Parameter optimization method and system for plasma spraying repair process of aero-engine wheel disc, electronic equipment, medium and product

Abstract

The invention discloses an aero-engine wheel disc plasma spraying repairing process parameter optimization method and system, electronic equipment, a medium and a product, and relates to the field of aero-engine part surface repairing, and the method comprises the steps that a plasma spraying process parameter combination is randomly generated to form a parent population; each group of plasma spraying process parameter combination is used as an individual, binary numbers after each group of plasma spraying process parameters are coded are determined as genes, binary codes formed by arranging the genes in sequence are determined as chromosomes, and the coating performance determined based on the coating performance prediction model is determined as an evaluation basis; and optimizing each plasma spraying process parameter combination based on the parent population by using an NSGA-II algorithm and a coating performance prediction model to obtain an optimal numerical value of the plasma spraying process parameter combination, thereby performing plasma spraying repair on the aero-engine wheel disc. And the parameter optimization efficiency of the plasma spraying process is improved.

Description

Technical Field

[0001] The present application relates to the field of surface repair of aircraft engine parts, and in particular to a method, system, electronic equipment, medium and product for optimizing process parameters of aircraft engine wheel disc plasma spray repair. Background Art

[0002] The impeller of an aircraft engine is in a complex environment of high temperature, high speed, vibration, high load, etc. Different wear conditions will occur in the parts that come into contact with other parts. In order to improve the service life and safety of parts, it is necessary to repair the size of the worn parts to ensure the long life requirements of the parts. In addition, the raw materials of aircraft engine impellers are of high value, the processing cycle is long, and the processing precision requirements are high. During the processing, improper processing may cause the size of the parts to be out of tolerance, or casting defects (sand holes, pores), etc., resulting in unqualified parts. If scrapped, it will bring huge economic losses and seriously delay the development and delivery progress of military aircraft engines. Therefore, appropriate maintenance technical means are required to restore the engine impeller itself and its matching characteristics to the level required by the design.

[0003] Plasma spraying is a surface repair technology suitable for aircraft engine disc repair tasks. In order to obtain a high-strength and high-thickness repair coating, the plasma spraying process parameters need to be optimized. Traditional process parameter optimization usually adopts a multi-round traversal method, which has a long optimization cycle, high optimization cost, lacks sufficient theoretical basis, lacks flexibility, and lacks a unified optimization process and standard for plasma spraying process parameter optimization. All of these have brought difficulties and challenges to the optimization of aircraft engine disc plasma spraying process parameters.

[0004] Therefore, there is an urgent need for an efficient, fast and highly applicable method for optimizing the process parameters of aircraft engine plasma spraying. Summary of the invention

[0005] The purpose of this application is to provide a method, system, electronic equipment, medium and product for optimizing the process parameters of aircraft engine wheel plasma spray repair, so as to solve the problem of low efficiency of plasma spray process parameter optimization.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for optimizing process parameters of an aircraft engine disk plasma spray repair, comprising:

[0008] Within the setting range of each plasma spraying process parameter of the aircraft engine wheel, randomly generate a plasma spraying process parameter combination to form a parent population; the plasma spraying process parameter combination includes: each plasma spraying process parameter, the square of each plasma spraying process parameter and the product of each plasma spraying process parameter;

[0009] Each group of plasma spraying process parameter combinations is regarded as an individual in a population, the binary numbers after the parameters in each group of plasma spraying process parameter combinations are encoded are determined as genes, the binary codes formed by sequentially arranging all the genes corresponding to each group of plasma spraying process parameter combinations are determined as chromosomes of the corresponding group of plasma spraying process parameter combinations, and the coating performance corresponding to each group of plasma spraying process parameter combinations determined based on the coating performance prediction model is determined as the evaluation basis; wherein the coating performance prediction model is obtained by training the back propagation neural network using the training set; the coating performance includes: hardness and wear amount;

[0010] Utilizing the NSGA-II algorithm and the coating performance prediction model, each plasma spraying process parameter combination is optimized based on the parent population to obtain an optimal value of the plasma spraying process parameter combination;

[0011] The plasma spray repair work of aircraft engine disks is controlled by using the optimal numerical control of plasma spray process parameter combination.

[0012] Optionally, the process of determining the coating performance prediction model includes:

[0013] Obtaining the plasma spray repair process parameters and corresponding coating properties of the aircraft engine wheel that has been spray repaired;

[0014] Determining a combination of plasma spraying process parameters for each aircraft engine wheel disc that has been repaired by spraying based on the plasma spraying repair process parameters for each aircraft engine wheel disc that has been repaired by spraying;

[0015] Performing a maximum absolute value normalization process on the plasma spraying process parameter combinations of each aircraft engine wheel that has been sprayed and repaired, and obtaining a corresponding processed plasma spraying process parameter combination;

[0016] The coating properties of each aircraft engine wheel that has been sprayed and repaired are normalized to the maximum and minimum values ​​to obtain the corresponding coating properties after treatment;

[0017] Constructing the training set based on the plasma spraying process parameter combinations and coating properties of each aircraft engine wheel disc that has been sprayed and repaired;

[0018] Constructing the back propagation neural network;

[0019] The back propagation neural network is trained based on the training set using the Adam optimizer to obtain the coating performance prediction model.

[0020] Optionally, the NSGA-II algorithm and the coating performance prediction model are used to optimize each plasma spraying process parameter combination based on the parent population to obtain the optimal value of the plasma spraying process parameter combination, including:

[0021] Determine the coating performance corresponding to each individual in the parent population by using the coating performance prediction model;

[0022] Determine a non-dominated hierarchy corresponding to the parent population based on coating properties corresponding to each individual in the parent population; the non-dominated hierarchy includes a plurality of individuals;

[0023] According to the order of coating performance, the individuals in the non-dominated hierarchy corresponding to the parent population are sorted, and the crowding degree of each individual in the non-dominated hierarchy corresponding to the parent population is calculated;

[0024] Based on each individual, the tournament method is used to select excellent individuals for crossover mutation to generate offspring populations, and the parent population and offspring population are merged to obtain the merged population;

[0025] Determine the coating performance corresponding to each individual in the combined population by using the coating performance prediction model;

[0026] Based on the coating properties corresponding to each individual in the merged population, determining the non-dominated level corresponding to the merged population;

[0027] According to the order of coating performance, the individuals in the non-dominated level corresponding to the merged population are sorted, and the crowding degree of each individual in the non-dominated level corresponding to the merged population is calculated;

[0028] The individuals with the top 50% of crowding degree in the non-dominated level corresponding to the merged population are used to form a new parent population, and "using the coating performance prediction model to determine the coating performance corresponding to each individual in the parent population" is returned until the maximum number of iterations is reached to obtain a non-dominated solution of multiple individuals;

[0029] Determine the coating performance corresponding to each individual non-dominated solution using the coating performance prediction model;

[0030] The non-dominated solution of the individual with the best coating performance is determined as the optimal value of the plasma spraying process parameter combination; the optimal coating performance is the maximum hardness and the minimum wear.

[0031] Optionally, each individual in the non-dominated level is not dominated by any other individual; the meaning of individual A being dominated by individual B is that the coating performance corresponding to individual B is better than the coating performance corresponding to individual A.

[0032] Optionally, the crowding degree of any current individual in the sorted non-dominated hierarchy is the sum of a first difference and a second difference of the current individual; the first difference is the difference between the coating performance of the current individual and the coating performance of the previous individual, and the second difference is the difference between the coating performance of the current individual and the coating performance of the next individual.

[0033] Optionally, the back propagation neural network comprises: an input layer, a hidden layer and an output layer; the input layer comprises 14 input units, the hidden layer comprises 128 hidden units, and the output layer comprises 2 output units;

[0034] The hidden layer and the output layer both use the ReLU activation function.

[0035] In a second aspect, the present application provides a system for optimizing process parameters of an aircraft engine wheel plasma spray repair, comprising:

[0036] An initialization module is used to randomly generate a combination of plasma spraying process parameters to form a parent population within the setting range of each plasma spraying process parameter of the aircraft engine wheel; the plasma spraying process parameter combination includes: each plasma spraying process parameter, the square of each plasma spraying process parameter and the product of each plasma spraying process parameter;

[0037] A parameter setting module is used to take each group of plasma spraying process parameter combinations as individuals in a population, determine the binary numbers after parameter encoding in each group of plasma spraying process parameter combinations as genes, determine the binary codes formed by sequentially arranging all genes corresponding to each group of plasma spraying process parameter combinations as chromosomes of the corresponding group of plasma spraying process parameter combinations, and determine the coating performance corresponding to each group of plasma spraying process parameter combinations determined based on the coating performance prediction model as an evaluation basis; wherein the coating performance prediction model is obtained by training a back propagation neural network using a training set; the coating performance includes: hardness and wear amount;

[0038] An optimization module, for optimizing each plasma spraying process parameter combination based on the parent population by using the NSGA-II algorithm and the coating performance prediction model, to obtain an optimal value of the plasma spraying process parameter combination;

[0039] A control module is used to control the plasma spray repair work of an aircraft engine wheel disc by using the optimal numerical value of the plasma spray process parameter combination.

[0040] In a third aspect, the present application provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned methods for optimizing process parameters of aircraft engine wheel plasma spray repair.

[0041] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned methods for optimizing process parameters of aircraft engine disk plasma spray repair.

[0042] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for optimizing the process parameters of the aircraft engine disk plasma spray repair.

[0043] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0044] The present application discloses a method, system, electronic equipment, medium and product for optimizing process parameters of aircraft engine wheel plasma spray repair, proposes a scheme for predicting the plasma spray repair effect by using BP neural network, adopts feature combination and normalization to process data, adopts a multi-layer neural network model and a nonlinear activation function, fully considers the coupling effect between plasma spray process parameters and the nonlinear relationship between the repair effect; combines the NSGA-II algorithm with the BP neural network, and adopts a machine learning method to improve the optimization efficiency of plasma spray process parameters and save the optimization cost; based on the coating performance prediction model, the plasma spray repair process parameter square optimization can be completed in a very short time, which can improve the optimization efficiency of plasma spray process parameters, reduce costs, and help break through the bottleneck of plasma spray technology and improve the level of plasma spray technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic flow chart of a method for optimizing process parameters of a plasma spray repair of an aircraft engine wheel disc provided in one embodiment of the present application;

[0047] Figure 2 Optimization framework diagram for the plasma spray repair process parameters of aircraft engine disks;

[0048] Figure 3 This is a schematic diagram of the back propagation neural network structure;

[0049] Figure 4 This is a schematic diagram of the output results of the first test set of the coating performance prediction model;

[0050] Figure 5 It is a schematic diagram of the output results of the second test set of the coating performance prediction model;

[0051] Figure 6 This is a schematic diagram of the NSGA-II algorithm optimization process;

[0052] Figure 7 This is a schematic diagram of chromosome crossing over;

[0053] Figure 8 This is a schematic diagram of chromosome mutation;

[0054] Fig. 9 Schematic diagram of the generation process of the new parent population;

[0055] Fig.10 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0057] The purpose of this application is to provide a method, system, electronic equipment, medium and product for optimizing the process parameters of aircraft engine wheel plasma spray repair, aiming to improve the optimization efficiency of plasma spray process parameters.

[0058] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0059] In an exemplary embodiment, Figure 1 and Figure 2 As shown, a method for optimizing process parameters of plasma spraying repair of an aircraft engine wheel disc is provided, comprising:

[0060] Step 1: Within the setting range of each plasma spraying process parameter of the aircraft engine wheel, randomly generate plasma spraying process parameter combinations to form a parent population.

[0061] The plasma spraying process parameter combination includes: each plasma spraying process parameter, the square of each plasma spraying process parameter and the product of each plasma spraying process parameter.

[0062] In fact, the plasma spraying process parameters include but are not limited to: aging, current, nozzle speed and gas flow; at this time, the plasma spraying process parameter combination includes: aging, current, nozzle speed, gas flow, the square of the aging, the square of the current, the square of the nozzle speed, the square of the gas flow and the product of each plasma spraying process parameter.

[0063] Specifically, the setting ranges of various plasma spraying process parameters are shown in Table 1.

[0064] Table 1 Plasma spraying process parameter setting range

[0065] Process parameters Value range Step Length Time limita 200-500 5 Current b 100-200 5 Nozzle speed c 50-300 5 Air flow rate 0-50 1

[0066] In addition to the above parameters, plasma spraying process parameters may also include: voltage, power and spraying distance.

[0067] Step 2: each group of plasma spraying process parameter combinations is regarded as an individual in the population, the binary numbers after parameter encoding in each group of plasma spraying process parameter combinations are determined as genes, the binary codes formed by arranging all genes corresponding to each group of plasma spraying process parameter combinations in sequence are determined as the chromosomes of the corresponding group of plasma spraying process parameter combinations, and the coating performance corresponding to each group of plasma spraying process parameter combinations determined based on the coating performance prediction model is determined as the evaluation basis.

[0068] Among them, the coating performance prediction model is obtained by training the back propagation neural network using the training set; the coating performance includes: hardness and wear amount.

[0069] As an optional implementation, the process of determining the coating performance prediction model includes:

[0070] Step 21: Obtain the plasma spray repair process parameters and corresponding coating properties of the aircraft engine wheel that has been spray repaired.

[0071] Step 22: Based on the plasma spraying repair process parameters of each aircraft engine wheel disc that has been sprayed and repaired, determine the plasma spraying process parameter combination of each aircraft engine wheel disc that has been sprayed and repaired.

[0072] Step 23: Perform maximum absolute value normalization processing on the plasma spraying process parameter combinations of each aircraft engine wheel that has been sprayed and repaired, and obtain the corresponding processed plasma spraying process parameter combinations.

[0073] Step 24: Perform maximum and minimum value normalization processing on the coating performance of each aircraft engine wheel that has been sprayed and repaired to obtain the corresponding coating performance after processing.

[0074] Step 25: Construct a training set based on the plasma spraying process parameter combinations and coating performance of each aircraft engine wheel that has been sprayed and repaired.

[0075] Step 26: Build a back-propagation neural network.

[0076] Step 27: Using the Adam optimizer, the back propagation neural network is trained based on the training set to obtain a coating performance prediction model.

[0077] As an optional implementation, Figure 3 As shown, the back propagation neural network includes: an input layer, a hidden layer and an output layer; the input layer includes 14 input units, the hidden layer includes 128 hidden units, and the output layer includes 2 output units.

[0078] Both the hidden layer and the output layer use the ReLU activation function.

[0079] After training the coating performance prediction model, the coating performance prediction model was tested using the first test set and the second test set. The results are as follows: Figure 4 and Figure 5 As shown, the prediction error is basically within 5%.

[0080] Step 3: Using the NSGA-II algorithm and the coating performance prediction model, each plasma spraying process parameter combination is optimized based on the parent population to obtain the optimal value of the plasma spraying process parameter combination.

[0081] As an optional implementation, Figure 6 As shown, step 3 includes:

[0082] Step 301: using the coating performance prediction model, determine the coating performance corresponding to each individual in the parent population.

[0083] Step 302: Based on the coating performance corresponding to each individual in the parent population, determine the non-dominated hierarchy corresponding to the parent population; the non-dominated hierarchy includes multiple individuals.

[0084] As an optional implementation, each individual in the non-dominated level is not dominated by any other individual; the meaning of individual A being dominated by individual B is that the coating performance corresponding to individual B is better than the coating performance corresponding to individual A.

[0085] Step 303: Sort the individuals in the non-dominated level corresponding to the parent population according to the order of coating performance, and calculate the crowding degree of each individual in the non-dominated level corresponding to the parent population.

[0086] As an optional implementation, the crowding degree of any current individual in the sorted non-dominated hierarchy is the sum of the first difference and the second difference of the current individual; the first difference is the difference between the coating performance of the current individual and the coating performance of the previous individual, and the second difference is the difference between the coating performance of the current individual and the coating performance of the next individual.

[0087] In fact, for individuals in the same non-dominated hierarchy, the greater the crowding degree, the better the individual.

[0088] Step 304: Based on each individual, excellent individuals are selected by using a tournament method to perform crossover mutation to generate a child population, and the parent population and the child population are merged to obtain a merged population.

[0089] Specifically, Figure 7 As shown in , crossing over is the random exchange of a segment between two chromosomes. Figure 8 As shown, mutation is a change in a random binary number on the chromosome.

[0090] Step 305: using the coating performance prediction model, determine the coating performance corresponding to each individual in the merged population.

[0091] Step 306: Based on the coating properties corresponding to each individual in the merged population, determine the non-dominated level corresponding to the merged population.

[0092] Step 307: Sort the individuals in the non-dominated level corresponding to the merged population according to the order of coating performance, and calculate the crowding degree of each individual in the non-dominated level corresponding to the merged population.

[0093] Step 308: The individuals with the top 50% of crowding degree in the non-dominated level corresponding to the merged population are used to form a new parent population, and the process returns to step 301 until the maximum number of iterations is reached to obtain a non-dominated solution of multiple individuals.

[0094] Specifically, the generation process of the new parent population is as follows: Fig. 9 shown.

[0095] Step 309: Using the coating performance prediction model, determine the coating performance corresponding to the non-dominated solution of each individual.

[0096] Step 310: Determine the non-dominated solution of the individual with the best coating performance as the optimal value of the plasma spraying process parameter combination. The best coating performance is the maximum hardness and the minimum wear amount.

[0097] Specifically, the optimal values ​​of each plasma spraying process parameter in the plasma spraying process parameter combination are shown in Table 2.

[0098] Table 2 Optimal values ​​of various plasma spraying process parameters

[0099]

[0100]

[0101] Step 4: Use the optimal numerical value of the plasma spraying process parameter combination to control the plasma spraying repair work of the aircraft engine disk.

[0102] In an exemplary embodiment, a system for optimizing process parameters of a plasma spraying repair process for an aircraft engine wheel disc is provided, comprising:

[0103] The initialization module is used to randomly generate plasma spraying process parameter combinations to form a parent population within the setting range of each plasma spraying process parameter of the aircraft engine wheel; the plasma spraying process parameters include: aging, current, nozzle speed and gas flow; the plasma spraying process parameter combinations include: each plasma spraying process parameter, the square of each plasma spraying process parameter and the product of each plasma spraying process parameter.

[0104] The parameter setting module is used to treat each group of plasma spraying process parameter combinations as individuals in a population, determine the binary numbers after the parameters in each group of plasma spraying process parameter combinations are encoded as genes, determine the binary codes formed by arranging all the genes corresponding to each group of plasma spraying process parameter combinations in sequence as chromosomes of the corresponding group of plasma spraying process parameter combinations, and determine the coating performance corresponding to each group of plasma spraying process parameter combinations determined based on the coating performance prediction model as an evaluation basis; wherein the coating performance prediction model is obtained by training a back propagation neural network using a training set; the coating performance includes: hardness and wear amount.

[0105] The optimization module is used to optimize each plasma spraying process parameter combination based on the parent population using the NSGA-II algorithm and the coating performance prediction model to obtain the optimal value of the plasma spraying process parameter combination.

[0106] A control module is used to control the plasma spray repair work of an aircraft engine wheel disc by using the optimal numerical value of the plasma spray process parameter combination.

[0107] In an exemplary embodiment, an electronic device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for optimizing process parameters of plasma spray repair of aircraft engine wheel discs.

[0108] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, a method for optimizing process parameters of a plasma spray repair of an aircraft engine wheel is implemented.

[0109] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements a method for optimizing process parameters of a plasma spray repair of an aircraft engine disk.

[0110] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the process parameters of plasma spraying repair of an aircraft engine wheel disc is implemented.

[0111] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0112] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0113] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0114] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0115] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing process parameters of aircraft engine disk plasma spray repair, characterized in that: The method for optimizing process parameters of aircraft engine wheel disk plasma spray repair comprises: Within the setting range of each plasma spraying process parameter of the aircraft engine wheel, randomly generate a plasma spraying process parameter combination to form a parent population; the plasma spraying process parameter combination includes: each plasma spraying process parameter, the square of each plasma spraying process parameter and the product of each plasma spraying process parameter; Each group of plasma spraying process parameter combinations is regarded as an individual in a population, the binary numbers after the parameters in each group of plasma spraying process parameter combinations are encoded are determined as genes, the binary codes formed by sequentially arranging all the genes corresponding to each group of plasma spraying process parameter combinations are determined as chromosomes of the corresponding group of plasma spraying process parameter combinations, and the coating performance corresponding to each group of plasma spraying process parameter combinations determined based on the coating performance prediction model is determined as the evaluation basis; wherein the coating performance prediction model is obtained by training the back propagation neural network using the training set; the coating performance includes: hardness and wear amount; Utilizing the NSGA-II algorithm and the coating performance prediction model, each plasma spraying process parameter combination is optimized based on the parent population to obtain an optimal value of the plasma spraying process parameter combination; The plasma spray repair work of aircraft engine disks is controlled by using the optimal numerical control of plasma spray process parameter combination.

2. The method for optimizing process parameters of aircraft engine disk plasma spray repair according to claim 1, characterized in that: The process of determining the coating performance prediction model includes: Obtaining the plasma spray repair process parameters and corresponding coating properties of the aircraft engine wheel that has been spray repaired; Determining a combination of plasma spraying process parameters for each aircraft engine wheel disc that has been repaired by spraying based on the plasma spraying repair process parameters for each aircraft engine wheel disc that has been repaired by spraying; Performing a maximum absolute value normalization process on the plasma spraying process parameter combinations of each aircraft engine wheel that has been sprayed and repaired, and obtaining a corresponding processed plasma spraying process parameter combination; The coating properties of each aircraft engine wheel that has been sprayed and repaired are normalized to the maximum and minimum values ​​to obtain the corresponding coating properties after treatment; Constructing the training set based on the plasma spraying process parameter combinations and coating properties of each aircraft engine wheel disc that has been sprayed and repaired; Constructing the back propagation neural network; The back propagation neural network is trained based on the training set using the Adam optimizer to obtain the coating performance prediction model.

3. The method for optimizing process parameters of aircraft engine disk plasma spray repair according to claim 1, characterized in that: Using the NSGA-II algorithm and the coating performance prediction model, each plasma spraying process parameter combination is optimized based on the parent population to obtain the optimal value of the plasma spraying process parameter combination, including: Determine the coating performance corresponding to each individual in the parent population by using the coating performance prediction model; Determine a non-dominated hierarchy corresponding to the parent population based on coating properties corresponding to each individual in the parent population; the non-dominated hierarchy includes a plurality of individuals; According to the order of coating performance, the individuals in the non-dominated hierarchy corresponding to the parent population are sorted, and the crowding degree of each individual in the non-dominated hierarchy corresponding to the parent population is calculated; Based on each individual, the tournament method is used to select excellent individuals for crossover mutation to generate offspring populations, and the parent population and offspring population are merged to obtain the merged population; Determine the coating performance corresponding to each individual in the combined population by using the coating performance prediction model; Based on the coating properties corresponding to each individual in the merged population, determining the non-dominated level corresponding to the merged population; According to the order of coating performance, the individuals in the non-dominated level corresponding to the merged population are sorted, and the crowding degree of each individual in the non-dominated level corresponding to the merged population is calculated; The individuals with the top 50% of crowding degree in the non-dominated level corresponding to the merged population are used to form a new parent population, and "using the coating performance prediction model to determine the coating performance corresponding to each individual in the parent population" is returned until the maximum number of iterations is reached to obtain non-dominated solutions for multiple individuals; Determine the coating performance corresponding to each individual non-dominated solution using the coating performance prediction model; The non-dominated solution of the individual with the best coating performance is determined as the optimal value of the plasma spraying process parameter combination; the optimal coating performance is the maximum hardness and the minimum wear.

4. The method for optimizing process parameters of aircraft engine disk plasma spray repair according to claim 3, characterized in that: Each individual in the non-dominated level is not dominated by any other individual; the meaning of individual A being dominated by individual B is that the coating performance corresponding to individual B is better than the coating performance corresponding to individual A.

5. The method for optimizing process parameters of aircraft engine disk plasma spray repair according to claim 3, characterized in that: The crowding degree of any current individual in the sorted non-dominated hierarchy is the sum of the first difference and the second difference of the current individual; The first difference is the difference between the coating performance of the current individual and the coating performance of the previous individual, and the second difference is the difference between the coating performance of the current individual and the coating performance of the next individual.

6. The method for optimizing process parameters of aircraft engine disk plasma spray repair according to claim 2, characterized in that: The back propagation neural network comprises: an input layer, a hidden layer and an output layer; the input layer comprises 14 input units, the hidden layer comprises 128 hidden units, and the output layer comprises 2 output units; The hidden layer and the output layer both use the ReLU activation function.

7. An aircraft engine wheel plasma spray repair process parameter optimization system, characterized in that: The aircraft engine wheel disk plasma spray repair process parameter optimization system comprises: An initialization module is used to randomly generate a combination of plasma spraying process parameters to form a parent population within the setting range of each plasma spraying process parameter of the aircraft engine wheel; the plasma spraying process parameter combination includes: each plasma spraying process parameter, the square of each plasma spraying process parameter and the product of each plasma spraying process parameter; A parameter setting module is used to take each group of plasma spraying process parameter combinations as individuals in a population, determine the binary numbers after parameter encoding in each group of plasma spraying process parameter combinations as genes, determine the binary codes formed by sequentially arranging all genes corresponding to each group of plasma spraying process parameter combinations as chromosomes of the corresponding group of plasma spraying process parameter combinations, and determine the coating performance corresponding to each group of plasma spraying process parameter combinations determined based on the coating performance prediction model as an evaluation basis; wherein the coating performance prediction model is obtained by training a back propagation neural network using a training set; the coating performance includes: hardness and wear amount; An optimization module, for optimizing each plasma spraying process parameter combination based on the parent population by using the NSGA-II algorithm and the coating performance prediction model, to obtain an optimal value of the plasma spraying process parameter combination; A control module is used to control the plasma spray repair work of an aircraft engine wheel disc by using the optimal numerical value of the plasma spray process parameter combination.

8. An electronic device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing the process parameters of the aircraft engine disk plasma spray repair as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for optimizing the process parameters of the aircraft engine disk plasma spray repair as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for optimizing the process parameters of the aircraft engine disk plasma spray repair as described in any one of claims 1 to 6 is implemented.

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