Mold surface repairing method and system based on laser cladding

Abstract

The invention provides a mold surface repairing method and system based on laser cladding, and relates to the technical field of laser cladding, the method comprises the following steps: selecting a matrix material with a proper size, and pretreating the surface of the matrix material; according to the actual process requirements, the value range of laser cladding process parameters is determined, and a response curved surface model of the microhardness and the dilution rate is established; optimizing the response curved surface model, and obtaining an optimized process parameter combination by taking maximization of microhardness and minimization of dilution rate as targets; according to the optimized technological parameter combination, the laser cladding process is carried out, and a cladding layer is formed on the pretreated base material; and performing performance detection on the formed cladding layer, and adjusting process parameters according to a performance detection result until an optimal process parameter combination is determined. By means of the method, it can be guaranteed that the hardness of the cladding layer is stabilized within the ideal range, the mold repairing quality is remarkably improved, and the service life of the mold is remarkably prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of laser cladding, and in particular to a mold surface repair method and system based on laser cladding. Background Art

[0002] In modern industrial production, molds are key tooling equipment, and their performance and service life directly affect product quality and production efficiency. Traditional mold repair processes, such as surfacing, electroplating, cold welding and spraying, can meet the basic needs of mold repair to a certain extent, but there are many shortcomings. The surfacing process is prone to thermal deformation of the mold due to the large heat input, and the heat-affected zone in the repair area is wide, which affects the overall performance of the mold. Although the electroplating process has a low temperature and is not easy to cause mold deformation, the hardness and bonding strength of the coating are limited, and it is difficult to adapt to high-wear environments. Cold welding technology is suitable for repairing small-scale damage, but it has limited effect on repairing large-area or deep damage. Although spraying technology can provide a harder coating, its bonding force is weak, and the coating is easy to fall off under high stress conditions, which limits its scope of application.

[0003] With the advancement of science and technology and the development of industry, laser cladding technology, as a new type of mold repair method, has gradually attracted widespread attention due to its low dilution rate, fine microstructure and excellent metallurgical bonding strength. Laser cladding technology can form a high-quality repair layer, significantly improve the wear resistance, corrosion resistance and high temperature performance of the mold, and achieve precise material deposition and energy input through intelligent control, reducing mold deformation and the need for secondary processing, greatly improving the accuracy and reliability of repair.

[0004] Although laser cladding technology has shown great potential in the field of mold repair, there are still many challenges and problems in the existing technology, which are specifically manifested in the following aspects: (1) The hardness of the cladding layer is difficult to stably control: Existing technologies usually find it difficult to stably achieve a high-hardness cladding layer, resulting in the repaired mold having an insufficient life under high wear conditions and being unable to meet the strict requirements of high-performance molds.

[0005] (2) Low efficiency of process parameter optimization: During the laser cladding process, the adjustment of process parameters (such as laser power, scanning speed, etc.) lacks systematic multi-objective optimization, which makes it difficult to simultaneously optimize hardness, wear resistance and bonding strength, affecting the overall performance and stability of the cladding layer.

[0006] (3) Poor bonding strength and wear resistance: Traditional laser cladding technology is difficult to effectively improve the bonding strength and wear resistance of the cladding layer while improving the hardness. It is easy to have problems such as insufficient bonding force or insufficient wear resistance, which affects the long-term performance of the mold.

[0007] (4) Single parameter control method and poor process repeatability: The existing technology usually uses single factor test or empirical method to adjust the process parameters, which lacks systematization and scientificity, resulting in difficulty in stable control of the quality of the cladding layer. At the same time, due to insufficient process parameter control, the consistency of the repair process is poor, which restricts the promotion and application of the technology in large-scale production.

[0008] (5) Low material utilization efficiency: During the laser cladding process, the utilization efficiency and cost factors of material resources are not fully considered, which limits the economy and practicality of laser cladding technology.

[0009] In summary, although the existing laser cladding technology has shown great potential in the field of mold repair, it still needs to solve key issues such as hardness control, process parameter optimization, bonding strength and wear resistance improvement, parameter control method and material utilization efficiency to achieve more efficient, stable and economical mold repair. Therefore, developing a new type of laser cladding technology to solve the problems of existing technologies has important practical significance and application value. Summary of the invention

[0010] To this end, an embodiment of the present invention provides a mold surface repair method and system based on laser cladding, which is used to solve the problems faced by existing laser cladding technology in the field of mold repair, such as unstable hardness control, insufficient process parameter optimization, poor bonding strength and wear resistance, single parameter control and poor process repeatability, and low material utilization efficiency.

[0011] In order to solve the above problems, an embodiment of the present invention provides a mold surface repair method based on laser cladding, the method comprising: Selecting a base material of suitable size and pre-treating the surface of the base material; According to the actual process requirements, the range of laser cladding process parameters is determined, and a response surface model of microhardness and dilution rate is established, wherein the laser cladding process parameters include laser power, scanning speed, and powder feeding amount; Optimizing the response surface model to maximize microhardness and minimize dilution rate, and obtaining an optimized process parameter combination; According to the optimized process parameter combination, a laser cladding process is performed to form a cladding layer on the pretreated base material; The formed cladding layer is subjected to performance tests, including hardness test, dilution rate test and microstructure analysis, and the process parameters are adjusted according to the performance test results until the optimal process parameter combination is determined.

[0012] Preferably, the pretreatment of the surface of the base material comprises: The surface of the base material is polished with sandpaper to remove the surface oxide layer and impurities, and the polished surface is cleaned with anhydrous ethanol.

[0013] Preferably, the method for establishing a response surface model of microhardness and dilution rate comprises: Central composite design or Box-Behnken design was used for experimental planning; Adjust the laser power, scanning speed and powder feeding amount to measure the corresponding microhardness and dilution rate; Using the experimental data, a response surface model of microhardness and dilution rate was established by quadratic polynomial regression.

[0014] Preferably, the laser cladding process parameters have a value range of: Laser power range: 2600W-3000W; The scanning speed range is 4mm / s-10mm / s; The range of powder feeding amount is: 4g / min-6g / min.

[0015] Preferably, the method of optimizing the response surface model to maximize microhardness and minimize dilution rate to obtain an optimized process parameter combination comprises: The response surface model is optimized by using the NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution, with the goal of maximizing microhardness and minimizing dilution rate, to obtain an optimized process parameter combination; The NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution includes the following steps: Initialize the population and generate an initial population containing multiple individuals; Define the fitness function and calculate the objective function value of each individual in the population; Perform non-dominated sorting and crowding calculations to assess the quality of individuals; The next generation population is generated using tournament selection, simulated binary crossover, and polynomial mutation operations; Repeat the selection, crossover, and mutation operations, and introduce dynamic Gaussian distribution to adaptively adjust the mutation operation and dynamically adjust the mutation amplitude; Until the set termination condition is met, the Pareto front solution set is output.

[0016] Preferably, the method of introducing dynamic Gaussian distribution to adaptively adjust the variation operation and dynamically adjust the variation amplitude includes: Set initial mean and the initial standard deviation ; According to the current individual and the mean of the dynamic Gaussian distribution and standard deviation , generate variant individuals : ; in, Indicates a is the mean, is the Gaussian noise with standard deviation; Adjust the mean and standard deviation based on the fitness and diversity of the population; Apply mutation operations to individuals, update the population and iterate to the next generation.

[0017] Preferably, the laser cladding process comprises: The laser is started, and the laser beam scans the surface of the pretreated base material along a set path; The molten powder material is uniformly deposited on the substrate surface to form a cladding layer; During the cladding process, shielding gas is used to avoid oxidation of the material.

[0018] The embodiment of the present invention further provides a mold surface repair system based on laser cladding, which is used to implement the above-mentioned mold surface repair method based on laser cladding, and specifically includes: A base material selection and pretreatment module is used to select a base material of suitable size and pretreat the surface of the base material; The laser cladding process parameter determination and model building module is used to determine the value range of the laser cladding process parameters according to the actual process requirements, and to establish a response surface model of microhardness and dilution rate, wherein the laser cladding process parameters include laser power, scanning speed, and powder feeding amount; A response surface model optimization module is used to optimize the response surface model to maximize microhardness and minimize dilution rate, and obtain an optimized process parameter combination; A laser cladding process implementation module is used to perform a laser cladding process according to an optimized process parameter combination to form a cladding layer on the pre-treated base material; The cladding layer performance detection and process parameter adjustment module is used to perform performance detection on the formed cladding layer, including hardness detection, dilution rate detection and microstructure analysis, and adjust the process parameters according to the performance detection results until the optimal process parameter combination is determined.

[0019] An embodiment of the present invention also provides an electronic device, which includes a processor, a memory and a bus system, wherein the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned mold surface repair method based on laser cladding.

[0020] An embodiment of the present invention further provides a computer storage medium storing a computer software product. The computer software product includes a number of instructions for enabling a computer device to execute the above-mentioned mold surface repair method based on laser cladding.

[0021] It can be seen from the above technical solutions that the present invention has the following beneficial effects: (1) The present invention combines the response surface analysis method with the NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution, and proposes an innovative laser cladding process optimization method, which effectively improves the quality and service life of mold repair and overcomes the shortcomings of the existing technology.

[0022] (2) The NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution significantly improves the exploration ability, convergence speed and solution accuracy of multi-objective optimization. By dynamically adjusting the distribution and mutation strategy of the population, it effectively avoids the local optimal solution trap and improves the efficiency of the optimization process and the quality of the solution.

[0023] (3) The present invention provides a systematic and scientific solution for the laser cladding process. By optimizing key parameters such as laser power, scanning speed, and powder feeding amount, the microhardness is improved and the dilution rate is reduced. The present invention is particularly suitable for aerospace, mold repair, component enhancement and other fields, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following is a brief description of the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 A flow chart of a mold surface repair method based on laser cladding provided in an embodiment; Figure 2 The overall structure of the present invention in the embodiments; Figure 3 is a three-dimensional schematic diagram of the Pareto front solution set obtained in the embodiment; Figure 4A block diagram of a mold surface repair system based on laser cladding provided in an embodiment. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1

[0026] In order to solve the problems faced by existing laser cladding technology in the field of mold repair, such as unstable hardness control, insufficient process parameter optimization, poor bonding strength and wear resistance, single parameter control and poor process repeatability, and low material utilization efficiency. Figure 1 As shown, an embodiment of the present invention provides a mold surface repair method based on laser cladding, the method comprising: Select a base material of suitable size and pre-treat the surface of the base material; According to the actual process requirements, the range of laser cladding process parameters is determined, and a response surface model of microhardness and dilution rate is established. The laser cladding process parameters include laser power, scanning speed, and powder feeding amount. The response surface model was optimized to maximize the microhardness and minimize the dilution rate, and the optimized process parameter combination was obtained; According to the optimized process parameter combination, a laser cladding process is performed to form a cladding layer on the pretreated base material; The formed cladding layer is subjected to performance tests, including hardness test, dilution rate test and microstructure analysis, and the process parameters are adjusted according to the performance test results until the optimal process parameter combination is determined.

[0027] It can be seen from the above technical scheme that the present invention proposes a mold surface repair method based on laser cladding, which realizes high efficiency and high quality of mold repair by combining the response surface analysis method (RSM) with the NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution. The specific steps include: selecting and pretreating the base material, determining the range of laser cladding process parameters and establishing a response surface model, optimizing the model to maximize the microhardness and minimize the dilution rate, implementing laser cladding according to the optimized parameters, and finally performing performance testing on the cladding layer and adjusting the parameters until the optimal. This method not only ensures that the hardness of the cladding layer is stable within the ideal range, but also significantly improves the quality and service life of mold repair. The introduction of dynamic Gaussian distribution enhances the global and local search capabilities of the algorithm, effectively avoids local optimal solutions, and improves optimization efficiency and accuracy. This technical scheme provides a systematic and scientific solution for the laser cladding process in the field of mold repair, and is particularly suitable for high-precision fields such as aerospace and mold repair.

[0028] In this embodiment, a base material of suitable size is first selected (for example, surface-hardened H13 steel is used as the base material). Then, the surface of the base material is polished with sandpaper to remove the surface oxide layer and impurities, and the polished surface is cleaned with anhydrous ethanol to ensure that the surface is clean and free of oil and particles, so as to enhance the bonding strength between the cladding layer and the base.

[0029] Figure 2 The overall architecture of the present invention is presented. First, the problem definition and experimental design are performed, wherein the problem definition includes: Characteristic variables: laser power P (unit: W), scanning speed V (unit: mm / s), powder feeding rate F (unit: r / min).

[0030] Target variable: Microhardness (Unit: HV), dilution rate (unit:%).

[0031] Optimization objective function: ; Maximizing microhardness: By controlling characteristic variables, a high-hardness cladding layer (microhardness above 820HV) is obtained.

[0032] Minimize the dilution rate: The lower the dilution rate, the lower the mixing degree of the base material and the cladding layer, and the better the material performance (dilution rate is less than 15%).

[0033] Furthermore, the experimental design includes: (1) Determine the range of laser cladding process parameters: According to the actual process requirements, determine the range of laser power P, scanning speed V and powder feeding amount F: Laser power range: 2600W-3000W; The scanning speed range is 4mm / s-10mm / s; The range of powder feeding amount is: 4g / min-6g / min.

[0034] (2) Response surface design: In this example, DesignExpert13 software was used to plan the experiment using an appropriate experimental design method (such as central composite design (CCD) or Box-Behnken design). The laser power, scanning speed, and powder feeding amount were adjusted during the experiment to measure the corresponding microhardness and dilution rate.

[0035] (3) Construction of response surface model: Using experimental data, a response surface model of microhardness and dilution rate was established through quadratic polynomial regression and other methods.

[0036] Specifically, this embodiment uses the response surface method to fit the response surface model of laser power, scanning speed, powder feeding amount, microhardness, and dilution based on the experimental results to determine the impact of process parameters on performance. The response surface model example is as follows: ; .

[0037] (4) Model verification: The accuracy and goodness of fit of the response surface model are verified through methods such as analysis of variance (ANOVA) to ensure that the model can better predict the target variable.

[0038] In order to optimize the bonding strength, wear resistance and microstructure uniformity of the cladding layer, this embodiment uses the NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution to optimize the response surface model, with the goal of maximizing the microhardness and minimizing the dilution rate to obtain the optimized process parameter combination.

[0039] Specifically, the NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution includes the following steps: Initialize the population: Generate a population containing The initial population of individuals , each individual represents a solution. The genetic encoding of each individual is consistent with the decision variables of the optimization problem. , whose target vector is calculated, where It is An objective function.

[0040] Fitness function definition: For each individual in the population, all objective functions are calculated according to the definition of the optimization problem. For example, if the objective function is to minimize the following two objectives: ; ; It is necessary to evaluate the target value for each individual in the population.

[0041] (3) Non-dominated sorting: Perform non-dominated sorting on the population according to the following rules and divide the individuals into multiple frontier layers: : An individual Domination of another individual , if both: ; ; Non-dominated sorting extracts individuals from the population that are not dominated by other individuals as the first frontier layer , remove these individuals and repeat the above steps for the remaining individuals to obtain .

[0042] (4) Crowding calculation: By calculating the crowding degree of each individual in the population Used to measure the diversity of a population. The crowding degree of individuals is:

[0043] in, , The targets are Previous The target value of the individual's neighbors; , The targets are The crowding degree of the boundary individuals is set to infinity to ensure their retention.

[0044] (5) Selection and crossover mutation: Selection: Tournament Selection is used to select based on the individual ranking and crowding: Preference is given to individuals with lower non-dominant hierarchies; If two individuals are in the same non-dominated layer, the one with higher crowding degree is selected.

[0045] Crossover and mutation: Perform crossover and mutation operations on the selected individuals to generate new solutions. Generate the next generation population : Crossover operation: Use simulated binary crossover (SBX) to generate two offspring and If the father and , then the offspring calculation formula is: ; ; in, is the crossover parameter, which is controlled by the random number and the crossover probability.

[0046] Mutation operation: Polynomial mutation is used. The individuals after mutation are:

[0047] in, is a random value controlled by the distribution index, They are The maximum and minimum values ​​of an individual.

[0048] (6) Iterative optimization: Repeat the selection, crossover, mutation and other operations, and introduce dynamic Gaussian distribution to adjust the mutation strategy and dynamically adjust the mutation amplitude until the set termination condition (such as maximum number of generations or fitness convergence) is met. The Pareto frontier solution set is obtained during the optimization process, which contains a series of non-dominated solutions under different process parameter combinations, such as Figure 3 As shown in Figure 2, the best combination of process parameters is selected from the Pareto frontier solution set in combination with actual requirements (such as maximizing microhardness and minimizing dilution rate).

[0049] Specifically, this embodiment uses dynamic Gaussian distribution to adaptively adjust the mutation operation, and dynamically adjusting the mutation amplitude includes: Initialization: Setting the initial mean and the initial standard deviation .

[0050] Mutation calculation: Based on the current individual and the mean of the dynamic Gaussian distribution and standard deviation , generate variant individuals : ; in, Indicates a is the mean, is the standard deviation of Gaussian noise.

[0051] Update mean and standard deviation: Adjust the mean and standard deviation based on the fitness and diversity of the population.

[0052] Apply mutation: Apply the mutation operation to individuals, update the population and iterate for the next generation.

[0053] Furthermore, the introduction of dynamic Gaussian distribution and its basic principles and adaptive adjustment mechanism are described as follows: The basic principle of dynamic Gaussian distribution: The core idea of ​​dynamic Gaussian distribution is to flexibly adjust the scale of mutation operation by using the characteristics of Gaussian distribution to adapt to different needs in the optimization process. Gaussian distribution is usually described by its mean μ and standard deviation σ. In dynamic Gaussian distribution, these two represent the center point and variation range of mutation operation respectively. The mathematical expression of Gaussian distribution is: ; In the application scenario of dynamic Gaussian distribution, the mean and standard deviation It will be dynamically adjusted according to the change of time (or iteration number) to meet the current population diversity and convergence requirements.

[0054] Adaptive adjustment mechanism: Dynamic Gaussian distribution adaptively adjusts the standard deviation (i.e., the amplitude of the mutation) during the mutation process, so that the mutation operation can change dynamically as the algorithm runs, thereby optimizing the effects of global search and local refinement. The specific goals of adaptive adjustment include: Initial stage (global search): In the early stage of the algorithm, the population diversity is high. At this time, a large variation range should be maintained to explore a wider solution space and improve the global search capability.

[0055] Late stage (local search): As the number of iterations increases, the population gradually converges. At this time, the variation amplitude should be reduced to make the search process more concentrated near the local optimal solution, thereby improving the accuracy of local search.

[0056] Specifically, to achieve the above goals, the dynamic Gaussian distribution can be adaptively adjusted from the following aspects: Standard deviation adjustment: During the optimization process, the standard deviation is dynamically adjusted according to the fitness and diversity of the current population to control the intensity of mutation. Generally, the standard deviation will gradually decrease as the number of iterations increases.

[0057] Mean adjustment: Dynamically adjust the mean according to the fitness distribution of the population. For example, when the population fitness is good, the mean can be closer to the current optimal solution, prompting the mutation operation to be more concentrated in the high-quality area.

[0058] Balance of mutation strategy: Under the setting of dynamic Gaussian distribution, the adjustment of mutation intensity needs to consider the diversity of the current population. If the individual differences in the population are large, maintain a larger mutation range; if the population tends to be consistent, reduce the mutation range. This balanced adjustment strategy helps to avoid premature convergence and loss of population diversity.

[0059] Application and advantages of dynamic Gaussian distribution in NSGAII algorithm: NSGAII algorithm based on dynamic Gaussian distribution can significantly improve the exploration ability, convergence speed and solution accuracy of multi-objective optimization. The key is to effectively avoid the local optimal solution trap by dynamically adjusting the distribution and mutation strategy of the population. The specific performance is: Balance between global search and local search: In the early stage of optimization, increase the variation range to promote extensive exploration; in the convergence stage, reduce the variation range to concentrate the search and improve the accuracy of local search. When the population tends to converge, increase the variation range to restore diversity and maintain the ability to explore new solutions.

[0060] Flexible control of search range: Dynamic Gaussian distribution adjusts the standard deviation of mutation according to population distribution and fitness value. A large standard deviation enhances global search capabilities, while a small standard deviation improves local search accuracy.

[0061] In summary, by dynamically adjusting the mutation strategy and population distribution, the NSGAII algorithm can adaptively switch between global search and local search, avoiding premature convergence and effectively escaping the local optimal solution. This feature enables the algorithm to have higher efficiency and solution quality when dealing with complex high-dimensional multi-objective optimization problems. For applications such as laser cladding process parameter optimization, the introduction of dynamic Gaussian distribution helps to more efficiently balance multiple objectives and improve optimization efficiency and accuracy.

[0062] Specifically, this embodiment is based on the optimized process parameter combination, such as Figure 3 As shown, the laser cladding process is performed to form a cladding layer on the pre-treated base material, including: The laser is started, and the laser beam scans the pre-treated surface of the substrate material along the set path; the molten powder material is evenly deposited on the substrate surface to form a cladding layer; during the cladding process, a protective gas (such as argon) is used to prevent material oxidation.

[0063] Specifically, this embodiment performs performance tests on the formed cladding layer, including hardness test (testing the hardness of the cladding layer by a Vickers hardness tester), dilution rate test (measuring the dilution rate of the cladding layer and the base material to ensure that it meets expectations) and microstructure analysis (analyzing the microstructure of the cladding layer by a microscope to ensure the uniformity of the structure), and adjusts the process parameters according to the performance test results until the optimal combination of process parameters is determined.

[0064] Finally, after the surface of the H13 steel mold is repaired by the method proposed in the present invention, the microhardness of the obtained cladding layer can be stably maintained in the range of 760HV to 820HV, while ensuring that the dilution rate is less than 15%. This method effectively improves the quality and service life of mold repair, overcomes the shortcomings of the prior art, and provides a systematic and scientific solution for optimizing the bonding strength, wear resistance and microstructure uniformity of the cladding layer in mold repair with high efficiency and quality. The present invention is particularly suitable for laser cladding processes in the fields of aerospace, mold repair, and component enhancement. Embodiment 2

[0065] like Figure 4 As shown, the present invention provides a mold surface repair system based on laser cladding, which is used to implement the mold surface repair method based on laser cladding of the above embodiment 1, and specifically includes: A base material selection and pretreatment module is used to select a base material of suitable size and pretreat the surface of the base material; Laser cladding process parameter determination and model building module, which is used to determine the value range of laser cladding process parameters according to actual process requirements, and to establish a response surface model of microhardness and dilution rate, where the laser cladding process parameters include laser power, scanning speed, and powder feeding amount; Response surface model optimization module, used to optimize the response surface model to maximize microhardness and minimize dilution rate, and obtain the optimized process parameter combination; A laser cladding process implementation module is used to perform a laser cladding process according to an optimized process parameter combination to form a cladding layer on the pre-treated base material; The cladding layer performance detection and process parameter adjustment module is used to perform performance detection on the formed cladding layer, including hardness detection, dilution rate detection and microstructure analysis, and adjust the process parameters according to the performance detection results until the optimal process parameter combination is determined.

[0066] A mold surface repair system based on laser cladding in this embodiment is used to implement the aforementioned mold surface repair method based on laser cladding. Therefore, the specific implementation method of the mold surface repair system based on laser cladding can be seen in the embodiment part of the mold surface repair method based on laser cladding in the previous text. In order to avoid redundancy, it will not be repeated here. Embodiment 3

[0067] An embodiment of the present invention provides an electronic device, which includes a processor, a memory and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned mold surface repair method based on laser cladding. Embodiment 4

[0068] An embodiment of the present invention provides a computer storage medium storing a computer software product. The computer software product includes a number of instructions for enabling a computer device to execute the above-mentioned mold surface repair method based on laser cladding.

[0069] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from them are still within the protection scope of the invention.

Claims

1. A mold surface repair method based on laser cladding, characterized in that: include: Selecting a base material of suitable size and pre-treating the surface of the base material; According to the actual process requirements, the range of laser cladding process parameters is determined, and a response surface model of microhardness and dilution rate is established, wherein the laser cladding process parameters include laser power, scanning speed, and powder feeding amount; Optimizing the response surface model to maximize microhardness and minimize dilution rate, and obtaining an optimized process parameter combination; According to the optimized process parameter combination, a laser cladding process is performed to form a cladding layer on the pretreated base material; The formed cladding layer is subjected to performance tests, including hardness test, dilution rate test and microstructure analysis, and the process parameters are adjusted according to the performance test results until the optimal process parameter combination is determined.

2. The mold surface repair method based on laser cladding according to claim 1 is characterized in that: The pretreatment of the surface of the base material comprises: The surface of the base material is polished with sandpaper to remove the surface oxide layer and impurities, and the polished surface is cleaned with anhydrous ethanol.

3. The mold surface repair method based on laser cladding according to claim 1 is characterized in that: The method for establishing a response surface model of microhardness and dilution rate comprises: Central composite design or Box-Behnken design was used for experimental planning; Adjust the laser power, scanning speed and powder feeding amount to measure the corresponding microhardness and dilution rate; Using the experimental data, a response surface model of microhardness and dilution rate was established by quadratic polynomial regression.

4. The mold surface repair method based on laser cladding according to claim 1 is characterized in that: The value range of the laser cladding process parameters is: Laser power range: 2600W-3000W; The scanning speed range is 4mm / s-10mm / s; The range of powder feeding amount is: 4g / min-6g / min.

5. The mold surface repair method based on laser cladding according to claim 1, characterized in that: The method of optimizing the response surface model to maximize microhardness and minimize dilution rate to obtain an optimized process parameter combination includes: The response surface model is optimized by using the NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution, with the goal of maximizing microhardness and minimizing dilution rate, to obtain an optimized process parameter combination; The NSGA-II multi-objective optimization algorithm based on dynamic Gaussian distribution includes the following steps: Initialize the population and generate an initial population containing multiple individuals; Define the fitness function and calculate the objective function value of each individual in the population; Perform non-dominated sorting and crowding calculations to assess the quality of individuals; The next generation population is generated using tournament selection, simulated binary crossover, and polynomial mutation operations; Repeat the selection, crossover, and mutation operations, and introduce dynamic Gaussian distribution to adaptively adjust the mutation operation and dynamically adjust the mutation amplitude; Until the set termination condition is met, the Pareto front solution set is output.

6. The mold surface repair method based on laser cladding according to claim 5 is characterized in that: The method of introducing dynamic Gaussian distribution to adaptively adjust the mutation operation and dynamically adjust the mutation amplitude includes: Set initial mean and the initial standard deviation ; According to the current individual and the mean of the dynamic Gaussian distribution and standard deviation , generate variant individuals : ； in, Indicates a is the mean, is the Gaussian noise with standard deviation; Adjust the mean and standard deviation based on the fitness and diversity of the population; Apply mutation operations to individuals, update the population and iterate to the next generation.

7. The mold surface repair method based on laser cladding according to claim 1 is characterized in that: The laser cladding process includes: The laser is started, and the laser beam scans the surface of the pretreated base material along a set path; The molten powder material is uniformly deposited on the surface of the substrate to form a cladding layer; During the cladding process, shielding gas is used to avoid oxidation of the material.

8. A mold surface repair system based on laser cladding, characterized in that: The system is used to implement the mold surface repair method based on laser cladding according to any one of claims 1 to 7, specifically comprising: A base material selection and pretreatment module is used to select a base material of suitable size and pretreat the surface of the base material; The laser cladding process parameter determination and model building module is used to determine the value range of the laser cladding process parameters according to the actual process requirements, and to establish a response surface model of microhardness and dilution rate, wherein the laser cladding process parameters include laser power, scanning speed, and powder feeding amount; A response surface model optimization module is used to optimize the response surface model to maximize microhardness and minimize dilution rate, and obtain an optimized process parameter combination; A laser cladding process implementation module is used to perform a laser cladding process according to an optimized process parameter combination to form a cladding layer on the pre-treated base material; The cladding layer performance detection and process parameter adjustment module is used to perform performance detection on the formed cladding layer, including hardness detection, dilution rate detection and microstructure analysis, and adjust the process parameters according to the performance detection results until the optimal process parameter combination is determined.

9. An electronic device, characterized in that: The electronic device includes a processor, a memory and a bus system, the processor and the memory are connected through the bus system, the memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the mold surface repair method based on laser cladding as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer software product, which includes a number of instructions for enabling a computer device to execute the mold surface repair method based on laser cladding as described in any one of claims 1 to 7.

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