Welding distortion prediction method and electronic device

Abstract

The application discloses a welding deformation prediction method and electronic equipment, and belongs to the technical field of welding. The method comprises the following steps: acquiring test welding deformation data of a test piece, the test piece being built based on a target piece to be predicted, the thickness of the test piece being consistent with the thickness of the target piece; performing thermal elastic-plastic simulation based on the test welding deformation data to determine first inherent strain parameters of the test piece; determining second inherent strain parameters of the target piece based on the first inherent strain parameters; extracting a random field sample based on the second inherent strain parameters to obtain a target inherent strain parameter set of the target piece, the target inherent strain parameter set comprising a plurality of target inherent strain parameters; and performing welding simulation based on the target inherent strain parameter set of the target piece to obtain a predicted welding deformation range of the target piece. The predicted welding deformation range obtained by the method has a smaller error with an actual welding deformation, and the prediction result is more accurate.

Description

Technical Field

[0001] This application belongs to the field of welding technology, and in particular relates to a welding deformation prediction method and electronic device. Background Technology

[0002] During welding, intense localized heating and cooling can cause welding deformation at the weld joint. This deformation can affect dimensional accuracy, structural integrity, and service life. Accurate prediction of welding deformation is crucial for controlling material deformation, optimizing processes, and ensuring weld quality.

[0003] However, there is still an error between the predicted deformation amount obtained by the current welding prediction method and the actual deformation amount, and the prediction results of the welding prediction method have low accuracy. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a welding deformation prediction method and electronic device, which predicts a welding deformation range with a smaller error compared to the actual welding deformation, resulting in more accurate prediction results.

[0005] In a first aspect, this application provides a method for predicting welding deformation, the method comprising:

[0006] The experimental welding deformation data of the test specimen are obtained. The test specimen is built based on the target part to be predicted, and the thickness of the test specimen is the same as the thickness of the target part.

[0007] Based on the experimental welding deformation data, thermo-elastic-plastic simulation was performed to determine the first inherent strain parameter of the test piece;

[0008] Based on the first inherent strain parameter, the second inherent strain parameter of the target component is determined;

[0009] Based on the second inherent strain parameter, random field samples are extracted to obtain the target inherent strain parameter set of the target component, which includes multiple target inherent strain parameters.

[0010] Based on the target component's inherent strain parameter set, welding simulation is performed to obtain the predicted welding deformation range of the target component.

[0011] According to the welding deformation prediction method provided in the embodiments of this application, a test piece with a structure similar to the target part is constructed, and the first inherent strain parameter of the test piece is extracted to obtain the second inherent strain parameter of the target part. Random field theory is creatively introduced into the welding simulation process of the target part. By extracting random field samples of multiple second inherent strain parameters, the set of target inherent strain parameters is obtained. The predicted welding deformation range determined based on the set of target inherent strain parameters reflects the statistical distribution characteristics of welding deformation under the influence of factors such as materials, processes and environment. The predicted welding deformation range obtained by this method has a smaller error with the actual welding deformation of the target part, and the prediction result is more accurate.

[0012] According to one embodiment of this application, the step of extracting random field samples based on the second inherent strain parameter to obtain the target inherent strain parameter set of the target component includes:

[0013] Based on the second inherent strain parameter, random field samples are extracted using the KL expansion algorithm to obtain the target inherent strain parameter set.

[0014] According to one embodiment of this application, the test piece includes a plurality of first welded segments, the weld of the target piece includes a plurality of second welded segments, the first welded segments and the second welded segments correspond one-to-one, and the second inherent strain parameter includes a plurality of second inherent strain sub-parameters, the second inherent strain sub-parameters and the second welded segments correspond one-to-one;

[0015] The extraction of random field samples using the KL expansion algorithm includes:

[0016] The second inherent strain gauge parameters are subjected to KL expansion to obtain the expansion result, which includes random variables;

[0017] The random variable is sampled to determine the sampled value of the random variable;

[0018] Substituting the sampled value into the expansion result, the correction value of the second inherent strain gauge parameter is obtained;

[0019] The random field sample is obtained based on the correction values ​​of multiple second intrinsic strain parameters.

[0020] According to one embodiment of this application, the test piece includes a plurality of first welded segments, and the weld of the target piece includes a plurality of second welded segments, wherein the first welded segments and the second welded segments correspond one-to-one. The step of obtaining the test weld deformation data of the test piece includes:

[0021] The test piece is welded to form the first welded segment, and welding deformation data corresponding to the first welded segment is obtained when the temperature of the test piece is less than a first temperature threshold. The test welding deformation data includes welding deformation data corresponding to multiple first welded segments.

[0022] According to one embodiment of this application, when the test piece is welded, the temperature of the edge of the test piece away from the weld is less than a second temperature threshold.

[0023] According to one embodiment of this application, the dimension of the test piece in the direction perpendicular to the weld direction is greater than or equal to a preset dimension threshold, the preset dimension threshold being K times the weld width, where K is greater than or equal to 10.

[0024] According to one embodiment of this application, the step of performing thermo-elastic-plastic simulation based on the experimental welding deformation data to determine the first inherent strain parameter of the test piece includes:

[0025] Based on the welding deformation data, a thermo-elastic-plastic simulation model was established;

[0026] Extract welding deformation data and inherent strain parameters of the model;

[0027] If the error between the model welding deformation data and the test welding deformation data is less than a preset error threshold, the model's inherent strain parameter is determined as the first inherent strain parameter.

[0028] According to one embodiment of this application, the model welding deformation data is obtained by sequentially activating the weld seams in the thermo-elastic-plastic simulation model according to the welding sequence of the test piece using the birth-death element method.

[0029] According to one embodiment of this application, the step of performing welding simulation based on the target inherent strain parameter set of the target component to obtain the predicted welding deformation range of the target component includes:

[0030] Obtain the model temperature range for thermo-elastic-plastic simulation based on the experimental welding deformation data;

[0031] Based on the temperature range of the model, the range of inherent strain application is determined in the simulation model of the target component;

[0032] Based on the inherent strain application range, the multiple target inherent strain parameters are applied to the simulation model of the target component to obtain the predicted welding deformation range of the target component.

[0033] In a second aspect, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the welding deformation prediction method as described in the first aspect above.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0036] Figure 1 This is one of the flowcharts illustrating the welding deformation prediction method provided in the embodiments of this application;

[0037] Figure 2 This is a second schematic flowchart of the welding deformation prediction method provided in the embodiments of this application;

[0038] Figure 3 This is the third flowchart illustrating the welding deformation prediction method provided in the embodiments of this application;

[0039] Figure 4 This is a schematic diagram of the structure of the target component provided in the embodiments of this application;

[0040] Figure 5 This is a cross-sectional schematic diagram of the target component provided in the embodiments of this application;

[0041] Figure 6 This is a schematic diagram of the structure of the test specimen provided in the embodiments of this application;

[0042] Figure 7 This is a cross-sectional schematic diagram of the test specimen provided in the embodiments of this application;

[0043] Figure 8 This is a schematic diagram of the dimensions of the inherent strain application area provided in the embodiments of this application;

[0044] Figure 9 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0046] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0047] The welding deformation prediction method provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0048] like Figure 1 As shown, the welding deformation prediction method includes steps 110, 120, 130, 140 and 150.

[0049] Step 110: Obtain the test welding deformation data of the test piece. The test piece is built based on the target part to be predicted, and the thickness of the test piece is consistent with the thickness of the target part.

[0050] Among them, the target part refers to the target product or structural part that needs to be predicted for welding deformation. Based on the actual structure of the target part, the location, number and welding process of the weld can be obtained. In addition, based on the geometric shape, material and size parameters of the target part, the number and size of the test pieces can be determined.

[0051] The test piece is a test reference object made using the target part as a template. It is used to simulate the actual welding process of the target part. The key parameters such as the thickness of the test piece are kept consistent with the target part so as to obtain accurate welding deformation data through simulation tests and simulation predictions.

[0052] Experimental welding deformation data are the deformation results measured after the test piece is subjected to the same actual welding test as the target piece. These data include data such as deformation, displacement or angular changes generated during the welding process, and are used to analyze and predict the deformation behavior of the target piece in actual welding.

[0053] In practice, the thickness of the test piece can be kept the same as that of the target piece, while other dimensions such as length and width can be scaled down proportionally based on the target piece. This simplifies the setup process and improves the efficiency of the welding deformation prediction method.

[0054] In this step, in addition to the test piece having the same thickness as the target piece, the welding process used in building the test piece must also be the same as that used in the target piece, including but not limited to welding parameters such as heat input, welding speed, and welding wire diameter.

[0055] It should be noted that the target part may be welded from multiple components, that is, the target part may have multiple welds. For target parts with multiple welds, the welds can be classified, and corresponding test pieces can be built for each category of welds in order to make a comprehensive deformation prediction of the multiple welds in the target part.

[0056] In addition, for welds of the same type, multiple identical test pieces can be built to conduct repeated tests and simulations, thereby reducing the error between the predicted welding deformation results and the actual results and improving the accuracy of the prediction results.

[0057] Step 120: Based on the experimental welding deformation data, perform thermo-elastic-plastic simulation to determine the first inherent strain parameter of the test piece.

[0058] Among them, thermo-elastic-plastic simulation is a process of analyzing the thermo-mechanical coupling behavior of materials during welding through the thermo-elastic-plastic method. The thermo-elastic-plastic method considers the elastic-plastic deformation of materials at high temperatures, the thermal expansion effect, and the influence of temperature changes on material properties, and can simulate the temperature field, stress field, and deformation field during the welding process.

[0059] Inherent strain refers to the total residual strain generated during welding due to thermal stress exceeding the elastic limit. It includes plastic strain, temperature strain, and phase transformation strain. Inherent strain is actually manifested as compressive plastic deformation in the weld area and is the main cause of welding residual stress and deformation. By analyzing the inherent strain in the weld area, welding deformation of the same type of weld can be predicted.

[0060] The first inherent strain parameter refers to the inherent strain parameter extracted from the weld area of ​​the test piece after the test piece has been welded in the same manner as the target piece.

[0061] In practice, based on experimental welding deformation data, thermo-elastic-plastic simulation modeling can be performed on the test piece and the welding process of the test piece to obtain a thermo-elastic-plastic simulation model. Through the thermo-elastic-plastic simulation model, data such as the first inherent strain parameters and temperature range of the welding area of ​​the model can be obtained.

[0062] Step 130: Determine the second inherent strain parameter of the target component based on the first inherent strain parameter.

[0063] The second inherent strain parameter refers to the inherent strain parameter of the target part obtained from the first inherent strain parameter based on the correspondence between the test piece and the target part. The second inherent strain parameter can be used to predict the actual welding deformation of the target part.

[0064] In practice, the first inherent strain parameter at the test specimen scale can be mapped to the target specimen scale to obtain the second inherent strain parameter.

[0065] In this step, the test piece and the target piece have similar structures and the same welding parameters. Their inherent strain parameters also have a certain correspondence. Based on this correspondence and the first inherent strain parameter, the second inherent strain parameter of the target piece can be determined for subsequent simulation and analysis of the welding deformation of the target piece.

[0066] Step 140: Based on the second inherent strain parameter, extract random field samples to obtain the target inherent strain parameter set of the target component. The target inherent strain parameter set includes multiple target inherent strain parameters.

[0067] Understandably, random field theory is a mathematical framework used to describe random variables. Random field theory extends random variables to the spatial or temporal domains, and can endow the target object with spatial or temporal randomness.

[0068] In this step, the random field sample is the inherent strain parameter obtained by giving randomness to the second inherent strain parameter through random field theory. The random field sample reflects the random influence of factors such as materials, processes or environment during the welding process, which is consistent with the fluctuation of actual welding.

[0069] In practice, random field samples can be obtained by establishing a random field model. The random field model is a mathematical expression based on random field theory, used to describe the probability distribution characteristics of the second inherent strain parameter in the weld region, providing a basis for the fluctuation analysis of welding deformation.

[0070] In this step, the target inherent strain parameter set includes multiple target inherent strain parameters. Each target inherent strain parameter is obtained through different random field samples. The target inherent strain parameter set reflects the possible distribution range of the target inherent strain parameters. The welding deformation prediction results obtained based on the target inherent strain parameter set take into account the randomness of the target part in the welding process, are closer to the actual value, and have high accuracy.

[0071] Step 150: Based on the target part's inherent strain parameter set, perform welding simulation to obtain the predicted welding deformation range of the target part.

[0072] Welding simulation refers to simulating welding in the simulation model of the target part, so as to apply different target inherent strain parameters from the target inherent strain parameter set to the simulation model of the target part.

[0073] In this step, the predicted welding deformation range is obtained by predicting the welding deformation range of the target part based on the welding deformation state of the simulation model. The predicted welding deformation range not only includes the predicted welding deformation of the target part, but also reflects the statistical distribution of the predicted results due to the random influence of factors such as materials, processes or environment during the welding process. The predicted deformation amount determined based on the predicted welding deformation range has a small error with the actual deformation amount and high prediction accuracy.

[0074] In related technologies, the thermo-elastic-plastic method and the inherent strain method are commonly used to predict welding deformation. The thermo-elastic-plastic method, based on finite element theory, simulates the welding process by simulating the distribution of welding heat sources. Its nonlinear process leads to long calculation time and high requirements for computer hardware, but the calculation results are relatively accurate and suitable for welding simulation of simple structures. The inherent strain method uses the residual plastic strain of the weld as the initial parameter. During the welding simulation, the residual plastic strain is applied to the weld area selected from the structure. Only one elastic calculation is needed to make the element at the loading position reach a state of equilibrium with the entire model, thereby obtaining the welding deformation distribution. The calculation speed is fast, but the calculation results are not as accurate as those of the thermo-elastic-plastic method. Furthermore, neither of the two prediction methods considers the random influence of factors such as materials, processes, or environment during the welding process, resulting in errors between the predicted deformation and the actual deformation, and the accuracy of the prediction results is low.

[0075] In this embodiment of the application, by constructing a test specimen with a structure similar to the target part, the second inherent strain parameter of the target part is obtained based on the first inherent strain parameter of the test specimen. In the process of extracting the first inherent strain parameter, the thermo-elastic-plastic method is used for simulation, which can improve the accuracy of the first inherent strain parameter and the second inherent strain parameter obtained based on the first inherent strain parameter. Furthermore, using the inherent strain method to perform simulation based on the second inherent strain parameter can improve the calculation speed of the simulation and prediction process.

[0076] Furthermore, the inventors creatively introduced random field theory into the simulation process based on the second inherent strain parameter. By extracting random field samples of multiple second inherent strain parameters, the target inherent strain parameter set of the target part is obtained. The random field samples reflect the random influence of factors such as materials, processes, or environment during the welding process, which is consistent with the fluctuation of actual welding. The predicted welding deformation range obtained based on the target inherent strain parameter set not only includes the predicted welding deformation of the target part, but also reflects the statistical distribution of the predicted results due to the random influence of factors such as materials, processes, or environment during the welding process. The predicted deformation amount determined based on the predicted welding deformation range has a small error with the actual deformation amount, and the prediction accuracy is high.

[0077] According to the welding deformation prediction method provided in the embodiments of this application, a test piece with a structure similar to the target part is constructed, and the first inherent strain parameter of the test piece is extracted to obtain the second inherent strain parameter of the target part. Random field theory is creatively introduced into the welding simulation process of the target part. By extracting random field samples of multiple second inherent strain parameters, the set of target inherent strain parameters is obtained. The predicted welding deformation range determined based on the set of target inherent strain parameters reflects the statistical distribution characteristics of welding deformation under the influence of factors such as materials, processes and environment. The predicted welding deformation range obtained by this method has a smaller error with the actual welding deformation of the target part, and the prediction result is more accurate.

[0078] In some embodiments, based on the second inherent strain parameter, random field samples are extracted to obtain a set of target inherent strain parameters for the target component, including:

[0079] Based on the second inherent strain parameter, random field samples are extracted using the KL expansion algorithm to obtain the set of inherent strain parameters of the target.

[0080] Among them, the KL expansion (Karhunen-Loève expansion) algorithm is a random field dimensionality reduction method based on eigenvalue decomposition. By selecting the main eigenfunctions and their corresponding random coefficients, the KL expansion can realize the parameterized expression of the random field.

[0081] It should be noted that, based on the second inherent strain parameter, the random field of the second inherent strain parameter can be determined. This random field can convert the inherent strain parameter generated during the welding process into a random variable field with spatial or temporal correlation, providing a mathematical basis for the randomness analysis of welding deformation.

[0082] In this embodiment, the KL expansion is introduced into the welding deformation analysis, which decomposes the random field of the second inherent strain parameter into a linear combination of deterministic basis functions and random variables. Then, random field samples can be obtained by randomly sampling the random variables, and the calculation process is simple.

[0083] Random field samples refer to instances generated based on KL expansion that conform to the statistical characteristics of random fields. Each random field sample represents a possible distribution of the second inherent strain parameter in the weld region, which can reflect both the randomness and the correlation of inherent strain.

[0084] In actual implementation, the second inherent strain parameter can be extracted multiple times to obtain multiple random field samples, that is, the set of target inherent strain parameters.

[0085] In this embodiment, the set of target inherent strain parameters can reflect the random distribution of the second inherent strain parameters of the target component, providing a basis for the simulation and analysis of the randomness of welding deformation.

[0086] In some embodiments, the test piece includes a plurality of first welded segments, the weld of the target piece includes a plurality of second welded segments, the first welded segments and the second welded segments correspond one-to-one, and the second inherent strain parameter includes a plurality of second inherent strain sub-parameters, the second inherent strain sub-parameters correspond one-to-one with the second welded segments.

[0087] Understandably, the welding parameters and structure of the experimental piece are similar to or the same as those of the target piece in order to improve the accuracy of the welding deformation prediction results.

[0088] In this embodiment, the weld seam of the test piece is set with reference to the weld seam of the target piece, such as... Figure 4 and Figure 5 As shown, Figure 5 for Figure 4 Cross-sectional view near the weld; the target part is welded from A1 and B1, and a single weld consists of multiple layers (see reference). Figure 5 The C1 section in the middle), each layer includes multiple welded sections (see section C1). Figure 4 In the C1 section of the test piece, which is the second welded section, a weld is also composed of the same number of layers, each layer including the same number of welded sections, namely the first welded section, that is, the first welded section and the second welded section correspond one-to-one.

[0089] It is understandable that the second inherent strain parameter reflects the inherent strain of the entire weld of the target part. The inherent strain of the entire weld is equal to the sum of the inherent strains of multiple weld segments. That is, the second inherent strain parameter includes multiple second inherent strain sub-parameters, and the second inherent strain sub-parameters correspond one-to-one with the second weld segment.

[0090] In this embodiment, the weld between the target part and the test part is divided into multiple welding segments. Using the welding segment as the smallest calculation unit can improve the calculation accuracy of the inherent strain parameters, thereby improving the accuracy of the welding deformation prediction results.

[0091] In this embodiment, such as Figure 2As shown, random field samples are extracted using the KL expansion algorithm, including steps 141, 142, 143, and 144.

[0092] Step 141: Perform KL expansion on the second inherent strainor parameters to obtain the expansion result, which includes random variables.

[0093] The expansion result refers to the low-dimensional expression of the random field of the second intrinsic strain quanta parameters obtained through KL decomposition, which consists of a set of deterministic basis functions, i.e., characteristic functions, and random variables corresponding to the characteristic functions.

[0094] The random variables are the random coefficients multiplied by the basis functions in the expansion result, and they usually follow a standard normal distribution. Each random variable corresponds to a weight. By adjusting the values ​​of these variables, different random field samples can be generated, thereby reflecting the randomness of the inherent strain parameters.

[0095] In practice, the expanded result can be obtained using the following formula:

[0096]

[0097] in, Let i be the i-th characteristic function. For the corresponding eigenvalues, They are independent standard normal random variables.

[0098] also, μ s The mean value of the second inherent strain gauge parameter, σ s The standard deviation of the second inherent strain gauge parameter can be used to build multiple test specimens and calculate the second inherent strain gauge parameter of the second welded segment multiple times, thereby obtaining the mean and standard deviation of the second inherent strain gauge parameter.

[0099] In this step, the random field of the second intrinsic strainor parameters is reduced in dimensionality by KL expansion, and key feature modes and random variables are extracted, which reduces computational complexity while preserving the randomness of the parameters.

[0100] Step 142: Sample the random variable and determine the sampled value of the random variable.

[0101] In this step, sampling refers to the process of generating a set of specific numerical values ​​that conform to statistical characteristics according to the probability distribution of random variables.

[0102] The sampled values ​​refer to the specific numerical results of the random variables obtained by sampling. These values ​​will be substituted into the KL expansion to construct different random field samples, thereby reflecting the actual fluctuation range of welding deformation parameters.

[0103] Step 143: Substitute the sampled values ​​into the expansion results to obtain the corrected values ​​of the second inherent strain gauge parameters.

[0104] In this step, the correction value of the second inherent strain parameter refers to the adjustment result of the second inherent strain parameter calculated by substituting the sampled value of the random variable into the KL expansion. It reflects the quantitative influence of random factors on the inherent strain parameter, and each correction value corresponds to a possible parameter fluctuation.

[0105] Step 144: Based on the correction values ​​of multiple second intrinsic strain parameters, obtain random field samples.

[0106] In this step, the random field sample includes correction values ​​for multiple second inherent strain parameters, and the random field sample represents one possible realization of the second inherent strain parameter of the target weld.

[0107] In practice, Monte Carlo methods can be used to independently sample random variables that follow a normal distribution multiple times to obtain multiple random field samples, thereby determining the set of inherent strain parameters of the target.

[0108] In this embodiment, the second inherent strain parameter is decomposed into a combination of deterministic basis functions and random variables through KL expansion. The random variables are sampled and substituted into the expansion to generate multiple correction values ​​to reflect parameter fluctuations. By integrating the correction values ​​of multiple second weld segments, a random field sample of the entire weld is obtained. After multiple samplings, the target inherent strain parameter set is finally constructed. This method reduces computational complexity while preserving the randomness of inherent strain, providing a data foundation for welding deformation prediction.

[0109] In some embodiments, the test piece includes a plurality of first welded segments, and the weld of the target piece includes a plurality of second welded segments, with the first welded segments corresponding one-to-one with the second welded segments.

[0110] Understandably, the weld seams of the test piece are designed with reference to the weld seams of the target piece, such as... Figure 4 and Figure 5 As shown, Figure 5 for Figure 4 Cross-sectional view near the weld; the target part is welded from A1 and B1, and a single weld consists of multiple layers (see reference). Figure 5 The C1 section in the middle), each layer includes multiple welded sections (see section C1). Figure 4 In the C1 section of the test piece, which is the second welded section, a weld is also composed of the same number of layers, each layer including the same number of welded sections, namely the first welded section, that is, the first welded section and the second welded section correspond one-to-one.

[0111] In this embodiment, obtaining test welding deformation data of the test specimen includes:

[0112] The test piece is welded to form a first welded segment. When the temperature of the test piece is lower than a first temperature threshold, the welding deformation data corresponding to the first welded segment is obtained. The test welding deformation data includes welding deformation data corresponding to multiple first welded segments.

[0113] It is understandable that welding deformation data will be affected by the thermal expansion effect caused by temperature.

[0114] In this embodiment, the test piece is welded in segments, and the number of segments welded is equal to the number of segments in the first weld.

[0115] Furthermore, after each first weld segment is formed, the weld segment is allowed to cool to below the first temperature threshold, and the corresponding welding deformation data is recorded. The "welding-cooling-recording" steps are repeated until the data of all first weld segments in the test piece are recorded, thereby obtaining the test welding deformation data.

[0116] In practice, the first temperature threshold can be room temperature.

[0117] In this implementation, recording the corresponding welding deformation data after the first welded segment cools to below the first temperature threshold can reduce the impact of thermal expansion on the welding deformation data, thereby improving the accuracy of the welding deformation prediction results.

[0118] In some embodiments, when the test piece is welded, the temperature of the edge of the test piece away from the weld is less than a second temperature threshold.

[0119] In practice, the second temperature threshold can be room temperature.

[0120] In this embodiment, such as Figure 6 As shown, the test piece is welded from A2 and B2, and the weld seam is... Figure 6 In C2, the leftmost and rightmost edges of the test piece are the edges of the test piece away from the weld. During the welding process of the test piece, the temperature of the edges of the test piece away from the weld is not affected by the welding.

[0121] In some embodiments, the dimension of the test specimen in the direction perpendicular to the weld direction is greater than or equal to a preset size threshold, the preset size threshold being K times the weld width, where K is greater than or equal to 10.

[0122] In this embodiment, such as Figure 6 As shown, the weld direction is R1, which is the vertical direction, and the direction perpendicular to the weld direction is R2, which is the horizontal direction. The weld width is the width of the weld in the R2 direction. If the dimension of the test piece in the horizontal direction is greater than or equal to K times the weld width, the temperature of the edge of the test piece away from the weld will not be affected by the welding.

[0123] In practice, K can be between 10 and 20.

[0124] In some embodiments, based on experimental welding deformation data, thermo-elastic-plastic simulation is performed to determine the first inherent strain parameter of the test specimen, including:

[0125] A thermo-elastic-plastic simulation model was established based on welding deformation data.

[0126] Extract welding deformation data and inherent strain parameters of the model;

[0127] If the error between the model welding deformation data and the experimental welding deformation data is less than a preset error threshold, the model's inherent strain parameter is determined as the first inherent strain parameter.

[0128] Among them, the thermo-elastic-plastic simulation model is a numerical calculation model used to simulate the welding process. It comprehensively considers physical phenomena such as heat conduction, elastic-plastic deformation and phase transformation during the welding process, and solves the temperature field and stress-strain field through the finite element method, thereby predicting the deformation and residual stress distribution after welding.

[0129] Model welding deformation data refers to the predicted results of welding deformation of test pieces obtained through thermo-elastic-plastic simulation calculations, including the spatial distribution of physical quantities such as displacement and strain, which are used to compare with actual test data to verify the accuracy of the simulation model.

[0130] The model's inherent strain parameters are extracted and calculated from the model, reflecting the plastic deformation behavior of the material caused by the welding thermal cycle.

[0131] The preset error threshold is an allowable deviation range set to judge the degree of agreement between the simulation results and the experimental data. When the difference between the two is less than the threshold, the simulation model is considered to have sufficient accuracy and can be used for subsequent analysis and parameter determination.

[0132] The first inherent strain parameter is the inherent strain parameter extracted from the verified thermo-elastic-plastic simulation model, which is used as the true inherent strain parameter of the test piece when the error between the model welding deformation data and the experimental welding deformation data meets the requirements.

[0133] In this embodiment, the thermo-elastic-plastic simulation model can accurately simulate the thermodynamic behavior of the test piece during the welding process. The simulated welding deformation data is compared and verified with the actual measured welding deformation data. When the error is less than a preset threshold, the established model is determined to be reliable. The inherent strain parameters extracted from the model are used as the true parameters of the test piece, namely the first inherent strain parameters, which provide a data basis for the subsequent calculation of the second inherent strain parameters and random field samples.

[0134] In some embodiments, the model welding deformation data is obtained by sequentially activating the weld seams in the thermo-elastic-plastic simulation model according to the welding sequence of the test piece using the birth-death element method.

[0135] Among them, the birth and death element method is a finite element method used to simulate the welding process. It can simulate the gradual filling process of weld material by dynamically activating or freezing elements.

[0136] In this embodiment, the test piece is constructed by segmented welding. The dead-live method can activate the weld elements in the thermo-elastic-plastic simulation model in sequence according to the actual welding sequence of the test piece, while keeping the unwelded area elements frozen, thereby accurately reproducing the movement of the welding heat source and the material accumulation effect, and finally obtaining welding deformation data that conforms to the actual process.

[0137] In some embodiments, welding simulation is performed based on the target component's inherent strain parameter set to obtain the predicted welding deformation range of the target component, including:

[0138] Obtain the model temperature range for thermo-elastic-plastic simulation based on experimental welding deformation data;

[0139] Based on the model temperature range, the range of inherent strain application is determined in the simulation model of the target component;

[0140] Based on the inherent strain application range, multiple target inherent strain parameters are applied to the simulation model of the target part to obtain the predicted welding deformation range of the target part.

[0141] In this embodiment, by observing the distribution of heat sources in the thermo-elastic-plastic simulation model, the model temperature range can be determined, and thus the range of inherent strain application can be determined in the simulation model of the target part based on the model temperature range.

[0142] It should be noted that, as Figure 7 The cross-sectional view of the test specimen shown indicates that, in the simulation results, the range of inherent strain applied to each weld layer should be the same. Based on the distribution characteristics of welding temperature, the temperature is highest at the center of the heat source and gradually decreases towards the periphery. The interface of the inherent strain application range approximates a trapezoid, i.e. Figure 7 The size of the dashed area in the figure is determined based on the temperature distribution and the high-temperature mechanical properties of the material.

[0143] In practice, the range of application of the inherent strain can be determined through the following steps:

[0144] Determine the high-temperature mechanical property parameter change curve of the material, that is, the relationship curve between temperature and yield strength. Select the temperature value at which the yield strength decreases the fastest as the critical temperature T0. Taking 304 stainless steel as an example, the yield stress decreases the fastest at 700-850℃, so T0=850℃.

[0145] Determine the temperature gradient during the filling of each weld layer, using T0 as the temperature boundary, and determine the size of the region with a temperature greater than T0 on the cross-section perpendicular to the welding direction, i.e., the size of the region where the inherent strain is applied (refer to...). Figure 8 (W1, W2, and W3), the application area of ​​each weld layer presents Figure 8 The dashed trapezoid shown.

[0146] In this embodiment, after the application range is determined, the welding deformation can be calculated using the cooling method.

[0147] In practice, the target inherent strain parameter can be used as the thermal expansion coefficient of the material. The thermal expansion coefficient is assigned to the predefined inherent strain application area, and the temperature boundary condition is set to ΔT = -1℃, thereby obtaining the predicted welding deformation result of the target part corresponding to the target inherent strain parameter.

[0148] In this embodiment, based on multiple target inherent strain parameters in the target inherent strain parameter set, the predicted welding deformation results of multiple target parts can be obtained, thereby determining the predicted welding deformation range.

[0149] The following is a specific example.

[0150] like Figure 3 As shown, based on the weld length and plate thickness of the target part, combined with the welding process, the size and number of welds in the test piece are determined. k proportionally scaled-down welding test pieces of the actual plate thickness are constructed, and the experimental welding deformation data for each test piece are measured. The determined test piece dimensions need to include the following characteristics:

[0151] 1. The thickness of the test piece is equal to that of the target piece; 2. The welding process of the test piece is the same as that of the target piece, that is, the process parameters such as heat input, welding speed and welding wire diameter are kept consistent; 3. The number of weld layers and the number of weld segments in each layer of the test piece are consistent with those of the target piece.

[0152] During the welding process of the test piece, segmented welding is performed. The width of the test piece is 10-20 times the width of the maximum weld seam to ensure that the temperature of the edge of the test piece is close to room temperature. After each segment is welded, it is fully cooled to room temperature, and the data of the monitoring points are measured and recorded. Then, the next segment is welded and the data is recorded to obtain the test welding deformation data.

[0153] Based on experimental welding deformation data, a welding deformation simulation verification model was built using the thermo-elastic-plastic method to simulate the actual welding process of the test piece. In the model, the weld is divided into m layers and n segments to maintain consistency with reality. Each weld segment is activated sequentially using the birth and death element method to simulate the weld filling process.

[0154] In the simulation results, the range of inherent strain applied to each weld layer should be the same. Based on the distribution characteristics of welding temperature, the temperature is highest at the center of the heat source and gradually decreases towards the periphery. The interface of the inherent strain application range is approximately trapezoidal, and its size needs to be determined based on the temperature distribution and the high-temperature mechanical properties of the material. The specific method is as follows:

[0155] 1. Determine the high-temperature mechanical parameter variation curve of the material, i.e. the relationship curve between temperature and yield strength, and select the temperature value with the fastest rate of decrease in yield strength as the critical temperature T0. 2. Determine the temperature gradient during the filling of each weld layer, and use T0 as the temperature boundary line to determine the size of the area with a temperature greater than T0 on the cross section perpendicular to the welding direction.

[0156] Based on the welding results of the test specimen, the inherent strain value during the filling of each weld segment is extracted, i.e., the first inherent strain parameter. This allows us to obtain the spatial distribution of the inherent strain brought about by each weld segment. The first inherent strain parameter should include the longitudinal contraction strain in the x-direction. and transverse strain in the y direction ,in, x Along the length of the weld, y The direction is perpendicular to the length of the weld. z The direction is the thickness direction. k Represents the results of k different test specimens, where, Including longitudinal shrinkage strain of multiple welded sections , Including transverse strain of multiple welded sections , i and j Indicates the position of each welded segment. i =1~n, j =1~m.

[0157] The first natural strain parameter and Mapped onto the target component, it serves as the second inherent strain parameter at its true dimensions. and ,in, The longitudinal shrinkage strain in the x-direction of the target component. Let y be the transverse strain of the target component. Calculation method and same, and The mapping relationship is as follows:

[0158] ,

[0159] ,

[0160] ,

[0161] ,

[0162] .

[0163] If k test pieces are used to simulate experiments and k results are obtained, then the mean and standard deviation of each weld segment in the k simulations can be calculated:

[0164]

[0165]

[0166] in, s Number each welded segment. s The value range is 1~ S , S This represents the total number of welded segments, i.e., m*n.

[0167] Then, the correlation of inherent strain between different welded segments is calculated:

[0168]

[0169] in, The distance between two points. The fluctuation scale.

[0170] Discretization and sampling of random fields using KL expansion:

[0171]

[0172] in, Let i be the i-th characteristic function. For the corresponding eigenvalues, Given independent standard normal random variables, solve for the correlation function. The integral eigenvalue problem with the kernel can yield feature pairs. For S discrete points, this is transformed into an eigenvalue problem of an S*S matrix.

[0173] For M random variables Sampling is performed (e.g., sampling from N(0,1)), and the sampled values ​​are substituted into the KL expansion to generate corrected values ​​for the second inherent strain parameter that conform to statistical laws. and Multiple sampling is performed to obtain the set of target inherent strain parameters based on multiple correction values.

[0174] A simulation model of the target component is established, and welding simulation is performed. The inherent strain parameters of the target are applied to the simulation model. The application range of the inherent strain is determined by the temperature range of the thermo-elastic-plastic welding deformation simulation model of the test piece, thereby obtaining the predicted welding deformation result of the target component. Through multiple inherent strain parameters of the target, the predicted welding deformation range of the target component is finally obtained.

[0175] In some embodiments, such as Figure 9 As shown, this application embodiment also provides an electronic device 900, including a processor 901, a memory 902, and a computer program stored in the memory 902 and executable on the processor 901. When the program is executed by the processor 901, it implements the various processes of the above-described welding deformation prediction method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0176] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0177] This application also provides a non-transitory computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described welding deformation prediction method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0178] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0179] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described welding deformation prediction method.

[0180] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0181] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above-described welding deformation prediction method embodiment and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0182] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0183] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0184] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0185] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0186] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0187] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for predicting welding deformation, characterized in that, include: The experimental welding deformation data of the test specimen are obtained. The test specimen is built based on the target part to be predicted, and the thickness of the test specimen is the same as the thickness of the target part. Based on the experimental welding deformation data, thermo-elastic-plastic simulation was performed to determine the first inherent strain parameter of the test piece; Based on the first inherent strain parameter, the second inherent strain parameter of the target component is determined; Based on the second inherent strain parameter, random field samples are extracted to obtain the target inherent strain parameter set of the target component, which includes multiple target inherent strain parameters. Based on the target component's inherent strain parameter set, welding simulation is performed to obtain the predicted welding deformation range of the target component. The step of extracting random field samples based on the second inherent strain parameter to obtain the target inherent strain parameter set of the target component includes: Based on the second inherent strain parameter, random field samples are extracted using the KL expansion algorithm to obtain the target inherent strain parameter set; The test piece includes multiple first welded segments, and the weld of the target piece includes multiple second welded segments. The first welded segments and the second welded segments correspond one-to-one. The second inherent strain parameter includes multiple second inherent strain sub-parameters, and the second inherent strain sub-parameters correspond one-to-one with the second welded segments. The extraction of random field samples using the KL expansion algorithm includes: The second inherent strain gauge parameters are subjected to KL expansion to obtain the expansion result, which includes random variables; The random variable is sampled to determine the sampled value of the random variable; Substituting the sampled value into the expansion result, the correction value of the second inherent strain gauge parameter is obtained; The random field sample is obtained based on the correction values ​​of multiple second intrinsic strain parameters.

2. The welding deformation prediction method according to claim 1, characterized in that, The test piece includes multiple first welded segments, and the weld of the target piece includes multiple second welded segments. The first welded segments and the second welded segments correspond one-to-one. Obtaining the test weld deformation data of the test piece includes: The test piece is welded to form the first welded segment, and welding deformation data corresponding to the first welded segment is obtained when the temperature of the test piece is less than a first temperature threshold. The test welding deformation data includes welding deformation data corresponding to multiple first welded segments.

3. The welding deformation prediction method according to claim 2, characterized in that, When the test piece is welded, the temperature of the edge of the test piece away from the weld is less than the second temperature threshold.

4. The welding deformation prediction method according to claim 3, characterized in that, The dimension of the test piece in the direction perpendicular to the weld direction is greater than or equal to a preset size threshold, where the preset size threshold is K times the weld width, and K is greater than or equal to 10.

5. The welding deformation prediction method according to any one of claims 1-4, characterized in that, The process of performing thermo-elastic-plastic simulation based on the experimental welding deformation data to determine the first inherent strain parameter of the test piece includes: Based on the welding deformation data, a thermo-elastic-plastic simulation model was established; Extract welding deformation data and inherent strain parameters of the model; If the error between the model welding deformation data and the test welding deformation data is less than a preset error threshold, the model's inherent strain parameter is determined as the first inherent strain parameter.

6. The welding deformation prediction method according to claim 5, characterized in that, The welding deformation data of the model is obtained by activating the weld seams in the thermo-elastic-plastic simulation model sequentially according to the welding sequence of the test piece using the birth-death element method.

7. The welding deformation prediction method according to any one of claims 1-4, characterized in that, The welding simulation based on the target component's inherent strain parameter set yields the predicted welding deformation range of the target component, including: Obtain the model temperature range for thermo-elastic-plastic simulation based on the experimental welding deformation data; Based on the temperature range of the model, the range of inherent strain application is determined in the simulation model of the target component; Based on the inherent strain application range, the multiple target inherent strain parameters are applied to the simulation model of the target component to obtain the predicted welding deformation range of the target component.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the welding deformation prediction method as described in any one of claims 1-7.

Images