Ultrasonic impact treatment process parameter optimization method, device and electronic equipment

Abstract

The present invention relates to the field of welding process technology, and in particular to a method, device, and electronic device for optimizing ultrasonic impact treatment process parameters. The method comprises: obtaining welding simulation data of a welded part and multiple sets of process parameters of the ultrasonic impact treatment process, wherein the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter, and impact time; performing finite element simulation on each set of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data at the weld under different process parameters; using a trained ultrasonic impact treatment prediction model to predict the residual stress and compressive stress depth data at the weld, and optimizing at least one set of process parameters of the ultrasonic impact treatment process based on the residual stress and compressive stress depth data at the weld. Thus, the method solves the problems in the related art of insufficiently optimizing the process parameters of the ultrasonic treatment process and ignoring the effect of the cooling temperature on the residual stress of the weld after ultrasonic impact treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of welding processes, and in particular to a method, a device and an electronic device for optimizing process parameters of ultrasonic impact treatment. Background Art

[0002] The vacuum chamber is a key component of a nuclear fusion reactor, providing a safe environment for maintaining high-temperature plasma. Due to its large size, the vacuum chamber is typically assembled in modular sections during manufacturing. This assembly requires extensive welding processes, which can generate significant residual stress and deformation at the weld joints, significantly impacting the fatigue strength and service life of the vacuum chamber material. Ultrasonic impact treatment, as a surface strengthening technology, can be used during vacuum chamber manufacturing and assembly to reduce residual stress in weld joints.

[0003] The equipment process parameters (impact frequency, impact amplitude, impact needle diameter) of the ultrasonic impact treatment equipment currently produced and sold on the market are mostly factory settings and cannot be adjusted. The process parameters during the ultrasonic impact treatment process are also less concerned.

[0004] The relevant technologies mainly include residual stress prediction methods under different ultrasonic impact treatment process parameters based on machine learning, as well as parameter optimization methods for individual parameters. However, the consideration of process parameters is not comprehensive enough, resulting in the difficulty in fully tapping the process potential after optimization, and it is difficult to obtain the best combination of processing parameters, which in turn leads to the ultrasonic impact treatment process failing to achieve the optimal effect in reducing residual stress and improving component performance. Summary of the Invention

[0005] The present invention provides a method, device and electronic equipment for optimizing process parameters of ultrasonic impact treatment to solve the problems in related technologies such as incomplete optimization of process parameters of ultrasonic treatment process and neglect of the influence of cooling temperature on residual stress of welded parts after ultrasonic impact treatment.

[0006] The first aspect of the present invention provides an embodiment of a method for optimizing ultrasonic impact treatment process parameters, comprising the following steps: obtaining welding simulation data of a welded part and multiple sets of process parameters of an ultrasonic impact treatment process, wherein the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter, and impact time; performing finite element simulation on each set of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, and generating a training data set based on multiple sets of process parameters, welding simulation data, and residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; using the training data set to train a pre-constructed ultrasonic impact treatment prediction model, wherein the input of the ultrasonic impact treatment prediction model is multiple sets of process parameters and welding simulation data, and the output of the ultrasonic impact treatment prediction model is residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; using the trained ultrasonic impact treatment prediction model to predict residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, and optimizing at least one set of process parameters of the ultrasonic impact treatment process based on the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment.

[0007] Optionally, the ultrasonic impact treatment prediction model includes an input layer, multiple hidden layers, an output layer and a loss function, wherein the input layer is used to input process parameters and welding simulation data; the multiple hidden layers are used to perform feature extraction and nonlinear transformation on the process parameters and welding simulation data; the output layer is used to output the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; the loss function is used to calculate the prediction error based on the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment and the true value in the training data set, and update the model parameters of the ultrasonic impact treatment prediction model based on the prediction error until the loss function is minimized.

[0008] Optionally, obtaining welding simulation data of the welded part and multiple sets of process parameters of the ultrasonic impact treatment process includes: constructing a welding finite element model; performing multi-layer and multi-pass welding simulation on the welded part using the welded part finite element model to obtain welding simulation data of the welded part.

[0009] Optionally, the welding simulation data includes: a post-weld deformation model, a post-weld temperature field before ultrasonic impact treatment, and a post-weld residual stress field. Finite element simulation is performed on each set of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, including: constructing an ultrasonic impact treatment finite element model based on the post-weld deformation model, and loading the post-weld temperature field and post-weld residual stress field before ultrasonic impact treatment as predefined fields into the ultrasonic impact treatment finite element model, using the ultrasonic impact treatment finite element model to perform ultrasonic impact simulation on the weld under different process parameters, and obtaining residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0010] Optionally, a finite element model of the welded part is used to perform multi-layer and multi-pass welding simulation on the welded part to obtain welding simulation data of the welded part, including: performing failure treatment on the weld area in the welded part so that the weld area does not participate in the finite element analysis; activating each weld in turn to participate in the finite element analysis, and performing thermal analysis simulation on the current weld participating in the finite element analysis to obtain temperature field distribution data of the current weld; using the temperature field distribution data of the current weld as the initial condition, performing force analysis on the current weld to obtain corresponding stress field distribution and deformation data, until all weld analyses are completed to obtain welding simulation data of the welded part.

[0011] Optionally, an ultrasonic impact treatment finite element model is used to perform ultrasonic impact simulation on the weld under different process parameters to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, including: cooling the weldment to the cooling temperature in the process parameter combination through the external temperature of the heat exchange condition, and setting the external temperature as the target temperature for temperature-dynamic coupling simulation; setting impact needles of different diameters, and applying a displacement load perpendicular to the weld surface and controlled by the amplitude to the impact needle, and setting the impact frequency and impact amplitude in the amplitude; setting the time for ultrasonic impact analysis, and applying a velocity load parallel to the weld direction to the impact needle to ensure that the impact on the weld is completed within the set time, so as to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0012] Optionally, at least one set of process parameters of the ultrasonic impact treatment process is optimized based on the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, including: setting an optimization target for the ultrasonic impact treatment process parameters, inputting the optimization target, at least one set of ultrasonic impact treatment process parameters and the corresponding residual stress and compressive stress depth data at the weld after ultrasonic impact treatment into a target algorithm; using the target algorithm to find the optimal solution, and optimizing at least one set of process parameters of the ultrasonic impact treatment process based on the optimization results.

[0013] Optionally, before using the training data set to train the pre-built ultrasonic impact processing prediction model, it also includes: determining the disturbance amplitude of the training data in the training data set based on the prediction error; generating new training data based on the disturbance amplitude, and expanding the training data set based on the new training data; and using the expanded training data set to train the pre-built ultrasonic impact processing prediction model.

[0014] The second aspect of the present invention provides an ultrasonic impact treatment process parameter optimization device, including: an acquisition module for acquiring welding simulation data and multiple groups of process parameters, including: acquiring welding simulation data of welded parts and multiple groups of process parameters of ultrasonic impact treatment process, wherein the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter and impact time; a simulation module for performing finite element simulation on each group of process parameters, including: performing finite element simulation on each group of process parameters based on welding simulation data, obtaining residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, and obtaining residual stress and compressive stress depth data at the weld after ultrasonic impact treatment according to multiple groups of process parameters, welding simulation data and ultrasonic impact treatment. The invention relates to an ultrasonic impact treatment prediction model, wherein the training data set is used to train a pre-built ultrasonic impact treatment prediction model, wherein the input of the ultrasonic impact treatment prediction model is multiple sets of process parameters and welding simulation data, and the output of the ultrasonic impact treatment prediction model is the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment; an optimization module is used to perform finite element simulation on each set of process parameters, including: using the trained ultrasonic impact treatment prediction model to predict the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment, and optimizing at least one set of process parameters of the ultrasonic impact treatment process according to the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment.

[0015] Optionally, the ultrasonic impact treatment prediction model includes an input layer, multiple hidden layers, an output layer and a loss function, wherein the input layer is used to input process parameters and welding simulation data; the multiple hidden layers are used to perform feature extraction and nonlinear transformation on the process parameters and welding simulation data; the output layer is used to output the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; the loss function is used to calculate the prediction error based on the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment and the true value in the training data set, and update the model parameters of the ultrasonic impact treatment prediction model based on the prediction error until the loss function is minimized.

[0016] Optionally, the acquisition module is further used to: construct a welding finite element model; perform multi-layer and multi-pass welding simulation on the welded part using the welded part finite element model to obtain welding simulation data of the welded part.

[0017] Optionally, the welding simulation data includes: a post-weld deformation model, a post-weld temperature field before ultrasonic impact treatment, and a post-weld residual stress field. The simulation module is further used to: construct an ultrasonic impact treatment finite element model based on the post-weld deformation model, and load the post-weld temperature field and post-weld residual stress field before ultrasonic impact treatment as predefined fields into the ultrasonic impact treatment finite element model, and use the ultrasonic impact treatment finite element model to perform ultrasonic impact simulation on the weld under different process parameters to obtain the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0018] Optionally, the acquisition module is further used to: perform failure treatment on the weld area in the welded part so that the weld area does not participate in the finite element analysis; activate each weld in turn to participate in the finite element analysis, and perform thermal analysis simulation on the current weld participating in the finite element analysis to obtain the temperature field distribution data of the current weld; use the temperature field distribution data of the current weld as the initial condition, perform force analysis on the current weld to obtain the corresponding stress field distribution and deformation data, until all weld analyses are completed, and obtain the welding simulation data of the welded part.

[0019] Optionally, the simulation module is further used to: cool the weldment to the cooling temperature in the process parameter combination through the external temperature of the heat exchange condition, and set the external temperature as the target temperature for temperature-dynamic coupling simulation; set impact needles of different diameters, and apply a displacement load perpendicular to the weld surface and controlled by the amplitude to the impact needle, and set the impact frequency and impact amplitude in the amplitude; set the time for ultrasonic impact analysis, apply a velocity load parallel to the weld direction to the impact needle, and ensure that the impact on the weld is completed within the set time, so as to obtain the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0020] Optionally, the optimization module is further used to: set the optimization target of the ultrasonic impact treatment process parameters, input the optimization target, at least one set of ultrasonic impact treatment process parameters and the corresponding residual stress and compressive stress depth data at the weld after ultrasonic impact treatment into the target algorithm; use the target algorithm to find the optimal solution, and optimize at least one set of process parameters of the ultrasonic impact treatment process according to the optimization results.

[0021] Optionally, it also includes: a disturbance module, which is used to determine the disturbance amplitude of the training data in the training data set based on the prediction error before using the training data set to train the pre-built ultrasonic impact processing prediction model; generate new training data based on the disturbance amplitude, and expand the training data set based on the new training data; and use the expanded training data set to train the pre-built ultrasonic impact processing prediction model.

[0022] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to perform the ultrasonic impact treatment process parameter optimization method as described in the above embodiment.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program or instruction stored thereon, and the computer program or instruction is executed by a processor to perform the ultrasonic impact treatment process parameter optimization method as described in the above embodiment.

[0024] Therefore, the present invention has at least the following beneficial effects:

[0025] The embodiment of the present invention can obtain welding simulation data of a welded part and multiple sets of process parameters of an ultrasonic impact treatment process, and perform finite element simulation on each set of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters. A training data set is generated based on the multiple sets of process parameters, welding simulation data, and residual stress and compressive stress depth data of the weld after ultrasonic impact treatment. The influence of multiple process parameters on the residual stress and compressive stress depth of the weld after ultrasonic impact treatment is comprehensively considered, and a pre-built ultrasonic impact treatment prediction model is trained using the training data set. The trained ultrasonic impact treatment prediction model is used to predict the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment, and at least one set of process parameters of the ultrasonic impact treatment process is optimized based on the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment. By comprehensively considering the influence of various process parameters on the ultrasonic impact treatment process, especially the influence of cooling temperature on the residual stress after the ultrasonic impact treatment process, the process parameters of the ultrasonic impact treatment process are comprehensively optimized to minimize residual stress and reduce the service risk of vacuum chamber components. This can also provide a reference for parameter adjustability of ultrasonic impact treatment equipment. Thus, the technical problems in related technologies such as incomplete optimization of process parameters for ultrasonic treatment process and neglect of the influence of cooling temperature on residual stress of welded parts after ultrasonic impact treatment are solved.

[0026] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 Flow chart of a method for optimizing process parameters of ultrasonic impact treatment according to an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of a finite element model used in an ultrasonic impact treatment simulation according to an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of residual stress distribution before ultrasonic impact treatment according to an embodiment of the present invention;

[0031] Figure 4 A schematic diagram of residual stress distribution after ultrasonic impact treatment according to an embodiment of the present invention;

[0032] Figure 5 A flowchart of adaptive training data enhancement according to an embodiment of the present invention;

[0033] Figure 6 Flowchart of the NSGA-II algorithm provided according to an embodiment of the present invention;

[0034] Figure 7 An execution diagram of a method for optimizing process parameters of ultrasonic impact treatment according to a specific embodiment of the present invention;

[0035] Figure 8 This is an example diagram of an ultrasonic impact treatment process parameter optimization device provided according to an embodiment of the present invention;

[0036] Figure 9 A schematic structural diagram of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0038] Before describing the solution of the present invention, the research and disadvantages of the related art on ultrasonic impact treatment process are introduced.

[0039] 1. Most numerical simulations of ultrasonic impact treatment in related technologies are based on ideal conditions and room temperature, without considering the temperature-ultrasonic impact coupling simulation. The residual stress is closely related to the temperature and cooling rate of the weld, which is one of the influencing parameters that needs to be studied, and is prone to large deviations from the actual situation.

[0040] 2. The input parameters of the residual stress prediction model for ultrasonic impact treatment process parameters in related technologies are not comprehensive, and the output parameters are single-objective optimization of the residual stress peak value, without considering other indicators.

[0041] 3. The residual stress prediction method of related technologies is based on the training and calculation of a complex function model, which may lead to overfitting and thus poor generalization ability.

[0042] 4. There is a highly nonlinear relationship between residual stress and ultrasonic impact treatment process parameters. Most of the ultrasonic impact treatment process parameter optimization algorithms in related technologies are based on traditional machine learning algorithms, which have limited modeling capabilities for nonlinear relationships and cannot capture all complex patterns in the data.

[0043] To this end, the present invention provides a method for optimizing process parameters of ultrasonic impact treatment, in which welding simulation data of welded parts and multiple groups of process parameters of ultrasonic impact treatment process can be obtained, and finite element simulation is performed on each group of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data of welds after ultrasonic impact treatment under different process parameters, and a training data set is generated according to the multiple groups of process parameters, welding simulation data and residual stress and compressive stress depth data of welds after ultrasonic impact treatment, and the influence of multiple process parameters on the residual stress and compressive stress depth of welds after ultrasonic impact treatment is comprehensively considered, and a pre-constructed ultrasonic impact treatment algorithm is trained using the training data set. The ultrasonic impact treatment prediction model is developed, and the trained ultrasonic impact treatment prediction model is used to predict the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, and at least one set of process parameters of the ultrasonic impact treatment process is optimized according to the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment. By comprehensively considering the influence of various process parameters on the ultrasonic impact treatment process, especially the influence of cooling temperature on the residual stress after the ultrasonic impact treatment process, the process parameters of the ultrasonic impact treatment process are comprehensively optimized to minimize the residual stress and reduce the service risk of vacuum chamber components, and a reference can be provided for the parameter adjustability of ultrasonic impact treatment equipment.

[0044] Specifically, Figure 1 A schematic flow chart of a method for optimizing process parameters of ultrasonic impact treatment provided by an embodiment of the present invention.

[0045] like Figure 1 As shown, the ultrasonic impact treatment process parameter optimization method includes the following steps:

[0046] In step S101 , welding simulation data of a welded part and multiple sets of process parameters of an ultrasonic impact treatment process are acquired.

[0047] Among them, the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter, impact time, etc.

[0048] In an embodiment of the present invention, welding simulation data of a welded part and multiple sets of process parameters of an ultrasonic impact treatment process are obtained, including: constructing a welding finite element model; performing multi-layer and multi-pass welding simulation on the welded part using the welded part finite element model to obtain welding simulation data of the welded part.

[0049] It can be understood that the embodiment of the present invention can construct a welding finite element model and use the weldment finite element model to perform multi-layer and multi-pass welding simulation on the weldment. The thermo-elastoplastic finite element method can be used to perform multi-layer and multi-pass welding simulation to obtain welding simulation data of the weldment.

[0050] In an embodiment of the present invention, a finite element model of a welded part is used to perform multi-layer and multi-pass welding simulation on the welded part to obtain welding simulation data of the welded part, including: performing failure treatment on the weld area in the welded part so that the weld area does not participate in the finite element analysis; activating each weld in turn to participate in the finite element analysis, and performing thermal analysis simulation on the current weld participating in the finite element analysis to obtain temperature field distribution data of the current weld; using the temperature field distribution data of the current weld as an initial condition, performing force analysis on the current weld to obtain corresponding stress field distribution and deformation data, until all weld analyses are completed to obtain welding simulation data of the welded part.

[0051] Specifically, after constructing the welding finite element model, the embodiment of the present invention uses the thermo-elastic-plastic finite element method to perform sequential coupled thermal analysis numerical simulation of the welding process. For multi-layer and multi-pass welding simulation, the specific process of sequential coupling is as follows:

[0052] (1) Using the birth-death unit method, all weld areas are “killed”, that is, failure treatment is performed and they are not involved in finite element analysis;

[0053] (2) Activate the first weld to participate in the analysis, and perform thermal analysis simulation on the first weld to obtain the temperature field distribution;

[0054] (3) Using the temperature field results of the first weld as the initial condition, force analysis of the first weld is performed to obtain the stress field distribution and deformation;

[0055] (4) Activate the second weld to participate in the analysis, consider the deformation and stress of the first weld, perform thermal analysis on the second weld, and obtain the temperature field distribution of the second weld;

[0056] (5) Using the temperature field results of the second weld as a condition, perform force analysis on the second weld to obtain stress distribution and deformation;

[0057] (6) The thermal analysis and force analysis of subsequent welds are completed in this way, and the post-weld deformation model, post-weld temperature field and post-weld residual stress field of the welded parts are obtained and saved in the result file.

[0058] In step S102, finite element simulation is performed on each set of process parameters based on the welding simulation data to obtain the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, and a training data set is generated based on multiple sets of process parameters, welding simulation data and the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment.

[0059] Among them, the welding simulation data includes: post-weld deformation model, post-weld temperature field and post-weld residual stress field before ultrasonic impact treatment, and the center point temperature of the weld temperature field, as well as the maximum residual stress value and the average residual stress value at the weld can be analyzed based on the post-weld temperature field and post-weld residual stress field before ultrasonic impact treatment; the residual stress data at the weld after ultrasonic impact treatment includes the maximum and average residual stress values ​​at the weld.

[0060] It can be understood that the embodiment of the present invention can perform finite element simulation on each set of process parameters based on the welding simulation data to obtain the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, and generate a training data set based on multiple sets of process parameters, welding simulation data and residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, so as to facilitate the subsequent training of the ultrasonic impact treatment prediction model.

[0061] In an embodiment of the present invention, finite element simulation is performed on each set of process parameters based on welding simulation data to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, including: constructing an ultrasonic impact treatment finite element model based on a post-weld deformation model, and loading the post-weld temperature field and post-weld residual stress field before ultrasonic impact treatment into the ultrasonic impact treatment finite element model as predefined fields, and using the ultrasonic impact treatment finite element model to perform ultrasonic impact simulation on the weld under different process parameters to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0062] It can be understood that the embodiment of the present invention can construct an ultrasonic impact treatment finite element model based on the post-weld deformation model, and load the post-weld temperature field and post-weld residual stress field before and after ultrasonic impact treatment into the ultrasonic impact treatment finite element model as predefined fields. The ultrasonic impact treatment finite element model is used to simulate the ultrasonic impact treatment of the weld under different process parameters, and the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters are obtained, so as to facilitate the subsequent training of the ultrasonic impact treatment prediction model. The schematic diagram of the ultrasonic impact treatment finite element model is shown in FIG. Figure 2 shown.

[0063] In an embodiment of the present invention, an ultrasonic impact treatment finite element model is used to perform ultrasonic impact simulation on the weld under different process parameters to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, including: cooling the weldment to the cooling temperature in the process parameter combination through the external temperature of the heat exchange condition, and setting the external temperature as the target temperature for temperature-dynamic coupling simulation; setting impact needles of different diameters, and applying a displacement load perpendicular to the weld surface and controlled by the amplitude to the impact needle, and setting the impact frequency and impact amplitude in the amplitude; setting the time for ultrasonic impact analysis, and applying a velocity load parallel to the weld direction to the impact needle to ensure that the impact on the weld is completed within the set time, so as to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0064] Specifically, the embodiment of the present invention can use the ultrasonic impact treatment finite element model to perform ultrasonic impact simulation on the weld under different process parameters, and obtain the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters. The residual stress simulation results of the weld before ultrasonic impact treatment are as follows: Figure 3 As shown in the figure, the simulation results of residual stress in the weld after ultrasonic impact treatment are as follows: Figure 4 As shown in Figure 2, during the simulation, the control methods of various parameters are as follows:

[0065] Cooling temperature: Cool the weldment to the cooling temperature set in the process parameter combination through the external temperature of the heat exchange condition, and then set the external temperature to room temperature for temperature-dynamic coupling simulation;

[0066] Impact needle diameter: Model and assemble impact needles of different diameters;

[0067] Impact frequency and impact amplitude: A displacement load controlled by amplitude is applied to the impact needle perpendicular to the weld surface. The impact frequency and amplitude are set in the amplitude.

[0068] Impact time: Set the ultrasonic impact analysis step time, apply a velocity load parallel to the weld direction to the impact needle, and ensure that the impact on the weld is completed within the set time.

[0069] In step S103, a pre-built ultrasonic impact treatment prediction model is trained using a training data set, wherein the input of the ultrasonic impact treatment prediction model is multiple sets of process parameters and welding simulation data, and the output of the ultrasonic impact treatment prediction model is the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment.

[0070] It can be understood that the embodiment of the present invention can use the training data set to train a pre-built ultrasonic impact treatment prediction model. The input of the ultrasonic impact treatment prediction model is multiple sets of process parameters and welding simulation data, and the output of the ultrasonic impact treatment prediction model is the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment.

[0071] In an embodiment of the present invention, the ultrasonic impact treatment prediction model includes an input layer, multiple hidden layers, an output layer and a loss function, wherein the input layer is used to input process parameters and welding simulation data; the multiple hidden layers are used to perform feature extraction and nonlinear transformation on the process parameters and welding simulation data; the output layer is used to output the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; the loss function is used to calculate the prediction error based on the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment and the true value in the training data set, and update the model parameters of the ultrasonic impact treatment prediction model based on the prediction error until the loss function is minimized.

[0072] Among them, the loss function can be calculated by MSE (Means squared error), and the formula of the loss function is:

[0073] ;

[0074] Where N is the number of training samples (process parameters and welding simulation data), is the actual value (target value) of the i-th sample, which is a vector containing three outputs: the maximum value, average value and depth of the compressive stress layer of the weld after ultrasonic impact treatment. is the model's prediction for the i-th sample, also a vector. || ... ||^2 represents the square of the vector's L2 norm (Euclidean norm), which is the square root of the sum of the squares of each element in the vector.

[0075] In an embodiment of the present invention, MLP (Multi Layer Perception) can be used as an ultrasonic impact treatment prediction model, wherein an input layer is used to input process parameters and welding simulation data; multiple hidden layers are used to perform feature extraction and nonlinear transformation on the process parameters and welding simulation data; an output layer is used to output predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; a loss function is used to calculate the prediction error based on the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment and the true value in the training data set, and the model parameters of the ultrasonic impact treatment prediction model are updated according to the prediction error until the loss function is minimized.

[0076] Specifically, the MLP includes an input layer, multiple hidden layers and an output layer, wherein the input layer includes 8 nodes, corresponding to: cooling temperature, impact frequency, impact amplitude, impact needle diameter, impact time and three characteristics of the weld area obtained from the welding simulation data (weld center point temperature before ultrasonic impact treatment, maximum residual stress value before ultrasonic impact treatment, average residual stress value before ultrasonic impact treatment); the hidden layer contains two hidden layers, the first layer contains 64 neurons, and the second layer contains 32 neurons; the output layer includes three nodes, corresponding to: the maximum residual stress at the weld after ultrasonic impact treatment, the average residual stress after ultrasonic impact treatment, and the average depth of the compressive stress layer after ultrasonic impact treatment; the connection between the input layer and the hidden layer adopts a fully connected mode, and the ReLU activation function is used as the activation function of the hidden layer, and the Sigmoid activation function is used as the activation function of the output layer. The loss function is constructed, which consists of data loss and is calculated using the mean square error (MSE).

[0077] In an embodiment of the present invention, before using a training data set to train a pre-constructed ultrasonic impact processing prediction model, it also includes: determining the disturbance amplitude of the training data in the training data set based on the prediction error; generating new training data based on the disturbance amplitude, and expanding the training data set based on the new training data; and using the expanded training data set to train the pre-constructed ultrasonic impact processing prediction model.

[0078] It can be understood that the present invention is suitable for embodiments that can perform slight perturbations on the training data in the existing training data set to generate new training data, enrich the characteristics of the training data, and improve the prediction performance and generalization ability of the ultrasonic impact treatment prediction model. It can determine the perturbation amplitude of the training data in the training data set based on the prediction error, generate new training data based on the perturbation amplitude, expand the training data set based on the new training data, and use the expanded training data set to train the pre-built ultrasonic impact treatment prediction model.

[0079] Specifically, the process of adaptively enhancing training data in the embodiment of the present invention is as follows: Figure 5 As shown in the figure, taking the ultrasonic impact treatment prediction model as an MLP model as an example, new training samples are generated by perturbing the existing data, and the perturbation amplitude is adaptively adjusted, specifically:

[0080] 1. Train an initial MLP model (using a small amount of existing finite element simulation data). Then, use the trained model to predict the existing training data and calculate the prediction error, which is calculated using the Mean Square Error (MSE).

[0081] 2. Adaptively adjust the magnitude of data augmentation based on the size of the prediction error, generate new samples (i.e., new training data), and add them to the original training dataset.

[0082] 3. Retrain the MLP model using the expanded dataset.

[0083] For example, assume that adaptive data augmentation is only performed for the impact time t.

[0084] (1) Assuming that there are only ten simulation points initially, the MLP model has only one input and one output, and an initial MLP model is trained using the initial ten data points;

[0085] (2) Use the trained MLP model to predict the values ​​of the original ten data points, calculate the prediction error (MSE) of the ten points using the predicted values ​​and the actual values, and calculate the average MSE;

[0086] (3) Set the basic perturbation amplitude, for example 20%. The basic perturbation amplitude means that there is no adaptive adjustment and the perturbation will be performed within 20% of the original input;

[0087] (4) Adjust the perturbation amplitude of each original data point according to the average MSE: new perturbation amplitude = basic perturbation amplitude * (MSE of a certain point / average MSE);

[0088] (5) Assuming that at a certain point t = 10, the new perturbation amplitude is calculated to be 20%, and the perturbation range is 10*(1-20%) to 10*(1+20%), that is, (8, 12);

[0089] (6) Randomly generate a t in this interval, repeat the ultrasonic impact treatment finite element simulation steps, calculate the output value, and obtain a new data point, that is, a new training data.

[0090] Furthermore, the inputs for new data points are randomly perturbed by the original inputs. The perturbation amplitude is adaptively adjusted based on the prediction error of the MLP model. The output values ​​of new data points are not predicted by the model but are simulated to ensure error accumulation and data authenticity. For 5 inputs and 3 outputs, the inputs and outputs are changed to vectors (1, 2, 3, 4, 5) and (1, 2, 3). The initial MLP model training and prediction error calculation are the same, except that a base perturbation amplitude must be set for each input parameter. The perturbation amplitude for each parameter is calculated for each original training data point. The perturbation range for each parameter in each original training data point is then determined, and a random number is generated within the range to obtain the new input. Finite element simulation is then performed to obtain the output and the new training data.

[0091] In step S104, the trained ultrasonic impact treatment prediction model is used to predict the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, and at least one set of process parameters of the ultrasonic impact treatment process is optimized based on the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment.

[0092] It can be understood that the embodiment of the present invention can use the trained ultrasonic impact treatment prediction model to predict the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment process, and optimize at least one set of process parameters of the ultrasonic impact treatment process based on the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment. By comprehensively considering various process parameters, especially the influence of cooling temperature on the ultrasonic impact treatment process, the overall process parameters are optimized to minimize the residual stress and reduce the service risk of vacuum chamber components, and can provide a reference for the parameter adjustability of ultrasonic impact treatment equipment.

[0093] In an embodiment of the present invention, at least one set of process parameters of the ultrasonic impact treatment process is optimized based on the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, including: setting an optimization target for the ultrasonic impact treatment process parameters, inputting the optimization target, at least one set of ultrasonic impact treatment process parameters and the corresponding residual stress and compressive stress depth data at the weld after ultrasonic impact treatment into a target algorithm; using the target algorithm to find the optimal solution, and optimizing at least one set of process parameters of the ultrasonic impact treatment process based on the optimization result.

[0094] Among them, the target algorithm can be selected according to the specific situation, such as the NSGA-Ⅱ algorithm, which is not specifically limited; the optimization goal can be to minimize the maximum and average values ​​of residual stress and maximize the compressive stress depth.

[0095] It can be understood that the embodiments of the present invention can use the target algorithm, optimization target, and residual stress and compressive stress depth data at the weld after ultrasonic impact treatment to optimize at least one set of process parameters of the ultrasonic impact treatment process, so as to achieve optimization of the ultrasonic impact treatment parameters of the ultrasonic impact treatment process.

[0096] For example, the NSGA-Ⅱ algorithm is used to determine the optimal solution for process parameters, specifically:

[0097] 1. Individual code.

[0098] Each set of ultrasonic impact treatment process parameters is encoded as an individual. In other words, each individual represents an ultrasonic impact treatment solution. This solution consists of a set of ultrasonic impact treatment parameters. Each individual can be represented as a vector, where (T, f, A, D, t) corresponds to temperature, frequency, amplitude, diameter, and time.

[0099] 2. Initialize the population.

[0100] Take N individuals as the initial parent population.

[0101] 3. Fitness assessment.

[0102] The fitness of each individual is evaluated using a trained ultrasonic impact treatment prediction model. The purpose of fitness evaluation is to assess the "quality" of each individual, that is, to evaluate the quality of each set of ultrasonic impact treatment parameters. The goal of this invention is to minimize the maximum and average residual stresses and maximize the depth of the compressive stress layer. Therefore, the fitness is a vector composed of multiple objective function values: (f1, f2, f3) = (σ_max, σ_avg, -d). To ensure that all objective functions are optimized towards the same minimum value, the compressive stress layer depth is negated.

[0103] 4. Non-dominated sorting.

[0104] The target algorithm is used to sort the initial parent population and divide the individuals in the initial parent population into multiple Pareto levels.

[0105] 5. Select an operation.

[0106] The tournament selection operator is used to perform the selection operation, that is, randomly selecting multiple individuals from the parent population, and determining multiple parent individuals based on the levels corresponding to the selected multiple individuals.

[0107] 6. Cross operation.

[0108] A simulated binary crossover operator is used to perform a crossover operation, that is, a crossover operation is performed on multiple parent individuals to obtain multiple offspring individuals.

[0109] 7. Mutation operation.

[0110] A polynomial mutation operator is used to perform mutation operations, that is, to perform mutation operations on multiple offspring individuals to form offspring species.

[0111] 8. Environment selection.

[0112] The parent population and the child population are merged, and the target algorithm is used to sort the merged population, select individuals with higher Pareto levels to form a new population, and eliminate individuals with lower Pareto levels.

[0113] 9. Repeat the above steps until the stopping condition is met (using the maximum number of iterations and the Pareto front change less than the threshold as the stopping condition), and finally output the Pareto optimal solution set, that is, the data set of the optimal process parameter combination.

[0114] Taking the NSGA-Ⅱ algorithm as an example and the ultrasonic impact treatment prediction model as the MLP model, the process of optimizing process parameters is as follows: Figure 6 As shown in Figure 1, the trained MLP model is used as a proxy model to search for the Pareto optimal solution set, including:

[0115] 1. Initialize the population.

[0116] 2. Use the MLP model to evaluate the fitness of each individual. Then, perform non-dominated sorting, selection, crossover, and mutation operations to generate a new population.

[0117] 3. Repeat the above steps until the stopping condition is met (using the maximum number of iterations and the Pareto front change less than the threshold as the stopping condition), and finally output the Pareto optimal solution set.

[0118] The following describes the method for optimizing the process parameters of ultrasonic impact treatment according to an embodiment of the present invention through a specific embodiment. The process is as follows: Figure 7 As shown, the following steps are included:

[0119] Step 1: Based on different cooling temperatures and different ultrasonic impact treatment process parameters, including impact frequency, impact amplitude, impact needle diameter, and impact time, a five-factor multi-level orthogonal experimental method is used to obtain several groups of parameter combinations.

[0120] Step 2: Establish a finite element model of the welded part, and use the thermo-elastic-plastic finite element method to perform sequential coupled thermal analysis numerical simulation of the welding process to obtain the post-weld deformation model, post-weld temperature field, and post-weld residual stress field of the welded part and save them in the result file.

[0121] Step 3: Based on the post-weld deformation model, a finite element model of ultrasonic impact treatment is established. The post-weld temperature field and post-weld residual stress field are loaded into the model as predefined fields, and simulations are performed for different temperature and process parameter combinations.

[0122] The control methods for each parameter are as follows: Cooling temperature: cool the weldment to the temperature set in the parameter combination through the external temperature of the heat exchange condition, and then set the external temperature to room temperature for temperature-dynamic coupling simulation; Impact needle diameter: model and assemble impact needles of different diameters respectively; Impact frequency and impact amplitude: apply a displacement load controlled by the amplitude perpendicular to the weld surface to the impact needle, and the impact frequency and amplitude are set in the amplitude; Impact time: set the ultrasonic impact analysis step time, and apply a velocity load parallel to the weld direction to the impact needle to ensure that the impact on the weld is completed within the set time.

[0123] Step 4: Cooling temperature, impact frequency, impact amplitude, impact needle diameter, and impact time are used as input parameters, and the average residual stress at the weld, the maximum residual stress at the weld, and the average depth of the compressive stress layer at the weld (the distance from the surface to the zero stress point in the depth direction) are used as three output parameters.

[0124] Step 5: Build a multi-layer perceptron (MLP) based on adaptive data augmentation.

[0125] Determine the network structure: Select an appropriate multi-layer perceptron (MLP) as the prediction model. The MLP consists of an input layer, multiple hidden layers, and an output layer. The input layer includes eight nodes, corresponding to the cooling temperature, impact frequency, impact amplitude, impact needle diameter, impact time, and three weld area characteristics derived from the welding simulation results (weld center temperature, maximum residual stress value, and average residual stress value). The hidden layer contains two hidden layers, with the first layer containing 64 neurons and the second layer containing 32 neurons. The output layer includes three nodes, corresponding to the maximum residual stress in the weld, the average residual stress value, and the average depth of the compressive stress layer. The connection between the input layer and the hidden layer uses a fully connected method. Determine the activation function: Use the ReLU activation function for the hidden layer and the Sigmoid activation function for the output layer. Construct the loss function: It consists of data loss and is calculated using the mean squared error (MSE).

[0126] Among them, adaptive data enhancement: generate new training samples by making slight perturbations to existing data; training MLP model: use Adam optimizer to train the MLP model, and the goal is to minimize the loss function.

[0127] Step 6: Construct the NSGA-II algorithm based on the multi-layer perceptron agent model.

[0128] Individual encoding: encoding ultrasonic impact treatment parameters into individuals; Initialization of population: randomly generating N individuals as the initial population;

[0129] Fitness evaluation: Use the trained MLP model to evaluate the fitness of each individual;

[0130] Non-dominated sorting: Use the NSGA-Ⅱ algorithm to sort the population and divide it into multiple Pareto levels;

[0131] Selection operation: Use tournament selection operator for selection operation. Crossover operation: Use simulated binary crossover operator for crossover operation.

[0132] Mutation operation: Use polynomial mutation operator to perform mutation operation;

[0133] Environmental selection: merge the parent population and the offspring population, use the NSGA-Ⅱ algorithm to sort the merged population, select individuals with higher Pareto levels to form a new population, and eliminate individuals with lower Pareto levels.

[0134] Step 7: Output the Pareto optimal solution set and select the best process parameter combination based on actual engineering requirements.

[0135] In summary, for the post-weld ultrasonic impact treatment process, the present invention proposes to perform ultrasonic impact treatment after cooling to different temperatures, taking into account the influence of temperature on the elimination of residual stress; for the highly nonlinear relationship between welding residual stress and process parameters, the present invention adopts a multi-layer perceptron (MLP) based on adaptive data enhancement and a proxy model evolutionary algorithm (NSGA-Ⅱ) to optimize the post-weld ultrasonic impact treatment process, and realizes accurate fitting prediction; for the ultrasonic impact treatment process of large structural parts in vacuum chambers, the multi-objective ultrasonic impact parameter optimization method proposed in the present invention can consider more output target parameters of residual stress and deformation, and select the optimal parameters that meet the actual application scenarios of the engineering.

[0136] The optimization method of the present invention focuses on the impact of temperature changes on the regulation of residual stress in the ultrasonic impact treatment process; combines the adaptive data enhancement method with a multi-layer perceptron (MLP) to construct a high-precision ultrasonic impact treatment parameter prediction model; considers multi-objective parameter optimization to better meet the needs of actual engineering applications; uses a trained MLP model as a proxy model, combined with the NSGA-II algorithm, to accelerate the evolution process and improve optimization efficiency.

[0137] According to the ultrasonic impact treatment process parameter optimization method proposed in an embodiment of the present invention, the welding simulation data of the welded part and multiple sets of process parameters of the ultrasonic impact treatment process can be obtained, and finite element simulation is performed on each set of process parameters based on the welding simulation data to obtain the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters. A training data set is generated based on the multiple sets of process parameters, welding simulation data and the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment, and the influence of multiple process parameters on the residual stress and compressive stress depth of the weld after ultrasonic impact treatment is comprehensively considered, and the training data set is used to train the pre-constructed ultrasonic impact treatment A prediction model is developed, and the trained ultrasonic impact treatment prediction model is used to predict the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, and at least one set of process parameters of the ultrasonic impact treatment process is optimized according to the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment. By comprehensively considering the influence of various process parameters on the ultrasonic impact treatment process, especially the influence of cooling temperature on the residual stress after the ultrasonic impact treatment process, the process parameters of the ultrasonic impact treatment process are comprehensively optimized to minimize the residual stress and reduce the service risk of vacuum chamber components, and a reference can be provided for the parameter adjustability of the ultrasonic impact treatment equipment.

[0138] Next, the ultrasonic impact treatment process parameter optimization device proposed in an embodiment of the present invention will be described with reference to the accompanying drawings.

[0139] Figure 8 It is a block diagram of an ultrasonic impact treatment process parameter optimization device according to an embodiment of the present invention.

[0140] like Figure 8 As shown, the ultrasonic impact treatment process parameter optimization device 10 includes: an acquisition module 100, a simulation module 200, a training module 300 and an optimization module 400.

[0141] The acquisition module 100 is used to acquire the welding simulation data of the welded part and multiple sets of process parameters of the ultrasonic impact treatment process, specifically including: acquiring the welding simulation data of the welded part and multiple sets of process parameters of the ultrasonic impact treatment process, wherein the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter and impact time;

[0142] The simulation module 200 is used to perform finite element simulation on each set of process parameters, specifically including: performing finite element simulation on each set of process parameters based on the welding simulation data, obtaining residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters, and generating a training data set based on multiple sets of process parameters, welding simulation data, and residual stress and compressive stress depth data of the weld after ultrasonic impact treatment;

[0143] The training module 300 is used to train the ultrasonic impact treatment prediction model, specifically including: using the training data set to train the pre-built ultrasonic impact treatment prediction model, wherein the input of the ultrasonic impact treatment prediction model is multiple sets of process parameters and welding simulation data, and the output of the ultrasonic impact treatment prediction model is the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment;

[0144] The optimization module 400 is used to optimize at least one set of process parameters of the ultrasonic impact treatment process, specifically including: using the trained ultrasonic impact treatment prediction model to predict the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, and optimizing at least one set of process parameters of the ultrasonic impact treatment process based on the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment.

[0145] In an embodiment of the present invention, the ultrasonic impact treatment prediction model includes an input layer, multiple hidden layers, an output layer and a loss function, wherein the input layer is used to input process parameters and welding simulation data; the multiple hidden layers are used to perform feature extraction and nonlinear transformation on the process parameters and welding simulation data; the output layer is used to output the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; the loss function is used to calculate the prediction error based on the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment and the true value in the training data set, and update the model parameters of the ultrasonic impact treatment prediction model according to the prediction error until the loss function is minimized.

[0146] In the embodiment of the present invention, the acquisition module 100 is further used to: construct a welding finite element model; and perform multi-layer and multi-pass welding simulation on the weldment using the weldment finite element model to obtain welding simulation data of the weldment.

[0147] In an embodiment of the present invention, the welding simulation data includes: a post-weld deformation model, a post-weld temperature field before ultrasonic impact treatment, and a post-weld residual stress field.

[0148] In an embodiment of the present invention, the simulation module 200 is further used to: construct an ultrasonic impact treatment finite element model based on the post-weld deformation model, and load the post-weld temperature field and post-weld residual stress field before ultrasonic impact treatment as predefined fields into the ultrasonic impact treatment finite element model, and use the ultrasonic impact treatment finite element model to perform ultrasonic impact simulation on the weld under different process parameters to obtain the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0149] In an embodiment of the present invention, the acquisition module 100 is further used to: perform failure processing on the weld area in the welded part so that the weld area does not participate in the finite element analysis; activate each weld in turn to participate in the finite element analysis, and perform thermal analysis simulation on the current weld participating in the finite element analysis to obtain the temperature field distribution data of the current weld; use the temperature field distribution data of the current weld as the initial condition, perform force analysis on the current weld to obtain the corresponding stress field distribution and deformation data, until all weld analyses are completed, and obtain the welding simulation data of the welded part.

[0150] In an embodiment of the present invention, the simulation module 200 is further used to: cool the weldment to the cooling temperature in the process parameter combination through the external temperature of the heat exchange condition, and set the external temperature as the target temperature for temperature-dynamic coupling simulation; set impact needles of different diameters, and apply a displacement load perpendicular to the weld surface and controlled by the amplitude to the impact needle, and set the impact frequency and impact amplitude in the amplitude; set the time for ultrasonic impact analysis, apply a velocity load parallel to the weld direction to the impact needle, and ensure that the impact on the weld is completed within the set time, so as to obtain the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters.

[0151] In an embodiment of the present invention, the optimization module 400 is further used to: set an optimization target for the ultrasonic impact treatment process parameters, input the optimization target, at least one set of ultrasonic impact treatment process parameters and the corresponding residual stress and compressive stress depth data at the weld after ultrasonic impact treatment into the target algorithm; use the target algorithm to find the optimal solution, and optimize at least one set of process parameters of the ultrasonic impact treatment process according to the optimization results.

[0152] In the embodiment of the present invention, the ultrasonic impact treatment process parameter optimization device 10 of the embodiment of the present invention further includes: a disturbance module.

[0153] Among them, the perturbation module is used to determine the perturbation amplitude of the training data in the training data set based on the prediction error before using the training data set to train the pre-built ultrasonic impact treatment prediction model; generate new training data based on the perturbation amplitude, and expand the training data set based on the new training data; and use the expanded training data set to train the pre-built ultrasonic impact treatment prediction model.

[0154] It should be noted that the above explanation of the embodiment of the ultrasonic impact treatment process parameter optimization method is also applicable to the ultrasonic impact treatment process parameter optimization device of this embodiment, and will not be repeated here.

[0155] According to the ultrasonic impact treatment process parameter optimization device proposed in the embodiment of the present invention, the welding simulation data of the welded part and multiple sets of process parameters of the ultrasonic impact treatment process can be obtained, and finite element simulation is performed on each set of process parameters based on the welding simulation data to obtain the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters. A training data set is generated based on the multiple sets of process parameters, welding simulation data and the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment, and the influence of multiple process parameters on the residual stress and compressive stress depth of the weld after ultrasonic impact treatment is comprehensively considered, and the training data set is used to train the pre-constructed ultrasonic impact treatment A prediction model is developed, and the trained ultrasonic impact treatment prediction model is used to predict the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment, and at least one set of process parameters of the ultrasonic impact treatment process is optimized according to the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment. By comprehensively considering the influence of various process parameters on the ultrasonic impact treatment process, especially the influence of cooling temperature on the residual stress after the ultrasonic impact treatment process, the process parameters of the ultrasonic impact treatment process are comprehensively optimized to minimize the residual stress and reduce the service risk of vacuum chamber components, and a reference can be provided for the parameter adjustability of the ultrasonic impact treatment equipment.

[0156] Figure 9 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0157] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0158] When the processor 802 executes the program, the ultrasonic impact treatment process parameter optimization method provided in the above embodiment is implemented.

[0159] Furthermore, the electronic device further includes:

[0160] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0161] The memory 801 is used to store computer programs that can be run on the processor 802.

[0162] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0163] If the memory 801, processor 802, and communication interface 803 are implemented independently, the communication interface 803, memory 801, and processor 802 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0164] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0165] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0166] An embodiment of the present invention further provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the above-mentioned method for optimizing process parameters of ultrasonic impact treatment is implemented.

[0167] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0168] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0169] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or N executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0170] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0171] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

Claims

1. A method for optimizing process parameters of ultrasonic impact treatment, characterized in that: The following steps are involved: Acquiring welding simulation data of the weldment and multiple sets of process parameters of the ultrasonic impact treatment process, wherein the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter, and impact time; Performing finite element simulation on each set of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, and generating a training data set based on the multiple sets of process parameters, the welding simulation data, and the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; Using the training data set to train a pre-built ultrasonic impact treatment prediction model, wherein the input of the ultrasonic impact treatment prediction model is the multiple sets of process parameters and the welding simulation data, and the output of the ultrasonic impact treatment prediction model is residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; The trained ultrasonic impact treatment prediction model is used to predict the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment, and at least one set of process parameters of the ultrasonic impact treatment process is optimized based on the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment.

2. The ultrasonic impact treatment process parameter optimization method according to claim 1, characterized in that: The ultrasonic impact treatment prediction model includes an input layer, multiple hidden layers, an output layer and a loss function, wherein: The input layer is used to input process parameters and welding simulation data; The multiple hidden layers are used for feature extraction and nonlinear transformation of the process parameters and welding simulation data; The output layer is used to output the predicted residual stress and compressive stress depth data of the weld after ultrasonic impact treatment; The loss function is used to calculate the prediction error based on the predicted residual stress and compressive stress depth data at the weld after ultrasonic impact treatment and the true value in the training data set, and update the model parameters of the ultrasonic impact treatment prediction model according to the prediction error until the loss function is minimized.

3. The ultrasonic impact treatment process parameter optimization method according to claim 1, characterized in that: The method of obtaining the welding simulation data of the welded part and multiple sets of process parameters of the ultrasonic impact treatment process includes: Construct finite element model of welded parts; The finite element model of the welded part is used to perform multi-layer and multi-pass welding simulation on the welded part to obtain welding simulation data of the welded part.

4. The ultrasonic impact treatment process parameter optimization method according to claim 3, characterized in that: The welding simulation data includes: a post-weld deformation model, a post-weld temperature field before ultrasonic impact treatment, and a post-weld residual stress field. The finite element simulation is performed on each set of process parameters based on the welding simulation data to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, including: An ultrasonic impact treatment finite element model is constructed based on the post-weld deformation model, and the post-weld temperature field and post-weld residual stress field before the ultrasonic impact treatment are loaded into the ultrasonic impact treatment finite element model as predefined fields. The ultrasonic impact treatment finite element model is used to perform ultrasonic impact simulation on the weld under the different process parameters, and the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters are obtained.

5. The method for optimizing process parameters of ultrasonic impact treatment according to claim 3, characterized in that: The method of performing multi-layer and multi-pass welding simulation on the welded part using the welded part finite element model to obtain welding simulation data of the welded part includes: Performing failure treatment on the weld area in the welded part so that the weld area does not participate in finite element analysis; Activating each weld in turn to participate in finite element analysis, and performing thermal analysis simulation on the current weld participating in the finite element analysis to obtain temperature field distribution data of the current weld; The temperature field distribution data of the current weld is used as the initial condition, and force analysis is performed on the current weld to obtain corresponding stress field distribution and deformation data. The analysis of all welds is completed to obtain the welding simulation data of the weldment.

6. The method for optimizing process parameters of ultrasonic impact treatment according to claim 4, characterized in that: The ultrasonic impact treatment finite element model is used to perform ultrasonic impact simulation on the weld under the different process parameters to obtain residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, including: Cooling the weldment to a cooling temperature in a process parameter combination by using an external temperature of a heat exchange condition, and setting the external temperature as a target temperature for temperature-dynamic coupling simulation; Setting impact pins of different diameters, applying a displacement load perpendicular to the weld surface and controlled by an amplitude to the impact pins, and setting an impact frequency and impact amplitude in the amplitude; The time for ultrasonic impact analysis is set, and a velocity load parallel to the weld direction is applied to the impact needle to ensure that the impact on the weld is completed within the set time, so as to obtain the residual stress and compressive stress depth data of the weld after ultrasonic impact treatment under different process parameters.

7. The method for optimizing process parameters of ultrasonic impact treatment according to claim 1, characterized in that: Optimizing at least one set of process parameters of the ultrasonic impact treatment process according to the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment includes: Setting an optimization target for the ultrasonic impact treatment process parameters, and inputting the optimization target, at least one set of ultrasonic impact treatment process parameters, and corresponding residual stress and compressive stress depth data at the weld after ultrasonic impact treatment into a target algorithm; The target algorithm is used to search for an optimum, and at least one set of process parameters of the ultrasonic impact treatment process is optimized according to the search result.

8. The method for optimizing process parameters of ultrasonic impact treatment according to claim 2, characterized in that: Before using the training data set to train the pre-built ultrasonic impact treatment prediction model, the method further includes: determining a disturbance amplitude of training data in the training data set based on the prediction error; generating new training data based on the disturbance amplitude, and expanding the training data set based on the new training data; The pre-built ultrasonic impact treatment prediction model was trained using the expanded training dataset.

9. An ultrasonic impact treatment process parameter optimization device, characterized in that: include: an acquisition module, configured to acquire welding simulation data and multiple sets of process parameters, including: acquiring welding simulation data of a welded part and multiple sets of process parameters of an ultrasonic impact treatment process, wherein the process parameters include cooling temperature, impact frequency, impact amplitude, impact needle diameter, and impact time; a simulation module, configured to perform a finite element simulation on each set of process parameters, comprising: performing a finite element simulation on each set of process parameters based on the welding simulation data, obtaining residual stress and compressive stress depth data at the weld after ultrasonic impact treatment under different process parameters, and generating a training data set based on the multiple sets of process parameters, the welding simulation data, and the residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; a training module for training an ultrasonic impact treatment prediction model, comprising: using the training data set to train a pre-built ultrasonic impact treatment prediction model, wherein the input of the ultrasonic impact treatment prediction model is the multiple sets of process parameters and the welding simulation data, and the output of the ultrasonic impact treatment prediction model is residual stress and compressive stress depth data at the weld after ultrasonic impact treatment; An optimization module is used to optimize at least one set of process parameters of the ultrasonic impact treatment process, including: using a trained ultrasonic impact treatment prediction model to predict the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment, and optimizing at least one set of process parameters of the ultrasonic impact treatment process based on the residual stress and compressive stress depth data at the weld after the ultrasonic impact treatment.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing process parameters of ultrasonic impact treatment according to any one of claims 1 to 8.

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