Machine learning based welding process reliability optimization acceleration method and system

Abstract

The application discloses a welding process reliability optimization acceleration method and system based on machine learning, comprising the following steps: acquiring a process parameter list, carrying out welding process test of a test piece according to the process parameter list, and obtaining historical test data; constructing a weld multi-scale numerical simulation model, carrying out numerical simulation analysis by using the weld multi-scale numerical simulation model, and obtaining simulation calculation results; performing dimension reduction processing on the simulation calculation results, inputting the data after the dimension reduction processing into multiple machine learning algorithms for training and verification, and obtaining an optimal machine learning model by selection; performing welding process reliability prediction based on the optimal machine learning model, and obtaining optimal process parameters; performing weld multi-scale numerical simulation verification on the optimal process parameters, and performing welding process verification on the optimal process parameters meeting the verification requirements until the welding process is optimized. The application realizes rapid prediction and selection of the reliability of a formed weld under different welding process parameters, and accelerates the process optimization cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of welding process optimization and acceleration, and in particular to a welding process reliability optimization and acceleration method and system based on machine learning. Background Art

[0002] With the rapid development of industrial technology in the new era, a large number of new structures, new materials, and new processes need to be converted into products with long-term reliable service. Welding, as a widely applicable, stable and reliable joining process, is widely used to connect components across various industrial sectors. Welding process optimization is one of the most important aspects of welding process research and development. The goal of welding process parameter optimization is to obtain a process with the highest reliability. Traditional optimization methods rely on welding process testing, which mainly focuses on the external dimensions and mechanical properties of the welded structure. These methods are costly, time-consuming, and difficult to evaluate. Multi-scale numerical simulation of welds, an important auxiliary tool for process optimization, can significantly reduce the number of process tests and enable reliability evaluation. It consists of four parts: phase field simulation, crystal plasticity finite element analysis, macroscopic finite element analysis, and reliability calculation. However, due to the high computational complexity and long cycle time of multi-scale numerical simulation of welds, the large number of combinations of process parameters and service conditions, and the limited computing resources and cycle time, it cannot meet the needs of process optimization. Therefore, to address the low computational efficiency of multi-scale numerical simulation of welds, a machine learning approach is adopted. Using a limited number of multi-scale numerical simulation results from welds, a model is trained to rapidly predict the reliability of welds formed with different welding process parameters, accelerating the process optimization cycle.

[0003] The domestic patent titled "A method for determining the connection configuration and process parameters of component packaging" discloses a method for obtaining solder joint reliability and inversely determining process parameters using a neural network model and simulation analysis. The method uses process parameters as input and connection configuration as output to train a neural network model, obtains the relationship between connection configuration and solder joint reliability through simulation analysis, establishes a process parameter-connection configuration-reliability inversion equation, and adjusts the process parameters based on the particle swarm algorithm until the calculated results meet the reliability requirements. However, the method belongs to the field of electronic component packaging, which is completely different from the field of fusion welding. In addition, the role of machine learning in the method is to predict the connection configuration corresponding to the process parameters, and the connection configuration is not obtained by process parameter simulation. The domestic patent titled "Weld Defect Identification Method, Device, Electronic Equipment and Storage Medium" has been published. It discloses a weld defect identification method using machine learning. The model is trained based on weld image samples and corresponding weld defect labels. The trained weld defect identification model is used to extract and fuse multi-scale features of the weld image to be identified. The weld defects are identified based on the fused features. The weld defect segmentation results in the weld image to be identified can be accurately identified, but it does not involve welding process optimization.

[0004] In view of this, this application is hereby filed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is the low efficiency of multi-scale numerical simulation calculation of welds. The purpose of the present invention is to provide a welding process reliability optimization acceleration method and system based on machine learning. By adopting machine learning means, a limited number of weld multi-scale numerical simulation calculation results are used to train the model, so as to realize the rapid prediction and optimization of the reliability of welds formed by different welding process parameters, accelerate the process optimization cycle, and provide a fast and effective means for welding process optimization.

[0006] The present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a welding process reliability optimization acceleration method based on machine learning, the method comprising:

[0008] Obtain a list of process parameters, conduct welding process tests on test pieces based on the list, and obtain historical test data;

[0009] Construct a multi-scale numerical simulation model of the weld, and use it to perform numerical simulation analysis based on historical test data to obtain simulation calculation results;

[0010] Perform dimensionality reduction on the simulation results, and input the reduced-dimensionality data into multiple machine learning algorithms for training and verification, and select the best machine learning model;

[0011] Welding process reliability prediction based on the optimal machine learning model to obtain the optimal process parameters;

[0012] The optimal process parameters are verified by multi-scale numerical simulation of the weld, and the welding process is verified for the optimal process parameters that meet the verification requirements until the welding process is optimized.

[0013] Furthermore, a process parameter list is obtained, and welding process tests of the test pieces are carried out according to the process parameter list to obtain historical test data, including:

[0014] Determine the peak current of the initial parameters based on actual engineering practice;

[0015] Based on the initial parameters, the peak current is increased or decreased to form a process parameter list;

[0016] Carry out welding process tests on test pieces according to the process parameter list to obtain process test pieces after welding;

[0017] EBSD characterization, base material tensile test and fatigue test are performed on the process test pieces after welding to obtain test results; and the test results are used as historical test data.

[0018] Furthermore, the upper and lower limits of the process parameter list envelop possible optimal parameters.

[0019] Furthermore, the weld multi-scale numerical simulation model includes a welding process finite element analysis sub-model, a phase field simulation sub-model, a crystal plasticity analysis sub-model, a working condition macro finite element analysis sub-model, and a fatigue life macro finite element analysis sub-model;

[0020] Among them, the output of the welding process finite element analysis sub-model is used as the input of the phase field simulation sub-model, the output of the phase field simulation sub-model is used as the input of the crystal plasticity analysis sub-model, the output of the crystal plasticity analysis sub-model is used as the input of the working condition macro finite element analysis sub-model, and the output of the working condition macro finite element analysis sub-model is used as the input of the fatigue life macro finite element analysis sub-model.

[0021] Furthermore, a multi-scale numerical simulation model of the weld was constructed, and numerical simulation analysis was performed using the multi-scale numerical simulation model of the weld based on historical test data to obtain simulation calculation results, including:

[0022] Construct a finite element analysis sub-model of the welding process in the finite element analysis software; calculate the residual stress field based on the finite element analysis sub-model of the welding process according to the historical test data and the first data, and extract the thermal cycle curves of the weld, heat-affected zone and base material;

[0023] Constructing a phase field simulation sub-model; performing phase field simulation based on the phase field simulation sub-model according to the residual stress field, thermal cycle curve, and second data to obtain the microstructure evolution results of the weld, heat-affected zone, and base material;

[0024] Construct a crystal plasticity analysis sub-model; based on the microstructure evolution results and the third data, perform simulations based on the crystal plasticity analysis sub-model to obtain the SN curves of the weld, heat-affected zone, and base material, as well as the stress-strain curves of the weld, heat-affected zone, and base material;

[0025] Construct a macroscopic finite element analysis submodel of the working condition; perform calculations based on the macroscopic finite element analysis submodel of the working condition according to the SN curve, stress-strain curve, residual stress field and the fourth data to obtain the stress distribution and load spectrum under the working condition;

[0026] A fatigue life macro-finite element analysis sub-model is constructed; according to the load spectrum and SN curve, calculations are performed based on the fatigue life macro-finite element analysis sub-model to obtain the fatigue life cloud diagram of the process test piece.

[0027] Furthermore, the simulation results are subjected to dimensionality reduction, and the data after dimensionality reduction is input into a variety of machine learning algorithms for training and verification, and the optimal machine learning model is obtained by selection, including:

[0028] Perform dimensionality reduction processing on the simulation calculation results to obtain data after dimensionality reduction processing;

[0029] The data after dimensionality reduction is divided into training set and validation set, and the training set is input into multiple machine learning algorithms for training to obtain fatigue training results;

[0030] The mean square error of the fatigue training results is verified based on the validation set, and the machine learning algorithm corresponding to the fatigue training result with the smallest mean square error is obtained as the optimal machine learning model.

[0031] Furthermore, the optimal process parameters are verified by multi-scale numerical simulation of the weld, and the welding process verification is carried out on the optimal process parameters that meet the verification requirements until the welding process is optimized, including:

[0032] Perform multi-scale numerical simulation of the weld seam for the optimal process parameters to obtain simulated values; and verify whether the deviation between the predicted value and the simulated value of the optimal machine learning model meets the requirements;

[0033] If the deviation meets the requirements, the welding process verification is continued for the optimal process parameters that meet the requirements to verify whether each parameter meets the requirements. If each parameter meets the requirements, the welding process has been optimized; otherwise, the multi-scale numerical simulation model of the weld is used to perform welding process optimization iterations until the welding process is optimized.

[0034] If the deviation does not meet the requirements, multiple machine learning algorithms are retrained and verified to select the best one.

[0035] In a second aspect, the present invention further provides a welding process reliability optimization acceleration system based on machine learning, the system comprising:

[0036] Welding process test unit, used to obtain a list of process parameters, conduct welding process tests on test pieces based on the list of process parameters, and obtain historical test data;

[0037] The multi-scale numerical simulation model unit is used to construct a multi-scale numerical simulation model of the weld, and to perform numerical simulation analysis using the multi-scale numerical simulation model of the weld based on historical test data to obtain simulation calculation results;

[0038] The model optimization unit is used to perform dimensionality reduction processing on the simulation calculation results, and input the data after dimensionality reduction processing into multiple machine learning algorithms for training and verification, and select the optimal machine learning model;

[0039] Welding process prediction unit, used to predict welding process reliability based on the optimal machine learning model and obtain the optimal process parameters;

[0040] The simulation and welding process verification unit conducts multi-scale numerical simulation verification of the weld for the optimal process parameters, and performs welding process verification on the optimal process parameters that meet the verification requirements until the welding process is optimized.

[0041] Furthermore, the weld multi-scale numerical simulation model includes a welding process finite element analysis sub-model, a phase field simulation sub-model, a crystal plasticity analysis sub-model, a working condition macro finite element analysis sub-model, and a fatigue life macro finite element analysis sub-model;

[0042] Among them, the output of the welding process finite element analysis sub-model is used as the input of the phase field simulation sub-model, the output of the phase field simulation sub-model is used as the input of the crystal plasticity analysis sub-model, the output of the crystal plasticity analysis sub-model is used as the input of the working condition macro finite element analysis sub-model, and the output of the working condition macro finite element analysis sub-model is used as the input of the fatigue life macro finite element analysis sub-model.

[0043] Furthermore, the simulation and welding process verification unit includes:

[0044] The model simulation verification subunit is used to perform multi-scale numerical simulation of the weld for the optimal process parameters to obtain the simulated value; and verify whether the deviation between the predicted value and the simulated value of the optimal machine learning model meets the requirements; if the deviation meets the requirements, the welding process verification is continued for the optimal process parameters that meet the requirements; if the deviation does not meet the requirements, multiple machine learning algorithms are retrained and verified to select the best one;

[0045] The welding process verification subunit is used to verify the welding process and whether the parameters meet the requirements. If the parameters meet the requirements, the welding process has been optimized; otherwise, the welding process optimization iteration is performed using the weld multi-scale numerical simulation model until the welding process is optimized.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] 1. The present invention adopts a method and system for accelerating the optimization of welding process reliability based on machine learning. It adopts machine learning and uses a limited number of multi-scale numerical simulation calculation results of welds to train the model, so as to achieve rapid prediction and optimization of the reliability of welds formed by different welding process parameters, accelerate the process optimization cycle, and provide a fast and effective means for welding process optimization.

[0048] 2. The present invention provides a welding process reliability optimization acceleration method and system based on machine learning. This method can assist the weld multi-scale numerical simulation method in achieving process optimization. If all weld multi-scale numerical simulations are used to calculate reliability, it will take a long time, be inefficient, and occupy a large amount of resources. Performing a structural component reliability calculation corresponding to a set of process parameters on a high-performance workstation takes about 1 week, including about 1 day for finite element analysis of the welding process, about 3 days for phase field simulation, about 2 days for crystal plasticity finite element analysis, and about 1 day for macro finite element analysis of working conditions and fatigue reliability. This method can reduce the multi-scale numerical simulations that originally required dozens or hundreds of times to more than ten times or even fewer, and the remaining times are replaced by machine learning model predictions, greatly improving the efficiency of process optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0050] Figure 1 This is a flow chart of the welding process reliability optimization acceleration method based on machine learning of the present invention;

[0051] Figure 2 This is a detailed flow chart of the welding process reliability optimization acceleration method based on machine learning of the present invention;

[0052] Figure 3 This is the EBSD characterization result of the weld-heat-affected zone of the present invention;

[0053] Figure 4 This is a graph showing the input and output of multi-scale numerical simulation data of the present invention;

[0054] Figure 5 This is an example of the residual stress field of the present invention;

[0055] Figure 6 This is an example of the thermal cycle curve of the present invention;

[0056] Figure 7 This is an example of the results of tissue evolution of the present invention;

[0057] Figure 8 This is an example of the stress-strain curve of the present invention;

[0058] Figure 9 This is an example of the change of plastic work per cycle with fatigue cycles corresponding to different stress amplitudes in the present invention;

[0059] Figure 10 This is an example of the stress field under the working conditions of the present invention;

[0060] Figure 11 This is an example of a fatigue life prediction cloud chart of the present invention;

[0061] Figure 12 is the mean square error of the training group and the validation group during the iterative training of the artificial neural network model of the present invention;

[0062] Figure 13 is the mean square error of the training group and the validation group during the Bayesian linear regression training iteration of the present invention;

[0063] Figure 14 This is a structural block diagram of the welding process reliability optimization acceleration system based on machine learning of the present invention. DETAILED DESCRIPTION

[0064] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0065] The welding process reliability optimization acceleration method and system based on machine learning in this application are used for accelerated optimization of welding processes. Based on the effective data obtained through welding process tests and multi-scale numerical simulation methods of welds, rapid prediction and optimization of weld reliability are achieved, providing a fast and effective means for welding process optimization.

[0066] like Figure 2 As shown in the figure, the specific steps are: Step A, welding process design and test; Step B, multi-scale numerical simulation calculation of welds; Step C, combined model training of multiple dimensionality reduction algorithms and multiple machine learning algorithms; Step D, variance comparison and selection of multiple combined models; Step E, rapid prediction and selection of weld reliability based on machine learning; Step F: multi-scale numerical simulation verification of welds; Step G: welding process test verification; repeat steps BG until the welding process is optimized.

[0067] Example 1

[0068] like Figure 1 As shown in the figure, the present invention's welding process reliability optimization acceleration method based on machine learning takes a single-tube butt weld as an example. The materials on both sides are TA16 and TA17 respectively. The welding method is automatic wire-filled argon arc welding, and the welding sequence is one primer pass and one filler pass. The present invention's method includes:

[0069] S1: Obtain a process parameter list, carry out a welding process test on the test piece according to the process parameter list, and obtain historical test data;

[0070] Step S1 specifically includes:

[0071] S11: Based on the actual project judgment and the initial parameters, the peak current is determined to be 52A;

[0072] S12: Based on the initial parameters, the peak current is increased or decreased to form a process parameter list; as shown in Table 1, Table 1 is a nine-level process parameter list, and the upper and lower limits of the process parameter list envelop possible optimal parameters.

[0073] Table 1 Process parameter list

[0074]

[0075] Note: XXX is the process test piece number, starting from 001.

[0076] S13: Conduct a welding process test on the test piece according to the process parameter list to obtain a process test piece after welding;

[0077] S14: Perform EBSD (electron backscatter diffraction technology) characterization, base material tensile test and fatigue test on the process test piece after welding to obtain test results; and use the test results as historical test data.

[0078] The test results will be used to verify the multi-scale numerical simulation model of the weld in step S2, such as Figure 3 As shown, Figure 3 This is the EBSD characterization result of the weld-heat-affected zone.

[0079] S2: Construct a multi-scale numerical simulation model of the weld, and perform numerical simulation analysis using the multi-scale numerical simulation model of the weld based on historical test data to obtain simulation calculation results;

[0080] The multi-scale numerical simulation model of the weld includes a welding process finite element analysis sub-model, a phase field simulation sub-model, a crystal plasticity analysis sub-model, a working condition macro finite element analysis sub-model, and a fatigue life macro finite element analysis sub-model. Among them, the output of the welding process finite element analysis sub-model is used as the input of the phase field simulation sub-model, the output of the phase field simulation sub-model is used as the input of the crystal plasticity analysis sub-model, the output of the crystal plasticity analysis sub-model is used as the input of the working condition macro finite element analysis sub-model, and the output of the working condition macro finite element analysis sub-model is used as the input of the fatigue life macro finite element analysis sub-model.

[0081] like Figure 4 As shown in Figure 1, each sub-model describes in detail the input and output relationship of the data between each part. The blue data box indicates that the data is obtained by numerical simulation calculation, and the data in the white data box comes from historical test data in step 1, material parameter database or literature and other channels.

[0082] It should be noted that the welding process finite element analysis submodel, the working condition macro finite element analysis submodel and the fatigue life macro finite element analysis submodel are macro analysis, the phase field simulation submodel is micro analysis, and the crystal plasticity analysis submodel is meso analysis, and the welding seam multi-scale numerical simulation analysis is comprehensively realized.

[0083] The step S2 specifically comprises:

[0084] S21: constructing a welding process finite element analysis submodel in finite element analysis software; calculating a residual stress field based on the welding process finite element analysis submodel according to historical test data and first data, and extracting thermal cycle curves of the weld, the heat-affected zone and the base material; that is, input of the welding process finite element analysis submodel: process parameters, model structure, material thermal conductivity, specific heat capacity and thermal expansion coefficient; output of the welding process finite element analysis submodel: residual stress field and thermal cycle curves.

[0085] Specifically, a weld structure model is established in finite element analysis software, a welding heat source model is established according to process parameters and a motion trajectory is defined, and a residual stress field is calculated, as shown in Figure 5 , and thermal cycle curves are obtained by extracting temperature histories of a plurality of points in the weld, the heat-affected zone and the base material, as shown in Figure 6 .

[0086] S22: constructing a phase field simulation submodel; performing phase field simulation based on the phase field simulation submodel according to the residual stress field, the thermal cycle curves and second data, and obtaining microstructure evolution results of the weld, the heat-affected zone and the base material; that is, input of the phase field simulation submodel: residual stress field and thermal cycle curves output by the step S21, and phase change driving force curve, phase fraction curve and solid phase fraction curve; output of the phase field simulation submodel: microstructure evolution results.

[0087] Specifically, a solidification structure and solid state phase change phase field model is established, phase field simulation is performed according to the thermal cycle curves obtained by finite element analysis of the welding process, and microstructure evolution results are obtained, as shown in Figure 7 . The phase field simulation model is checked according to EBSD characterization data of a process test piece.

[0088] S23: constructing a crystal plasticity analysis submodel; performing simulation based on the crystal plasticity analysis submodel according to the microstructure evolution results and third data, and obtaining S-N curves of the weld, the heat-affected zone and the base material and stress-strain curves of the weld, the heat-affected zone and the base material; that is, input of the crystal plasticity analysis submodel: microstructure evolution results and elastic constant matrix, crystal face index and crystal direction index; output of the crystal plasticity analysis submodel: S-N curves and stress-strain curves.

[0089] Specifically, a crystal plasticity analysis sub-model was established. Based on the microstructural evolution results obtained by the phase field simulation sub-model, the orientation data of 100 grains were extracted to establish a model. The inverse pole figures of the microstructural model after modeling and the microstructural evolution results before extraction were plotted to verify the consistency between the two. Tensile and tension-compression fatigue simulations were performed on the established crystal plasticity analysis sub-model, and the stress-strain curves of the local microstructure were calculated as follows: Figure 8 As shown, the SN curve is composed of Figure 9 The plastic work per cycle corresponding to the different stress amplitudes shown is fitted with the life cycle values ​​calculated by fatigue cycles. The crystal plasticity analysis sub-model is calibrated based on the results of the tensile test and fatigue test of the base material of the process test piece. Figure 8 The horizontal axis represents the true strain value, and the vertical axis represents the true stress value; Figure 9 The horizontal axis represents the number of cyclic loading, and the vertical axis represents the plastic work per week.

[0090] S24: Construct a macroscopic finite element analysis sub-model of the working condition; perform calculations based on the macroscopic finite element analysis sub-model of the working condition according to the SN curve, stress-strain curve, residual stress field and the fourth data to obtain the stress distribution and load spectrum under the working condition; that is, the input of the macroscopic finite element analysis sub-model of the working condition: SN curve and stress-strain curve, residual stress field, working condition, JC constitutive parameters; the output of the macroscopic finite element analysis sub-model of the working condition: stress distribution and load spectrum under the working condition.

[0091] Specifically, taking the tensile working condition as an example, the welding residual stress field obtained by the finite element analysis sub-model of the welding process is imported into the initial state, and the weld joint is divided into three parts: weld, heat-affected zone, and base material. The stress-strain curves obtained by the crystal plasticity analysis sub-model are assigned to each part. The working condition stress field is calculated as follows: Figure 10 In addition, the load spectrum is obtained by adding the time variation factor to the stress field.

[0092] S25: Construct a fatigue life macro-finite element analysis sub-model; according to the load spectrum and SN curve, calculate based on the fatigue life macro-finite element analysis sub-model to obtain the fatigue life cloud map of the process test piece; that is, the input of the fatigue life macro-finite element analysis sub-model: load spectrum and SN curve; the output of the fatigue life macro-finite element analysis sub-model: fatigue life cloud map of the process test piece.

[0093] Specifically, an appropriate fatigue life macro finite element analysis sub-model is selected, such as the Brown-Miller model for predicting metal fatigue life at room temperature, and the SN curves of the weld, heat-affected zone, and base metal obtained by the crystal plasticity analysis sub-model are imported. Different regions of the joint have different fatigue properties. The fatigue life prediction cloud diagram corresponding to the stress field and residual stress field under different working conditions is calculated, for example Figure 11 as shown.

[0094] S3: Dimensionality reduction is performed on the simulation results, and the data after dimensionality reduction is input into multiple machine learning algorithms for training and verification to obtain an optimal machine learning model;

[0095] Step S3 specifically includes:

[0096] S31: Dimensionality reduction is performed on the simulation results to obtain data after dimensionality reduction;

[0097] Specifically, data dimensionality reduction methods such as principal component analysis, linear discriminant analysis, kernel principal component analysis, isometric mapping, local linear embedding, random neighbor embedding, t-SNE, etc. can be used to process data in the simulation results.

[0098] S32: The data after dimensionality reduction is divided into a training set and a verification set, and the training set is input into multiple machine learning algorithms for training to obtain a fatigue training result;

[0099] Specifically, the multiple machine learning algorithms include multiple linear regression, locally weighted linear regression, polynomial regression, Lasso regression & Ridge regression, elastic network regression, Bayesian ridge regression, Huber regression, artificial neural network, KNN, SVM, CART tree, random forest, GBDT, XGBOOST, time series-ARIMA, and hidden Markov.

[0100] S33: The mean square error of the fatigue training result is verified based on the verification set, and the machine learning algorithm corresponding to the fatigue training result with the smallest mean square error is taken as the optimal machine learning model.

[0101] The following lists the training and verification mean square errors of two models with better prediction effects. The artificial neural network model includes two closely connected hidden layers, and the output layer can return a single continuous value. The mean square error of the verification set as the loss function has a trend of oscillation divergence with the increase of the number of iterations, although the mean square error is small, but it is not as stable as the Bayesian linear regression model. For example, Figure 12 and Figure 13 as shown, Figure 12 is the mean square error of the training set and the verification set during the training iteration process of the artificial neural network model, Figure 13 is the mean square error of the training set and the verification set during the training iteration process of the Bayesian linear regression. Figure 12 and Figure 13 The horizontal coordinate represents the number of iterations, and the vertical coordinate represents the mean square error.

[0102] S4: Based on the optimal machine learning model, the welding process reliability is predicted to obtain optimal process parameters;

[0103] Specifically, a large number of process parameters within the upper and lower limits of the proposed process parameter list are imported into the optimal machine learning model, and the process parameters with the best reliability are selected through machine learning prediction.

[0104] S5: Perform multi-scale numerical simulation verification of the weld for the optimal process parameters, and perform welding process verification for the optimal process parameters that meet the verification requirements until the welding process is optimized.

[0105] Step S5 specifically includes:

[0106] S51: Perform multi-scale numerical simulation of the weld for the optimal process parameters to obtain simulated values; and verify whether the deviation between the predicted value and the simulated value of the optimal machine learning model meets the preset requirements;

[0107] S52: If the deviation meets the preset requirements, the welding process verification is continued for the optimal process parameters that meet the requirements to verify whether the parameters such as size and performance meet the requirements. If the parameters meet the requirements, the welding process has been optimized; otherwise, the welding process optimization iteration is performed using the weld multi-scale numerical simulation model until the welding process is optimized;

[0108] S53: If the deviation does not meet the requirements, retrain and verify multiple machine learning algorithms to select the best one.

[0109] The present invention involves welding process design and testing, multi-scale numerical simulation of welds, model training using a combination of multiple dimensionality reduction algorithms and multiple machine learning algorithms, variance comparison and optimization of the combined models, rapid prediction and optimization of weld reliability based on machine learning, verification through multi-scale numerical simulation of welds, and welding process testing. These steps are repeated until the welding process is optimized. This method addresses the low computational efficiency of multi-scale numerical simulation of welds by employing machine learning. Using a limited number of multi-scale numerical simulation results, the model is trained to rapidly predict the reliability of welds formed with different welding process parameters, accelerating the process optimization cycle.

[0110] Example 2

[0111] like Figure 14 As shown, the difference between this embodiment and embodiment 1 is that this embodiment provides a welding process reliability optimization acceleration system based on machine learning, which corresponds one-to-one with the welding process reliability optimization acceleration method based on machine learning in embodiment 1; the system includes:

[0112] Welding process test unit, used to obtain a list of process parameters, conduct welding process tests on test pieces based on the list of process parameters, and obtain historical test data;

[0113] The multi-scale numerical simulation model unit is used to construct a multi-scale numerical simulation model of the weld, and to perform numerical simulation analysis using the multi-scale numerical simulation model of the weld based on historical test data to obtain simulation calculation results;

[0114] The model optimization unit is used to perform dimensionality reduction processing on the simulation calculation results, and input the data after dimensionality reduction processing into multiple machine learning algorithms for training and verification, and select the optimal machine learning model;

[0115] Welding process prediction unit, used to predict welding process reliability based on the optimal machine learning model and obtain the optimal process parameters;

[0116] The simulation and welding process verification unit conducts multi-scale numerical simulation verification of the weld for the optimal process parameters, and performs welding process verification on the optimal process parameters that meet the verification requirements until the welding process is optimized.

[0117] As a further implementation, the weld multi-scale numerical simulation model includes a welding process finite element analysis sub-model, a phase field simulation sub-model, a crystal plasticity analysis sub-model, a working condition macro finite element analysis sub-model and a fatigue life macro finite element analysis sub-model;

[0118] Among them, the output of the welding process finite element analysis sub-model is used as the input of the phase field simulation sub-model, the output of the phase field simulation sub-model is used as the input of the crystal plasticity analysis sub-model, the output of the crystal plasticity analysis sub-model is used as the input of the working condition macro finite element analysis sub-model, and the output of the working condition macro finite element analysis sub-model is used as the input of the fatigue life macro finite element analysis sub-model.

[0119] As a further implementation, the simulation and welding process verification unit includes:

[0120] The model simulation verification subunit is used to perform multi-scale numerical simulation of the weld for the optimal process parameters to obtain the simulated value; and verify whether the deviation between the predicted value and the simulated value of the optimal machine learning model meets the requirements; if the deviation meets the requirements, the welding process verification is continued for the optimal process parameters that meet the requirements; if the deviation does not meet the requirements, multiple machine learning algorithms are retrained and verified to select the best one;

[0121] The welding process verification subunit is used to verify the welding process and whether the parameters meet the requirements. If the parameters meet the requirements, the welding process has been optimized; otherwise, the welding process optimization iteration is performed using the weld multi-scale numerical simulation model until the welding process is optimized.

[0122] Among them, the execution process of each unit can be executed according to the process steps of the welding process reliability optimization acceleration method based on machine learning in Example 1, and will not be repeated one by one in this embodiment.

[0123] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0124] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 steps in the process. 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.

[0125] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work 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 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A welding process reliability optimization acceleration method based on machine learning, characterized in that: The method includes: Obtaining a process parameter list, conducting a welding process test on a test piece according to the process parameter list, and obtaining historical test data; A multi-scale numerical simulation model of the weld is constructed, and according to the historical test data, a numerical simulation analysis is performed using the multi-scale numerical simulation model of the weld to obtain simulation calculation results; the multi-scale numerical simulation model of the weld includes a welding process finite element analysis sub-model, a phase field simulation sub-model, a crystal plasticity analysis sub-model, a working condition macro finite element analysis sub-model, and a fatigue life macro finite element analysis sub-model; wherein the output of the welding process finite element analysis sub-model is used as the input of the phase field simulation sub-model, the output of the phase field simulation sub-model is used as the input of the crystal plasticity analysis sub-model, the output of the crystal plasticity analysis sub-model is used as the input of the working condition macro finite element analysis sub-model, and the output of the working condition macro finite element analysis sub-model is used as the input of the fatigue life macro finite element analysis sub-model; The simulation calculation results are subjected to dimensionality reduction processing, and the data after dimensionality reduction processing are input into a plurality of machine learning algorithms for training and verification, and an optimal machine learning model is obtained by selecting the best one; the method comprises: performing dimensionality reduction processing on the simulation calculation results to obtain the data after dimensionality reduction processing; dividing the data after dimensionality reduction processing into a training set and a verification set, and inputting the training set into a plurality of machine learning algorithms for training to obtain fatigue training results; verifying the mean square error of the fatigue training results based on the verification set, and obtaining the machine learning algorithm corresponding to the fatigue training result with the smallest mean square error as the optimal machine learning model; Predicting welding process reliability based on the optimal machine learning model to obtain optimal process parameters; The optimal process parameters are verified by multi-scale numerical simulation of the weld, and the welding process verification is performed on the optimal process parameters that meet the verification requirements until the welding process is optimized.

2. The welding process reliability optimization acceleration method based on machine learning according to claim 1 is characterized in that: Obtain a list of process parameters, conduct welding process tests on the test piece according to the list of process parameters, and obtain historical test data, including: Determine the peak current of the initial parameters based on actual project conditions; Based on the initial parameters, the peak current is adjusted to form a process parameter list; Conducting a welding process test on the test piece according to the process parameter list to obtain a welded process test piece; The welded process test piece is subjected to EBSD characterization, a base material tensile test, and a fatigue test to obtain test results; and the test results are used as historical test data.

3. The welding process reliability optimization acceleration method based on machine learning according to claim 2 is characterized in that: The upper and lower limits of the process parameter list envelop the possible optimal parameters.

4. The welding process reliability optimization acceleration method based on machine learning according to claim 1 is characterized in that: Constructing a multi-scale numerical simulation model of the weld, and performing numerical simulation analysis using the multi-scale numerical simulation model of the weld based on the historical test data to obtain simulation calculation results, including: Constructing a finite element analysis sub-model for the welding process; calculating the residual stress field based on the finite element analysis sub-model for the welding process according to the historical test data and the first data, and extracting thermal cycle curves of the weld, the heat-affected zone, and the base material; Constructing a phase field simulation sub-model; performing phase field simulation based on the phase field simulation sub-model according to the residual stress field, thermal cycle curve and second data to obtain microstructure evolution results of the weld, heat affected zone and base material; Constructing a crystal plasticity analysis sub-model; performing simulation based on the crystal plasticity analysis sub-model according to the microstructure evolution results and the third data to obtain SN curves of the weld, heat-affected zone, and base material, as well as stress-strain curves of the weld, heat-affected zone, and base material; Constructing a macroscopic finite element analysis submodel of the working condition; performing calculations based on the macroscopic finite element analysis submodel of the working condition according to the SN curve, the stress-strain curve, the residual stress field, and the fourth data to obtain the stress distribution and load spectrum under the working condition; A fatigue life macro finite element analysis sub-model is constructed; and according to the load spectrum and the SN curve, a fatigue life macro finite element analysis sub-model is used to perform calculations to obtain a fatigue life cloud diagram of the process test piece.

5. The welding process reliability optimization acceleration method based on machine learning according to claim 1 is characterized in that: Perform multi-scale numerical simulation verification of the weld for the optimal process parameters, and perform welding process verification on the optimal process parameters that meet the verification requirements until the welding process is optimized, including: Performing a multi-scale numerical simulation of the weld on the optimal process parameters to obtain a simulated value; and verifying whether the deviation between the predicted value of the optimal machine learning model and the simulated value meets the requirements; If the deviation meets the requirements, the welding process verification is continued for the optimal process parameters that meet the requirements to verify whether each parameter meets the requirements. If each parameter meets the requirements, the welding process has been optimized; otherwise, the multi-scale numerical simulation model of the weld is used to perform welding process optimization iterations until the welding process is optimized; If the deviation does not meet the requirements, multiple machine learning algorithms are retrained and verified to select the best one.

6. A welding process reliability optimization acceleration system based on machine learning, characterized in that: The system includes: A welding process test unit is used to obtain a process parameter list, carry out a welding process test on a test piece according to the process parameter list, and obtain historical test data; A multi-scale numerical simulation model unit is used to construct a multi-scale numerical simulation model of a weld, and to perform numerical simulation analysis using the multi-scale numerical simulation model of the weld based on the historical test data to obtain simulation calculation results; A model optimization unit is used to perform dimensionality reduction processing on the simulation calculation results, and input the data after dimensionality reduction processing into multiple machine learning algorithms for training and verification, so as to obtain the optimal machine learning model; A welding process prediction unit, configured to predict the reliability of the welding process based on the optimal machine learning model and obtain optimal process parameters; A simulation and welding process verification unit performs multi-scale numerical simulation verification of the weld for the optimal process parameters, and performs welding process verification for the optimal process parameters that meet the verification requirements until the welding process is optimized; The multi-scale numerical simulation model of the weld includes a finite element analysis sub-model of the welding process, a phase field simulation sub-model, a crystal plasticity analysis sub-model, a working condition macro finite element analysis sub-model and a fatigue life macro finite element analysis sub-model; The output of the welding process finite element analysis sub-model is used as the input of the phase field simulation sub-model, the output of the phase field simulation sub-model is used as the input of the crystal plasticity analysis sub-model, the output of the crystal plasticity analysis sub-model is used as the input of the working condition macro finite element analysis sub-model, and the output of the working condition macro finite element analysis sub-model is used as the input of the fatigue life macro finite element analysis sub-model; The execution process of the model optimization unit includes: The simulation calculation results are subjected to dimensionality reduction processing to obtain data after dimensionality reduction processing; the data after dimensionality reduction processing are divided into a training set and a validation set, and the training set is input into multiple machine learning algorithms for training to obtain fatigue training results; the mean square error of the fatigue training results is verified based on the validation set, and the machine learning algorithm corresponding to the fatigue training result with the smallest mean square error is obtained as the optimal machine learning model.

7. The welding process reliability optimization acceleration system based on machine learning according to claim 6 is characterized in that: The simulation and welding process verification unit includes: A model simulation verification subunit is used to perform multi-scale numerical simulation of the weld for the optimal process parameters to obtain simulation values; and verify whether the deviation between the predicted value of the optimal machine learning model and the simulation value meets the requirements; if the deviation meets the requirements, then continue to verify the welding process for the optimal process parameters that meet the requirements; if the deviation does not meet the requirements, then retrain and verify multiple machine learning algorithms to select the best one; The welding process verification subunit is used to verify the welding process and verify whether each parameter meets the requirements. If each parameter meets the requirements, the welding process has been optimized; otherwise, the welding process optimization iteration is performed using the multi-scale numerical simulation model of the weld until the welding process is optimized.

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