Blade repairing method and system based on laser cladding simulation model

Abstract

The invention discloses a blade repairing method and system based on a laser cladding simulation model, and relates to the technical field of blade repairing, the laser cladding simulation model is established, high-speed laser cladding process parameters are set as model variables to be input into the laser cladding simulation model, and the laser cladding process under different parameter combinations is simulated. The method comprises the following steps: preparing a cladding layer, acquiring a simulation result of the cladding layer, preparing cladding samples under different technological parameter combinations, performing characteristic analysis, recording related analysis data, and establishing a performance prediction model of a corresponding mapping relationship between the technological parameter combinations and blade coating performance based on the related analysis data so as to predict the coating performance of the blade under different technological parameter combinations. Combining specific service environment conditions of the blade, setting performance constraint conditions, optimizing a process parameter combination, determining an optimal preparation process parameter combination, performing laser cladding repair on the actually damaged blade, monitoring the laser cladding process in real time, determining whether to intervene or not, and performing quality detection after the repair is completed.

Description

Technical Field

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

[0002] In the fields of aerospace, energy and electricity, blades, as key components, are in harsh service environments such as high temperature, high pressure, and high-speed erosion for a long time. They are prone to wear, corrosion and other damage, which leads to the degradation of blade performance or even failure. Traditional blade repair methods such as welding and thermal spraying have problems such as low repair accuracy, large heat-affected zone, and difficulty in ensuring the mechanical properties of the repaired blades. Laser cladding technology has great application potential in the field of blade repair due to its advantages such as low dilution rate, small heat-affected zone, and ability to repair in situ.

[0003] However, the laser cladding process involves complex physical and chemical changes, and there are many mutually coupled process parameters, such as laser power, laser scanning speed, and powder feeding speed. These parameters have a significant impact on the microstructure, coating-base bonding strength, corrosion resistance, and wear resistance of the cladding coating. At present, in the actual repair process, the selection of process parameters mostly relies on experience and lacks scientific and effective theoretical guidance, resulting in uneven quality of the repaired blades, which is difficult to meet the growing high-performance requirements. Therefore, how to deeply analyze the influence of process parameters on the quality of blade repair based on the laser cladding simulation model and optimize the process parameters to obtain the best preparation process has become an urgent problem to be solved. Summary of the invention

[0004] In order to solve the above problems, the object of the present invention is to provide a blade repair method and system based on a laser cladding simulation model.

[0005] The purpose of the present invention can be achieved by the following technical solution: A blade repair method based on a laser cladding simulation model comprises the following steps: Step S1: establishing a laser cladding simulation model, setting model parameters, setting high-speed laser cladding process parameters as model variables and inputting them into the laser cladding simulation model, simulating the laser cladding process under different parameter combinations, and obtaining simulation results of the cladding layer; Step S2: According to the simulation results, prepare cladding samples under different process parameter combinations, perform characteristic analysis on the cladding samples, and record relevant analysis data; Step S3: Based on the relevant analysis data, a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance is established, and the performance prediction model is operated based on the test data to predict the coating performance of the blade under different process parameter combinations; Step S4: In combination with the specific service environment conditions of the blade, performance constraints are set, and the process parameter combination is optimized with the goal of optimizing the comprehensive coating performance to determine the best preparation process parameter combination; Step S5: Use the best combination of preparation process parameters to perform laser cladding repair on the actual damaged blade, monitor the key working parameters of the laser cladding process in real time, and decide whether to intervene. After the repair is completed, perform quality inspection on the blade.

[0006] Furthermore, the process of establishing a laser cladding simulation model and setting model parameters includes: Acquire the geometric information of the blade, and construct a three-dimensional solid model of the blade according to the geometric information; Obtain all process steps in the entire laser cladding process, establish a step operation model corresponding to each process step, and construct a laser cladding process model based on all step operation models; The initial convolutional neural network framework model is constructed through deep learning technology, and the blade three-dimensional solid model and the laser cladding process model are synchronously mapped into the convolutional neural network framework model, and then the convolutional neural network framework model is constructed as a laser cladding simulation model; The model parameters corresponding to the laser cladding simulation model are set, and the model parameters include blade material parameters, cladding material parameters and environmental parameters.

[0007] Furthermore, the process of setting the high-speed laser cladding process parameters as model variables and inputting them into the laser cladding simulation model, simulating the laser cladding process under different parameter combinations, and obtaining the simulation results of the cladding layer includes: Set the high-speed laser cladding process parameters corresponding to the laser cladding simulation model. The high-speed laser cladding process parameters include laser power, laser scanning speed, and powder feeding speed, which are denoted as W and V respectively. 扫描 and V 送粉 ; Set the power safety range and record it as Ω[w]; Set the effective speed ranges corresponding to the laser scanning speed and the powder feeding speed, and record them as Ω[v 扫描 ] and Ω[v 送粉 ]; Keep W Total laser power in Ω[W], excluding W The total laser power of Ω[w] generates a laser power set; Keep V 扫描 Ω[v 扫描 ] of all laser scanning speeds, excluding V 扫描 Ω[v 扫描] to generate a laser scanning speed set; Keep V 送粉 Ω[v 送粉 ] of all powder feeding speeds, excluding V 送粉 Ω[v 送粉 ] to generate a powder feeding speed set; The laser power set, laser scanning speed set and powder feeding speed set are integrated as model variables and input into the laser cladding simulation model. A laser power, laser scanning speed and powder feeding speed are randomly selected from the laser power set, laser scanning speed set and powder feeding speed set and integrated as a parameter combination to generate several different parameter combinations. The laser cladding simulation model simulates the laser cladding process of the blade under different parameter combinations to obtain the simulation results of different cladding layers of the blade.

[0008] Furthermore, the process of preparing cladding samples under different process parameter combinations, performing characteristic analysis on the cladding samples, and recording relevant analysis data includes: Construct a sample information library, which is used to store cladding sample ratio information corresponding to different simulation results, input any simulation result obtained by the laser cladding simulation model into the sample information library, obtain cladding sample ratio information that matches the current simulation result and meets the standard, and prepare a cladding sample with a working parameter combination corresponding to the current simulation result according to the cladding sample ratio information; Repeat the above operation, input all simulation results into the sample information library, and then obtain the cladding sample ratio information corresponding to each simulation result, and prepare the cladding samples under different process parameter combinations; perform feature analysis on all cladding samples to obtain relevant analysis data of the cladding samples, including geometric feature parameters, microscopic feature parameters, mechanical property parameters and functional performance parameters.

[0009] Furthermore, based on the relevant analysis data, a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance is established, and the performance prediction model is operated based on the test data. The process of predicting the coating performance of the blade under different process parameter combinations includes: Perform initial model selection based on the amount of relevant analysis data, and then build the model architecture corresponding to the initial performance prediction model, and define the input features and output targets corresponding to the initial performance prediction model; The input features are different combinations of process parameters; the output targets are different coating performance indicators corresponding to the blade, including blade hardness, residual stress and defect rate; Construct a function mapping relationship between a process parameter combination and a coating performance index, set a division ratio to divide the relevant analysis data into a training set and a validation set, input the training set into the performance prediction model under the initial model architecture for model training, and obtain the corresponding model evaluation index through the validation set. When the model evaluation index meets the requirements, construct the final performance prediction model based on the function mapping relationship between the process parameter combination and the blade coating performance. The test data is obtained and input into the performance prediction model to predict the coating performance of the blade under different process parameter combinations. The coating performance of the blade includes four performance levels: excellent, good, qualified and unqualified.

[0010] Furthermore, in combination with the specific service environment conditions of the blade, performance constraints are set, and the process parameter combination is optimized with the goal of achieving the best comprehensive coating performance. The process of determining the best preparation process parameter combination includes: The specific service environment conditions of the blade include temperature conditions, mechanical load conditions and corrosion environment conditions; set performance constraints, including the threshold interval or endpoint threshold composition corresponding to blade hardness, residual stress, defect rate, laser power, laser scanning speed and powder feeding speed; The coating performance of the blade is set at an excellent performance level as the goal of the best comprehensive coating performance. A multi-objective optimization model is constructed based on the goal of the best comprehensive coating performance, the specific service environment conditions of the blade, and the performance constraints. Different process parameter combinations are used as debugging data for the multi-objective optimization model. The coating index coefficient is obtained in real time by the multi-objective optimization model, and an excellent quantitative value is set. When the coating index coefficient is ≥ the excellent quantitative value, the current process parameter combination is selected as the candidate data, and all the candidate data are integrated to generate a candidate data set, and the optimization conditions of the candidate data set are set; When the coating index coefficient is less than the excellent quantitative value, the corresponding process parameter combination is eliminated; The optimization condition is: select the process parameter combination with the highest coating index coefficient in the candidate data set as the optimal preparation process parameter combination.

[0011] Furthermore, the optimal combination of preparation process parameters is used to perform laser cladding repair on the actual damaged blade, and the key working parameters of the laser cladding process are monitored in real time to decide whether to intervene. After the repair is completed, the process of quality inspection of the blade includes: Use the best preparation process parameters to operate laser cladding related equipment, perform laser cladding repair on the actual damaged blade, and monitor the laser cladding process in real time to obtain all monitoring objects of the laser cladding repair process and the monitoring parameters of each monitoring object; The monitoring parameters of each monitoring object are integrated as the key working parameters of the laser cladding process; Monitoring objects include melt pool temperature, melt pool morphology, powder delivery and laser power; Decide whether to intervene in the laser cladding repair process based on key working parameters; If any of the molten pool temperature, molten pool morphology, powder delivery and laser power does not meet the standards, it is decided to adjust the laser cladding related equipment and intervene in the laser cladding repair process. Otherwise, it is decided not to intervene. After the blade repair is completed, the blade is subjected to quality inspection, which includes surface defect inspection and internal defect inspection, and a quality inspection report of the blade is generated after the quality inspection is completed.

[0012] Furthermore, a blade repair system based on the laser cladding simulation model comprises: Laser cladding simulation module, used to establish a laser cladding simulation model and set model parameters, set high-speed laser cladding process parameters as model variables and input them into the laser cladding simulation model, simulate the laser cladding process under different parameter combinations, and obtain the simulation results of the cladding layer; The sample characteristic analysis module is used to prepare cladding samples under different process parameter combinations according to the simulation results, perform characteristic analysis on the cladding samples, and record relevant analysis data; The coating performance prediction module establishes a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance based on the relevant analysis data, operates the performance prediction model based on the test data, and predicts the coating performance of the blade under different process parameter combinations; The process parameter optimization module is used to set performance constraints based on the specific service environment conditions of the blade, optimize the process parameter combination with the goal of optimizing the comprehensive coating performance, and determine the best preparation process parameter combination; The blade repair quality inspection module uses the best combination of preparation process parameters to perform laser cladding repair on the actual damaged blades, monitors the key working parameters of the laser cladding process in real time, and decides whether to intervene. After the repair is completed, the blades are quality inspected.

[0013] Compared with the prior art, the present invention has the following beneficial effects: by establishing a laser cladding simulation model, the laser cladding process under different process parameter combinations can be simulated without conducting actual experiments, thereby quickly screening out parameter combinations with potential optimization space, reducing the experimental cost, and providing effective theoretical guidance; by preparing cladding samples under different process parameter combinations, and establishing a mapping relationship between process parameters and coating performance after characteristic analysis, the coating performance prediction is more accurate, which helps to adjust the process parameters in a targeted manner and improve the comprehensive performance of the blade coating; in combination with the specific service environment conditions of the blade, performance constraints are set, and process parameters are optimized with the goal of achieving the best comprehensive coating performance, ensuring that the blade coating has the best performance under specific working conditions; using the best preparation process parameter combination for laser cladding repair, real-time monitoring of key working parameters, ensuring that the repair process is stable and controllable, and improving the repair quality of the blade to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a blade repair method based on a laser cladding simulation model according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] like Figure 1 As shown, a blade repair method based on a laser cladding simulation model comprises the following steps: Step S1: establishing a laser cladding simulation model, setting model parameters, setting high-speed laser cladding process parameters as model variables and inputting them into the laser cladding simulation model, simulating the laser cladding process under different parameter combinations, and obtaining simulation results of the cladding layer; Step S2: According to the simulation results, prepare cladding samples under different process parameter combinations, perform characteristic analysis on the cladding samples, and record relevant analysis data; Step S3: Based on the relevant analysis data, a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance is established, and the performance prediction model is operated based on the test data to predict the coating performance of the blade under different process parameter combinations; Step S4: In combination with the specific service environment conditions of the blade, performance constraints are set, and the process parameter combination is optimized with the goal of optimizing the comprehensive coating performance to determine the best preparation process parameter combination; Step S5: Use the best combination of preparation process parameters to perform laser cladding repair on the actual damaged blade, monitor the key working parameters of the laser cladding process in real time, and decide whether to intervene. After the repair is completed, perform quality inspection on the blade.

[0016] It should be further explained that, in the specific implementation process, the process of establishing the laser cladding simulation model and setting the model parameters includes: Acquire the geometric information of the blade, and construct a three-dimensional solid model of the blade according to the geometric information; The geometric information of the blade includes blade shape, blade length, blade circumference, blade thickness, blade profile information, blade large-scale drawing information, blade main view information and blade decomposition diagram information; the geometric information of the blade is used to reflect the physical property characteristics of the blade at the visual level; Obtain all process steps in the entire laser cladding process, establish the step operation model corresponding to each process step, and according to the step execution order of the process steps, take the first step operation model as the fusion reference body, and take the other step operation models as the individuals to be fused, and fuse each individual model to be fused to the fusion reference body in turn, so as to construct the laser cladding process model; The initial convolutional neural network framework model is constructed through deep learning technology, and the blade three-dimensional solid model and the laser cladding process model are synchronously mapped into the convolutional neural network framework model, and then the convolutional neural network framework model is constructed as a laser cladding simulation model; The model parameters corresponding to the laser cladding simulation model are set. The model parameters include blade material parameters, cladding material parameters and environmental parameters. The blade material parameters are used to characterize the material properties corresponding to the blade that needs to be repaired by cladding. The cladding material parameters are used to characterize all material properties corresponding to the materials used for cladding repair.

[0017] The blade material parameters specifically include thermophysical parameters, blade mechanical performance parameters, blade optical characteristic parameters and historical state parameters; among which, the thermophysical parameters include thermal conductivity, specific heat capacity, density, thermal expansion coefficient and melting point; the blade mechanical performance parameters include elastic modulus, Poisson's ratio, yield strength and creep parameters; the blade optical characteristic parameters are expressed as laser absorptivity; the historical state parameters include initial residual stress and microstructure; The cladding material parameters specifically include cladding powder characteristic parameters, cladding layer mechanical property parameters, solid-liquid phase change parameters and powder optical property parameters; among them, cladding powder characteristic parameters include particle size distribution and powder flow rate; cladding layer mechanical property parameters also include elastic modulus, Poisson's ratio and yield strength; solid-liquid phase change parameters include melting latent heat, solidification latent heat, surface tension and temperature coefficient; powder optical property parameters include powder absorptivity and cladding layer reflectivity; The environmental parameters specifically include heat dissipation condition related parameters, protective gas related parameters and substrate fixing condition parameters; among them, the heat dissipation condition related parameters include convection heat transfer coefficient, radiation heat transfer coefficient and ambient temperature; the protective gas related parameters include gas type and gas flow rate; the substrate fixing condition parameters include constraint method and initial preload.

[0018] It should be further explained that, in the specific implementation process, the high-speed laser cladding process parameters are set as model variables and input into the laser cladding simulation model, the laser cladding process under different parameter combinations is simulated, and the process of obtaining the simulation results of the cladding layer includes: Set the high-speed laser cladding process parameters corresponding to the laser cladding simulation model. The high-speed laser cladding process parameters include laser power, laser scanning speed and powder feeding speed. The laser power, laser scanning speed and powder feeding speed are denoted as W and V, respectively. 扫描 and V 送粉 ; Set the power safety interval corresponding to the laser power and record it as Ω[w]; Set the effective speed range corresponding to the laser scanning speed and record it as Ω[v 扫描 ]; Set the effective speed range corresponding to the powder feeding speed and record it as Ω[v 送粉 ]; Among them, the power safety interval Ω[w] is used to limit the upper and lower limits of the laser power when it is in safe operation, Ω[w] = [W-min, W-max], and the speed effective intervals of the laser scanning speed and the powder feeding speed are used to limit the speed value range when performing laser scanning or powder feeding. Only the laser scanning speed and powder feeding speed within the speed effective interval are regarded as effective operations of laser cladding, which can better; Keep W Total laser power in Ω[W], excluding W The total laser power of Ω[w] generates a laser power set; Keep V 扫描 Ω[v 扫描 ] of all laser scanning speeds, excluding V 扫描 Ω[v 扫描 ] to generate a laser scanning speed set; Keep V 送粉 Ω[v 送粉 ] of all powder feeding speeds, excluding V 送粉 Ω[v 送粉 ] to generate a powder feeding speed set; Integrate the laser power set, the laser scanning speed set and the powder feeding speed set as model variables, and input the model variables into the laser cladding simulation model. The laser cladding simulation model randomly selects one laser power, laser scanning speed and powder feeding speed from the laser power set, the laser scanning speed set and the powder feeding speed set, respectively, and integrates them as parameter combinations, thereby generating several different parameter combinations. The laser cladding simulation model simulates the laser cladding process of the blade under different parameter combinations according to the obtained several different parameter combinations, and then obtains the simulation results of the blade corresponding to different cladding layers.

[0019] It should be further explained that, in the specific implementation process, according to the simulation results, the cladding samples under different process parameter combinations are prepared, the characteristics of the cladding samples are analyzed, and the process of recording relevant analysis data includes: Constructing a sample information library, the sample information library is used to store the cladding sample ratio information corresponding to different simulation results, the cladding sample ratio information includes the ratio information between different chemical components of the cladding material, the ratio information between the substrate material and the cladding material, and the ratio parameters of the physical properties of the powder; Input any simulation result obtained by the laser cladding simulation model into the sample information library, match the cladding sample ratio information that matches the current simulation result from the sample information library, prepare the cladding sample with the working parameter combination corresponding to the current simulation result according to the cladding sample ratio information, repeat the above operation, input all simulation results into the sample information library, and then match the cladding sample ratio information corresponding to each simulation result from the sample information library, so as to prepare the cladding samples under different process parameter combinations; Perform feature analysis on all cladding samples, and obtain relevant analysis data of cladding samples including geometric feature parameters, microscopic feature parameters, mechanical property parameters and functional performance parameters through feature analysis records; Among them, the geometric characteristic parameters include the size of the cladding layer corresponding to the cladding sample, such as: melt width, melt height and dilution rate, the surface morphology of the cladding sample, such as: roughness, cracks and pores, etc. The size of the cladding layer is obtained by measuring with an optical microscope or a 3D profilometer. The microscopic characteristic parameters include the microstructural characteristics and interface bonding characteristics of the cladding sample. The microstructural characteristics include grain size, dendrite spacing and phase composition, etc., which are obtained by metallographic microscope; the interface bonding characteristics include the continuity of the interface between the substrate material and the cladding layer corresponding to the cladding sample and the element diffusion intensity, which are obtained by line scanning; Among them, the mechanical performance parameters include the hardness distribution, residual stress and bonding strength of the cladding sample. Multi-point tests are carried out along the depth direction of the corresponding cladding layer of the cladding sample to obtain the hardness distribution; the surface and internal stresses are measured by X-ray diffraction or neutron diffraction to obtain the residual stress of the cladding sample, and the bonding strength of the cladding sample is obtained through tensile test and shear test; the functional performance parameters include the wear resistance, corrosion resistance and high temperature resistance of the cladding sample.

[0020] It should be further explained that, in the specific implementation process, based on the relevant analysis data, a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance is established, and the performance prediction model is operated based on the test data. The process of predicting the coating performance of the blade under different process parameter combinations includes: Perform initial model selection based on the amount of relevant analysis data; The amount of relevant analysis data is denoted as D 相关 , which is a natural number greater than 0, unit: group; When D 相关 When <100, the linear regression equation is used to construct the model architecture corresponding to the initial performance prediction model; When 100≤D 相关 When <1000, a shallow neural network is used to construct the model architecture corresponding to the initial performance prediction model; When D 相关 When the number is greater than 10,000, deep learning is used to construct the model architecture corresponding to the initial performance prediction model; Define the input features and output targets corresponding to the initial performance prediction model; The input feature is a cladding simulation model corresponding to different process parameter combinations, which are marked as a set X; different process parameter combinations are composed of any value of laser power, any value of laser scanning speed, and any value of powder feeding speed, thereby constructing a parameter set including different laser powers, laser scanning speeds, and powder feeding speeds; Then there is a set X = <any value of laser power, any value of laser scanning speed, any value of powder feeding speed>; The output target is the different coating performance indicators corresponding to the blade, marked as Y; Then the value of Y includes blade hardness, residual stress and defect rate; The function mapping relationship between the laser power, laser scanning speed, powder feeding speed and a coating performance index in the process parameter combination is constructed. The function mapping relationship is expressed as follows: Y = a × laser power + b × laser scanning speed + c × powder feeding speed + d; Among them, a, b and c are the linear fitting coefficients of laser power, laser scanning speed and powder feeding speed respectively, and d is the tuning coefficient of the corresponding function mapping relationship between the process parameter combination and the coating performance index; Set the division ratio, and divide the relevant analysis data into a training set and a validation set according to the division ratio; The training set is input into the performance prediction model under the initial model architecture, and the corresponding model training is performed. The corresponding model evaluation indicators are obtained through the validation set. The model evaluation indicators include normalization rate, generalization rate and mean square error. When the normalization rate, generalization rate and mean square error are all within their respective preset standard ranges, the final performance prediction model is constructed based on the functional mapping relationship between the process parameter combination and the blade coating performance; Acquire test data, which are a combination of process parameters of a certain amount of data for cladding repair of blades, input the test data into a performance prediction model, and then use the performance prediction model to predict the coating performance of the blade under different process parameter combinations; The coating performance of the blade is characterized by blade hardness, residual stress and defect rate; The coating performance of the blades includes four performance levels: excellent, good, qualified and unqualified.

[0021] It should be further explained that, in the specific implementation process, the performance constraints are set in combination with the specific service environment conditions of the blade, and the process parameter combination is optimized with the goal of achieving the best comprehensive coating performance. The process of determining the best preparation process parameter combination includes: Obtaining the specific service environment conditions of the blades, which include temperature conditions, mechanical load conditions and corrosion environment conditions. The temperature conditions are used to characterize the high temperature environment in which the blades are located. The mechanical load conditions are used to characterize the extreme values ​​of centrifugal force and airflow impact that the blades can withstand, as well as the vibration fatigue that the blades can withstand. The corrosion environment conditions are used to characterize the real-time concentration of salt spray, sulfide and other chemical corrosion gases in the current service environment of the blade; The performance constraint conditions are set. The performance constraint conditions are composed of threshold intervals or endpoint thresholds corresponding to blade hardness, residual stress, defect rate, laser power, laser scanning speed, and powder feeding speed. Examples of the performance constraint conditions are as follows: ; Among them, the constraint types of residual stress ≤ -300MPa, defect rate ≤ 1%, and blade hardness ≥ 450HV0.5 are all endpoint thresholds, while W∈[1000,3000], V 扫描 ∈[40, 100] mm / s and V 送粉 The constraint types of ∈[8,20]mm / s are all threshold intervals; The coating performance of the blade is set at the excellent performance level as the goal of the best comprehensive coating performance. A multi-objective optimization model is constructed based on the goal of the best comprehensive coating performance, the specific service environment conditions of the blade, and the performance constraints. The construction process of the multi-objective optimization model is as follows: Construct a target optimization function between the specific service environment conditions of the blade and the coating performance of the blade, and record the current target optimization function as f1; Constructing a target optimization function between the performance constraint condition and the coating performance of the blade, and recording the current target optimization function as f2; The initial multi-objective optimization model is constructed by deep learning technology, and the objective optimization functions f1 and f2 are associated and mapped to the initial multi-objective optimization model. The optimal comprehensive performance of the coating is used as the model iteration stop condition of the initial multi-objective optimization model. The initial multi-objective optimization model is continuously iterated until the model prediction accuracy of the multi-objective optimization model meets the preset expected accuracy and the comprehensive performance of the coating is optimal. The current iteration is stopped and the final multi-objective optimization model is constructed. Different process parameter combinations are used as debugging data for the multi-objective optimization model. The coating index coefficient is obtained in real time by the multi-objective optimization model, and an excellent quantitative value is set. When the coating index coefficient is ≥ the excellent quantitative value, the current process parameter combination is selected as the candidate data, and all the candidate data are integrated to generate a candidate data set, and the optimization conditions corresponding to the candidate data set are set; The coating index coefficient is recorded as μ, and the coating index coefficient is expressed as follows: μ=H -n ×w1+L -n ×w2+D -n ×w3+P -n ×w4+K -n ×w5+Q -n ×w6; Among them, H -n Indicates the normal hardness after numerical normalization, L -n represents the normal residual stress after numerical normalization, D -n represents the normal defect rate after numerical normalization, P -n represents the normal laser power after numerical normalization, K -n represents the normal laser scanning speed after numerical normalization, Q -n Indicates the normal powder feeding speed after numerical normalization; w1, w2, w3, w4, w5 and w6 are the weighted weights of normal hardness, normal residual stress, normal defect rate, normal laser power, normal laser scanning speed and normal powder feeding speed, respectively, where w1+w2+w3+w4+w5+w6=1, and the values ​​of w1-w6 are set as needed, w1-w6 are all real numbers greater than 0, and the value range of the coating index coefficient μ is (0, 1); H -n , L -n , D -n , P -n , K -n , and Q -n The respective calculation formulas are as follows: H -n =(H-H -min ) / (H -max -H -min ); L -n =(L-L -min ) / (L -max -L -min ); D -n =1-[(D-D -min ) / (D -max -D -min )]; P -n =(P-P -min ) / (P -max -P -min ); K -n =(K-K -min ) / (K -max -K -min ); Q -n =(Q-Q -min ) / (Q -max -Q -min ); Among them, the above H -n , L -n , D -n , P -n , K -n , and Q -n The relevant parameters in each calculation formula are described as follows: H is the normal hardness of any value, H -min is the minimum value of normal hardness, H -max is the maximum value of normal hardness; L is the normal residual stress of any value, L -min is the minimum value of normal residual stress, L -max is the maximum value of normal residual stress; D is the normal defect rate of any value, D -min is the minimum value of the normal defect rate, D -max is the maximum value of normal defect rate; P is the normal laser power of any value, P -min is the minimum value of normal laser power, P -max is the maximum value of normal laser power; K is the normal laser scanning speed of any value, K -min is the minimum value of normal laser scanning speed, K -max is the maximum value of normal laser scanning speed; Q is the normal powder feeding speed of any value, Q -min is the minimum value of normal powder feeding speed, Q -max It is the maximum value of normal powder feeding speed; The value of the excellent quantization value is set to 0.8, which can be adjusted later according to actual conditions; When the coating index coefficient is less than the excellent quantitative value, the corresponding process parameter combination is eliminated; The optimization condition is: select the process parameter combination with the highest coating index coefficient in the selected data set, and use the process parameter combination as the optimal preparation process parameter combination.

[0022] It should be further explained that in the specific implementation process, the optimal combination of preparation process parameters is used to perform laser cladding repair on the actual damaged blade, and the key working parameters of the laser cladding process are monitored in real time to decide whether to intervene. After the repair is completed, the process of quality inspection of the blade includes: Select the actual damaged blade that needs to be repaired, use the best preparation process parameters to operate the laser cladding related equipment, and then perform laser cladding repair on the actual damaged blade, and monitor the laser cladding process in real time. Through real-time monitoring, all monitoring objects corresponding to the laser cladding repair process and the monitoring parameters of each monitoring object are obtained; The monitoring parameters of each monitoring object are integrated as the key working parameters of the laser cladding process; The monitoring objects include molten pool temperature, molten pool morphology, powder delivery and laser power; The monitoring parameters of the molten pool temperature include the peak temperature and the trough temperature; Monitoring parameters of molten pool morphology include molten pool width and molten pool height; Monitoring parameters of powder delivery include powder delivery rate and powder delivery utilization; Laser power monitoring parameters include real-time power and peak power; Decide whether to intervene in the laser cladding repair process based on key working parameters; If any of the molten pool temperature, molten pool morphology, powder delivery and laser power does not meet the standards, it is decided to adjust the laser cladding related equipment and intervene in the laser cladding repair process. Otherwise, it is decided not to intervene. Among them, the contents of judging whether the molten pool temperature, molten pool morphology, powder delivery and laser power meet the standards are as follows: For the molten pool temperature, when the peak temperature is higher than the preset upper temperature limit, or the trough temperature is lower than the preset lower temperature limit, it is judged that the current molten pool temperature does not meet the standard; otherwise, it is judged that the molten pool temperature meets the standard; For the molten pool morphology, when the molten width deviation corresponding to the molten width is greater than 10%, or the molten height deviation corresponding to the molten height is greater than 5%, it is judged that the current molten pool morphology does not meet the standard; otherwise, it is judged that the molten pool morphology meets the standard; For powder delivery, when the powder delivery rate fluctuation is greater than 5%, or the powder delivery utilization rate is lower than the preset utilization rate lower limit threshold, it is judged that the current powder delivery does not meet the standard, otherwise, it is judged that the powder delivery meets the standard; For laser power, when the power deviation of the real-time power is greater than 15%, or the peak power is greater than the preset power upper limit, it is judged that the current laser power does not meet the standard, otherwise, it is judged that the laser power meets the standard; When the blade repair is completed, the blade is subjected to quality inspection, which includes surface defect inspection and internal defect inspection, and then the corresponding surface detail information data and internal detail information data after the blade repair is completed are generated respectively, and integrated as a quality inspection report for the blade.

[0023] The present invention also provides a blade repair system based on a laser cladding simulation model, the system comprising: Laser cladding simulation module, used to establish a laser cladding simulation model and set model parameters, set high-speed laser cladding process parameters as model variables and input them into the laser cladding simulation model, simulate the laser cladding process under different parameter combinations, and obtain the simulation results of the cladding layer; The sample characteristic analysis module is used to prepare cladding samples under different process parameter combinations according to the simulation results, perform characteristic analysis on the cladding samples, and record relevant analysis data; The coating performance prediction module establishes a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance based on the relevant analysis data, operates the performance prediction model based on the test data, and predicts the coating performance of the blade under different process parameter combinations; The process parameter optimization module is used to set performance constraints based on the specific service environment conditions of the blade, optimize the process parameter combination with the goal of optimizing the comprehensive coating performance, and determine the best preparation process parameter combination; The blade repair quality inspection module uses the best combination of preparation process parameters to perform laser cladding repair on the actual damaged blades, monitors the key working parameters of the laser cladding process in real time, and decides whether to intervene. After the repair is completed, the blades are quality inspected.

[0024] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A blade repair method based on a laser cladding simulation model, characterized in that: The following steps are involved: Step S1: establishing a laser cladding simulation model, setting model parameters, setting high-speed laser cladding process parameters as model variables and inputting them into the laser cladding simulation model, simulating the laser cladding process under different parameter combinations, and obtaining simulation results of the cladding layer; Step S2: According to the simulation results, prepare cladding samples under different process parameter combinations, perform characteristic analysis on the cladding samples, and record relevant analysis data; Step S3: Based on the relevant analysis data, a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance is established, and the performance prediction model is operated based on the test data to predict the coating performance of the blade under different process parameter combinations; Step S4: In combination with the specific service environment conditions of the blade, performance constraints are set, and the process parameter combination is optimized with the goal of optimizing the comprehensive coating performance to determine the best preparation process parameter combination; Step S5: Use the best combination of preparation process parameters to perform laser cladding repair on the actual damaged blade, monitor the key working parameters of the laser cladding process in real time, and decide whether to intervene. After the repair is completed, perform quality inspection on the blade.

2. A blade repair method based on a laser cladding simulation model according to claim 1, characterized in that: The process of establishing a laser cladding simulation model and setting model parameters includes: Acquire the geometric information of the blade, and construct a three-dimensional solid model of the blade according to the geometric information; Obtain all process steps in the entire laser cladding process, establish a step operation model corresponding to each process step, and construct a laser cladding process model based on all step operation models; The initial convolutional neural network framework model is constructed through deep learning technology, and the blade three-dimensional solid model and the laser cladding process model are synchronously mapped into the convolutional neural network framework model, and then the convolutional neural network framework model is constructed as a laser cladding simulation model; The model parameters corresponding to the laser cladding simulation model are set, and the model parameters include blade material parameters, cladding material parameters and environmental parameters.

3. The blade repair method based on the laser cladding simulation model according to claim 2 is characterized in that: The process of setting high-speed laser cladding process parameters as model variables and inputting them into the laser cladding simulation model, simulating the laser cladding process under different parameter combinations, and obtaining the simulation results of the cladding layer includes: Set the high-speed laser cladding process parameters corresponding to the laser cladding simulation model. The high-speed laser cladding process parameters include laser power, laser scanning speed, and powder feeding speed, which are denoted as W and V respectively. 扫描 and V 送粉 ; Set the power safety range and record it as Ω[w]; Set the effective speed ranges corresponding to the laser scanning speed and the powder feeding speed, and record them as Ω[v 扫描 ] and Ω[v 送粉 ]; Keep W Total laser power in Ω[W], excluding W The total laser power of Ω[w] generates a laser power set; Keep V 扫描 Ω[v 扫描 ] of all laser scanning speeds, excluding V 扫描 Ω[v 扫描 ] to generate a laser scanning speed set; Keep V 送粉 Ω[v 送粉 ] of all powder feeding speeds, excluding V 送粉 Ω[v 送粉 ] to generate a powder feeding speed set; The laser power set, laser scanning speed set and powder feeding speed set are integrated as model variables and input into the laser cladding simulation model. A laser power, laser scanning speed and powder feeding speed are randomly selected from the laser power set, laser scanning speed set and powder feeding speed set and integrated as a parameter combination to generate several different parameter combinations. The laser cladding simulation model simulates the laser cladding process of the blade under different parameter combinations to obtain the simulation results of different cladding layers of the blade.

4. The blade repair method based on the laser cladding simulation model according to claim 3 is characterized in that: According to the simulation results, the process of preparing cladding samples under different process parameter combinations, performing characteristic analysis on the cladding samples, and recording relevant analysis data includes: Construct a sample information library, which is used to store cladding sample ratio information corresponding to different simulation results, input any simulation result obtained by the laser cladding simulation model into the sample information library, obtain cladding sample ratio information that matches the current simulation result and meets the standard, and prepare a cladding sample with a working parameter combination corresponding to the current simulation result according to the cladding sample ratio information; Repeat the above operation, input all simulation results into the sample information library, and then obtain the cladding sample ratio information corresponding to each simulation result, and prepare the cladding samples under different process parameter combinations; perform feature analysis on all cladding samples to obtain relevant analysis data of the cladding samples, including geometric feature parameters, microscopic feature parameters, mechanical property parameters and functional performance parameters.

5. The blade repair method based on the laser cladding simulation model according to claim 4 is characterized in that: Based on the relevant analysis data, a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance is established. The performance prediction model is operated based on the test data. The process of predicting the coating performance of the blade under different process parameter combinations includes: Perform initial model selection based on the amount of relevant analysis data, and then build the model architecture corresponding to the initial performance prediction model, and define the input features and output targets corresponding to the initial performance prediction model; The input features are different combinations of process parameters; the output targets are different coating performance indicators corresponding to the blade, including blade hardness, residual stress and defect rate; Construct a function mapping relationship between a process parameter combination and a coating performance index, set a division ratio to divide the relevant analysis data into a training set and a validation set, input the training set into the performance prediction model under the initial model architecture for model training, and obtain the corresponding model evaluation index through the validation set. When the model evaluation index meets the requirements, construct the final performance prediction model based on the function mapping relationship between the process parameter combination and the blade coating performance. The test data is obtained and input into the performance prediction model to predict the coating performance of the blade under different process parameter combinations. The coating performance of the blade includes four performance levels: excellent, good, qualified and unqualified.

6. The blade repair method based on the laser cladding simulation model according to claim 5 is characterized in that: Combined with the specific service environment conditions of the blade, performance constraints are set, and the process parameter combination is optimized with the goal of optimizing the comprehensive coating performance. The process of determining the best preparation process parameter combination includes: The specific service environment conditions of the blade include temperature conditions, mechanical load conditions and corrosion environment conditions; set performance constraints, including the threshold interval or endpoint threshold composition corresponding to blade hardness, residual stress, defect rate, laser power, laser scanning speed and powder feeding speed; The coating performance of the blade is set at an excellent performance level as the goal of the best comprehensive coating performance. A multi-objective optimization model is constructed based on the goal of the best comprehensive coating performance, the specific service environment conditions of the blade, and the performance constraints. Different process parameter combinations are used as debugging data for the multi-objective optimization model. The coating index coefficient is obtained in real time by the multi-objective optimization model, and an excellent quantitative value is set. When the coating index coefficient is ≥ the excellent quantitative value, the current process parameter combination is selected as the candidate data, and all the candidate data are integrated to generate a candidate data set, and the optimization conditions of the candidate data set are set; When the coating index coefficient is less than the excellent quantitative value, the corresponding process parameter combination is eliminated; The optimization condition is: select the process parameter combination with the highest coating index coefficient in the candidate data set as the optimal preparation process parameter combination.

7. The blade repair method based on the laser cladding simulation model according to claim 6 is characterized in that: The optimal combination of preparation process parameters is used to perform laser cladding repair on the actual damaged blade. The key working parameters of the laser cladding process are monitored in real time to decide whether to intervene. After the repair is completed, the quality inspection process of the blade includes: Use the best preparation process parameters to operate laser cladding related equipment, perform laser cladding repair on the actual damaged blade, and monitor the laser cladding process in real time to obtain all monitoring objects of the laser cladding repair process and the monitoring parameters of each monitoring object; The monitoring parameters of each monitoring object are integrated as the key working parameters of the laser cladding process; Monitoring objects include melt pool temperature, melt pool morphology, powder delivery and laser power; Decide whether to intervene in the laser cladding repair process based on key working parameters; If any of the molten pool temperature, molten pool morphology, powder delivery and laser power does not meet the standards, it is decided to adjust the laser cladding related equipment and intervene in the laser cladding repair process. Otherwise, it is decided not to intervene. After the blade repair is completed, the blade is subjected to quality inspection, which includes surface defect inspection and internal defect inspection, and a quality inspection report of the blade is generated after the quality inspection is completed.

8. A blade repair system based on a laser cladding simulation model, used to implement the blade repair method according to any one of claims 1 to 7, characterized in that: The system includes: Laser cladding simulation module, used to establish a laser cladding simulation model and set model parameters, set high-speed laser cladding process parameters as model variables and input them into the laser cladding simulation model, simulate the laser cladding process under different parameter combinations, and obtain the simulation results of the cladding layer; The sample characteristic analysis module is used to prepare cladding samples under different process parameter combinations according to the simulation results, perform characteristic analysis on the cladding samples, and record relevant analysis data; The coating performance prediction module establishes a performance prediction model of the corresponding mapping relationship between the process parameter combination and the blade coating performance based on the relevant analysis data, operates the performance prediction model based on the test data, and predicts the coating performance of the blade under different process parameter combinations; The process parameter optimization module is used to set performance constraints based on the specific service environment conditions of the blade, optimize the process parameter combination with the goal of optimizing the comprehensive coating performance, and determine the best preparation process parameter combination; The blade repair quality inspection module uses the best combination of preparation process parameters to perform laser cladding repair on the actual damaged blades, monitors the key working parameters of the laser cladding process in real time, and decides whether to intervene. After the repair is completed, the blades are quality inspected.

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