculation method is:

[0067]

[0068] in Indicates the distance between the current process parameters and the optimal process parameters; b is a constant that defines the spiral; l is a random number between [-1,1];

[0069] Step 252: Set the coefficient variable A. When |A| is less than 1, select the best individual to use the shrinking and surrounding predation strategy to update the individual and calculate the individual fitness (as shown in Formula 3). When |A| is greater than 1, select the random walk predation strategy to update the individual and calculate the individual fitness (as shown in Formula 5) and go to S26.

[0070]

[0071]

[0072]

[0073]

[0074] Where t represents the number of iterations; A and C are coefficient variables; is the position vector of the current optimal process parameters; is the position vector of the current process parameter solution; is the element-by-element multiplication, are randomly selected process parameters;

[0075] The calculation formulas for coefficient variables A and C are as follows:

[0076] A=2a·r1-a (6)

[0077] C=2r2 (7)

[0078] Where a is a coefficient that decays from 2 to 0 from the beginning of the iteration; r1 and r2 are random variables between [0,1];

[0079] S26, determine the new non-dominated solution in the population and save it to the Pareto solution set, and remove the dominated solutions in the Pareto solution set;

[0080] S27. Determine whether the number of iterations is greater than a set threshold. If so, stop the iteration and obtain the process parameter solution set. Otherwise, re-execute step S24.

[0081] In this embodiment, when the number of iterations reaches the set threshold requirement, multiple process parameter solution sets are successfully obtained, each of which contains four target solutions.

[0082] Then, all the obtained process parameter solution sets are evaluated and ranked by the approximate ideal solution ranking method based on entropy weight, and the optimal process parameter solution is obtained according to the actual production needs of the enterprise.

[0083] The specific steps to obtain the optimal process parameter solution include:

[0084] S31, using the number of hob heads k, hob diameter d1, spindle speed n and feed rate fz in the process parameter solution set as evaluation samples; and using the optimization target as the evaluation index;

[0085] The jth index of the i-th object among the m evaluation samples and n evaluation indicators is defined as the feature index X ij , construct the judgment matrix X=(X ij )m×n(i=1,2,···,m; j=1,2,···,n);

[0086] S32, the characteristic index X ij Conduct standardization processing;

[0087]

[0088] S33, solve each characteristic index X ij The proportion in each sample, that is, the characteristic index variation P ij ;

[0089]

[0090] S34. Calculate each characteristic index X ij Information entropy E j And determine each characteristic index X ij The weight W j ;

[0091]

[0092]

[0093] S35, constructing the evaluation index weighted normalized matrix Z;

[0094] Z=W·Y=(Z ij ) m×rt (11)

[0095] Among them, Y is the matrix of the normalized original data;

[0096] Specifically, Y=(Y ij )m×n(i=1,2,···,m; j=1,2,···,n)

[0097]

[0098] S36. Use TOPSIS method to determine the positive ideal solution Z j + and negative ideal solution Z j - , calculate the distance D between the positive and negative ideal solutions i + 、D i - ;

[0099]

[0100]

[0101]

[0102]

[0103] S37, calculate relative closeness S i , according to the relative closeness S i Sort all process parameter solution sets and select the solution in the first-rank process parameter solution set as the optimal process parameter solution;

[0104]

[0105] According to the above method, the optimal process parameter solution for gear hobbing can be obtained.

[0106] In this example, using a company's gear processing as an example, the gear parameters are as follows: module 2.5mm, number of teeth 60, pressure angle 20°, tooth thickness 45mm, and number of hob heads 2mm. Using the above method, the optimized process parameter solution set is shown in Table 1. The process parameter solution is scored and determined using the entropy-based approach to ideal solution ranking method, as shown in Table 2.

[0107] Table 1 Optimized process parameter solution set

[0108]

[0109]

[0110] Table 2 Process parameters after decision

[0111]

[0112] Enterprises can refer to the above data and select a process parameter solution that meets the requirements based on actual production needs.

[0113] As can be seen from Tables 1 and 2, in this embodiment, the score of the J3 solution set ranks first, so the feed rate f in the J3 solution set is selected. z =1.269mm / r; spindle speed n = 918.575r / min; hob diameter d1 = 82mm; number of hob heads k = 2. The four solutions are taken as the optimal process parameter solutions to form a process parameter solution.

[0114] Example 2:

[0115] The present invention also provides a gear hobbing process parameter optimization system based on a multi-objective whale optimization algorithm, characterized in that the system comprises:

[0116] Multi-objective optimization function model building module is used to calculate the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z As the process parameter variables to be optimized, the objective function is constructed with the minimum processing energy consumption, minimum processing error and minimum processing cost as the optimization goals, and a multi-objective optimization function model for gear hobbing is constructed;

[0117] The whale optimization module is used to input the process parameter variables to be optimized into the multi-objective optimization function model of gear hobbing, and iteratively search for the optimal solution through the multi-objective whale optimization algorithm to obtain multiple process parameter solution sets;

[0118] The evaluation and decision module is used to evaluate and sort all the obtained process parameter solution sets through the approximate ideal solution sorting method based on entropy weight to obtain the optimal process parameter solution.

[0119] Among them, the multi-objective optimization function model construction module is connected with the whale optimization module and the evaluation and decision-making module in sequence to realize the above-mentioned gear hobbing process parameter optimization method.

[0120] Example 3:

[0121] The present invention also provides an electronic device, comprising a processor and a memory; the processor is connected to the memory; the processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and performs various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of DSP, FPGA, PLA. The processor can integrate one or a combination of CPU, GPU and modem, etc. Among them, the CPU mainly processes the operating system, user interface and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented separately through a chip.

[0122] The memory may include RAM or ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory may also be at least one storage device located away from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module and an application. The processor may be used to call the application stored in the memory and execute the method in one or more of the above-mentioned embodiments.

[0123] The above description is merely an illustration of the preferred embodiments disclosed in this application and the technical principles employed. Those skilled in the art should understand that the scope of protection provided by this disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0124] In addition, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of this disclosure. Certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination.

Claims

1. A gear hobbing process parameter optimization method based on a multi-objective whale optimization algorithm, characterized in that: The method includes: S1, the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z as process parameter variables to be optimized; construct an objective function with the minimum machining energy consumption model, minimum machining error model and minimum machining cost model as optimization targets; and construct a multi-objective optimization function model for gear hobbing; The processing energy consumption model E=E s +E e +E c , where E s is the energy consumption during standby period, E e is the energy consumption during the no-load period, E c is the energy consumption during the cutting period; The machining error model Q=w1δ x +w2δ y , where δ x is the tooth direction error, δ y is the tooth profile error, w1 and w2 are weight coefficients; The processing cost model C=C1+C2+C3+C4+C5, where C1 is the blank cost, C2 is the tool wear cost, C3 is the machine tool wear cost, C4 is the labor cost, and C5 is the energy cost; S2. Using a multi-objective whale optimization algorithm, iteratively optimizing the process parameter variables to be optimized input into the multi-objective optimization function model of gear hobbing to obtain multiple process parameter solution sets; S3. Evaluate and sort all the obtained process parameter solution sets through the approximate ideal solution sorting method based on entropy weight to obtain the optimal process parameter solution.

2. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 1 is characterized in that: S1 also includes a constraint setting step, specifically: based on the process parameter value range, tool manufacturer, machine tool performance requirements and processing conditions, variable constraints are proposed for the multi-objective optimization function model of gear hobbing. The variable constraints are specifically: Among them, R a is the surface roughness, and r is the radius of the hob.

3. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 2 is characterized in that: In S2, the specific steps of iteratively optimizing process parameters using the multi-objective whale optimization algorithm include: S21. Initialize the whale population size N, the number of targets obj, the number of iterations t, the maximum number of iterations T, the crossover probability pc, the mutation probability pm, the upper limit ub and the lower limit lb of the search space; S22, using the objective function as the fitness function, and calculating the fitness and position vectors of all individuals; S23, determine the non-dominated solutions in the population and save them into the Pareto solution set; S24, calculating the crowding degree and selecting an optimal individual according to the crowding degree; S25. Use optimization search mechanism to optimize the algorithm; S26, determine the new non-dominated solution in the population and save it to the Pareto solution set, and remove the dominated solutions in the Pareto solution set; S27. Determine whether the number of iterations is greater than a set threshold. If so, stop the iteration and obtain the process parameter solution set. Otherwise, re-execute step S24.

4. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 3 is characterized in that: In step S25, the execution steps of the optimization search mechanism include: S251. Generate a random number in the range of (0,1). If the random number is less than 0.5, use the surround contraction strategy to update the individual position; if the random number is greater than or equal to 0.5, use the spiral bubble predation strategy to update the individual position.

5. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 4 is characterized in that: Step S25 includes step S252, setting the coefficient variable A. When |A| is less than 1, the shrinking and surrounding predation strategy is selected according to the best individual to update the individual and calculate the individual fitness; when |A| is greater than 1, the random walk predation strategy is selected to update the individual and calculate the individual fitness.

6. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 5 is characterized in that: In S3, the specific steps for obtaining the optimal process parameter solution include: S31, the number of hob heads k, hob diameter d1, spindle speed n and feed rate f in the process parameter solution set z As the evaluation sample; taking the optimization target as the evaluation index; obtaining the characteristic index and constructing the judgment matrix; S32, performing standardization processing on the characteristic indicators; S33, calculating the proportion of each characteristic index in each sample, that is, the characteristic index variation; S34, calculating the information entropy of each characteristic indicator and determining the weight of each characteristic indicator; S35. Construct a weighted normalized matrix of evaluation indicators; S36. Use TOPSIS method to determine the positive ideal solution Z j + and negative ideal solution Z j - , calculate the distance D between the positive and negative ideal solutions i + 、D i - ; S37, calculate relative closeness S i , according to the relative closeness S i Sort all process parameter solution sets and select the solution in the first-rank process parameter solution set as the optimal process parameter solution.

7. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 6 is characterized in that: In S31, obtaining characteristic indicators and constructing a judgment matrix include: The jth index of the i-th object among the m evaluation samples and n evaluation indicators is defined as the feature index X ij , construct the judgment matrix X=(X ij )m×n(i=1,2,···,m; j=1,2,···,n).

8. The gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm according to claim 6 is characterized in that: In S37, selecting the solution in the first-rank process parameter solution set as the optimal process parameter solution includes: Among them, S i is the relative closeness, D i + is the positive ideal solution distance, D i - is the negative ideal solution distance.

9. A gear hobbing process parameter optimization system based on a multi-objective whale optimization algorithm, characterized in that: The system includes: Multi-objective optimization function model building module is used to calculate the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z As the process parameter variables to be optimized, the objective function is constructed with the minimum processing energy consumption, minimum processing error and minimum processing cost as the optimization goals, and a multi-objective optimization function model for gear hobbing is constructed; The whale optimization module is used to input the process parameter variables to be optimized into the multi-objective optimization function model of gear hobbing, and iteratively search for the optimal solution through the multi-objective whale optimization algorithm to obtain multiple process parameter solution sets; The evaluation and decision module is used to evaluate and sort all the obtained process parameter solution sets through the approximate ideal solution sorting method based on entropy weight to obtain the optimal process parameter solution.

10. An electronic device comprising a processor and a memory; The processor is connected to the memory; The memory is used to store executable program code; The processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the gear hobbing process parameter optimization method based on the multi-objective whale optimization algorithm described in any one of claims 1-8.

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