Hobbing process parameter optimization method and system based on multi-target whale optimization algorithm

Abstract

The invention relates to the technical field of gear hobbing, in particular to a hobbing process parameter optimization method and system. The method comprises the steps that S1, the number k of hob heads, the diameter d1 of the hob, the rotating speed n of a main shaft and the feeding amount fz serve as technological parameter variables to be optimized; constructing a target function by taking the minimum processing energy consumption, the minimum processing error and the minimum processing cost as optimization targets; a gear hobbing multi-objective optimization function model is constructed; s2, adopting a multi-objective whale optimization algorithm to perform iterative optimization on to-be-optimized process parameter variables input in the hobbing multi-objective optimization function model to obtain a plurality of process parameter solution sets; and S3, evaluating and sorting all the obtained process parameter solution sets through an approximate ideal solution sorting method based on entropy weight to obtain an optimal process parameter solution. According to the method, multiple target factors of the actual hobbing process production link are considered in multiple dimensions, the production efficiency can be improved and the production cost can be reduced on the premise that the machining quality is guaranteed, and the method has high actual production guidance value.

Description

Technical Field

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

[0002] The goal of carbon neutrality has put forward higher requirements for gear processing. In the process of gear hobbing, unreasonable process parameters will seriously affect the processing energy consumption, processing errors and processing costs of gears. 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 speed, excessive feed rate 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 processed surface, thereby affecting the processing error. In addition, if the process parameters are not set properly, it will seriously affect the material cost, maintenance cost or tool replacement cost, etc., which will increase the overall production cost.

[0003] Therefore, the optimization of gear hobbing process parameters has become a key consideration in gear processing. In actual production, the process parameters of gear hobbing are mostly determined by process personnel based on experience, which is highly subjective. There are some optimization methods in the prior art that optimize the gear hobbing process parameters, but there is a lack of methods that simultaneously consider the processing energy consumption, processing error and processing cost factors. In addition, the scientificity of the optimization method is poor, and the optimization effect is small, which makes it impossible for factories to directly apply it to meet production needs. Summary of the invention

[0004] The present invention provides a method and system for optimizing gear hobbing process parameters based on a multi-objective whale optimization algorithm, which considers multiple target factors of the actual gear hobbing process production link in multiple dimensions, 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 purpose, the technical means adopted by the present invention are as follows: a method for optimizing gear hobbing process parameters based on a multi-objective whale optimization algorithm, the method comprising:

[0006] S1, based on the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z as process parameter variables to be optimized; construct objective functions with minimum machining energy consumption model, minimum machining error model and minimum machining cost model as optimization targets; 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 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 optimize 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 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, and 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, taking the objective function as the fitness function, calculating the fitness and position vector 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, using an 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 solution 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 encirclement and 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 in the current solution space area, which makes it possible to perform refined search in known good areas. |A|>1, explore new solution space areas, which helps avoid falling into 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, using the number of hob heads k, hob diameter d1, spindle speed n and feed rate f in the process parameter solution set z As evaluation samples; taking optimization objectives as evaluation indicators; obtaining characteristic indicators and constructing a judgment matrix;

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

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

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

[0032] S35, constructing a weighted normalized matrix of evaluation indicators;

[0033] S36. Determine the positive ideal solution Z using the TOPSIS method 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 All process parameter solution sets are sorted, and the solution in the first-rank process parameter solution set is selected 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-order 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 the minimum processing energy consumption, the minimum processing error, and the minimum processing cost as optimization objectives, to construct 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 iterate and optimize through the multi-objective whale optimization algorithm to obtain multiple process parameter solution sets;

[0041] The evaluation and decision-making 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 also provides an electronic device, including 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 a gear hobbing process parameter optimization method based on a multi-objective whale optimization algorithm as described above.

[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 multi-dimensional comprehensive considerations on processing energy consumption, processing error and processing cost, utilizes the multi-objective whale optimization algorithm's better global optimization capability and adaptive adjustment strategy to realize iterative optimization of process parameters, and utilizes the approximate ideal solution sorting method based on entropy weight 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 drawings required for use in the embodiments will be briefly introduced below. 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 paying creative work.

[0049] Figure 1 This is a 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 technical solutions of the embodiments of the present invention are explained and described below in conjunction with the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, not all. Based on the embodiments in the implementation mode, other embodiments obtained by those skilled in the art without creative work are all within the protection scope of the present invention.

[0052] The terms "first", "second", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any variation thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0053] Embodiment 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 process parameter variables to be optimized; construct objective functions with minimum machining energy consumption, minimum machining error and minimum machining cost as optimization targets; construct a multi-objective optimization function model for gear hobbing;

[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; 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 wear cost, C3 is the machine tool wear 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 obtained four 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, taking the objective function as the fitness function, calculating the fitness and position vector 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 encirclement and 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, where 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 line; l is a random number between [-1,1];

[0069] Step 252, set the coefficient variable A. When |A| < 1, select the best individual to use the shrinking and surrounding predation strategy to update the individual and calculate the individual fitness (such as formula 3); when |A| > 1, select the random walk predation strategy to update the individual and calculate the individual fitness (such as 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, is a randomly selected process parameter;

[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 solution 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 sorted by the approximate ideal solution sorting 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, the hob diameter d1, the spindle speed n and the 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 m evaluation samples and n evaluation indexes 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 Standardize the process;

[0087]

[0088] S33, solving 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 a weighted normalized matrix Z of evaluation indicators;

[0094] Z=W·Y=(Z ij ) m×rt (11)

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

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

[0097]

[0098] S36. Determine the positive ideal solution Z using the TOPSIS method 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 embodiment, taking the processing of gears in a certain enterprise as an example, the gear-related parameters are as follows: the module is 2.5mm, the number of teeth is 60, the pressure angle is 20°, the tooth thickness is 45mm, and the number of hob heads is 2mm. Using the above method, the optimized process parameter solution set is shown in Table 1, and the process parameter solution after scoring and deciding the optimized process parameter solution set using the approximate ideal solution sorting method based on entropy weight is 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] It can be seen from Tables 1 and 2 that 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] Embodiment 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, the minimum processing error and the minimum processing cost as the optimization objectives, and the multi-objective optimization function model of 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 iterate and optimize through the multi-objective whale optimization algorithm to obtain multiple process parameter solution sets;

[0118] The evaluation and decision-making 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] Embodiment 3:

[0121] The present invention also provides an electronic device, including 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 various parts of the entire electronic device, and executes 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, and PLA. The processor can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application program, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented by a single chip.

[0122] The memory may include RAM or ROM. Optionally, the memory includes a non-transitory computer-readable medium. The memory may 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 descriptions are only preferred embodiments disclosed in this application and descriptions of the technical principles used. Those skilled in the art should understand that the scope of protection involved in this disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this disclosure (but not limited to) to form a technical solution.

[0124] In addition, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, 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, based on the number of hob heads k, hob diameter d1, spindle speed n, feed rate f z as process parameter variables to be optimized; construct objective functions with minimum machining energy consumption model, minimum machining error model and minimum machining cost model as optimization targets; 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 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 optimize 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 method for optimizing gear hobbing process parameters based on a 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 method for optimizing gear hobbing process parameters based on a multi-objective whale optimization algorithm according to claim 2 is characterized in that: In S2, the specific steps of iteratively optimizing the 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, taking the objective function as the fitness function, calculating the fitness and position vector 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, using an 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 solution 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 method for optimizing gear hobbing process parameters based on a 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 encirclement and 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 method for optimizing gear hobbing process parameters based on a 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 best individual is selected to use the shrinking and surrounding predation strategy 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 method for optimizing gear hobbing process parameters based on a multi-objective whale optimization algorithm according to claim 5 is characterized in that: In S3, the specific steps of obtaining the optimal process parameter solution include: S31, using the number of hob heads k, hob diameter d1, spindle speed n and feed rate f in the process parameter solution set z As evaluation samples; taking optimization objectives as evaluation indicators; obtaining characteristic indicators and constructing a judgment matrix; S32, performing standardization processing on the characteristic index; S33, solving the proportion of each characteristic index in each sample, that is, the characteristic index variation; S34, calculating the information entropy of each characteristic index and determining the weight of each characteristic index; S35, constructing a weighted normalized matrix of evaluation indicators; S36. Determine the positive ideal solution Z using the TOPSIS method 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 All process parameter solution sets are sorted, and the solution in the first-rank process parameter solution set is selected as the optimal process parameter solution.

7. The method for optimizing gear hobbing process parameters based on a 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 m evaluation samples and n evaluation indexes 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 method for optimizing gear hobbing process parameters based on a multi-objective whale optimization algorithm according to claim 6 is characterized in that: In S37, selecting the solution in the first-ranked 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, the minimum processing error and the minimum processing cost as the optimization objectives, and the multi-objective optimization function model of 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 iterate and optimize through the multi-objective whale optimization algorithm to obtain multiple process parameter solution sets; The evaluation and decision-making 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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