An X80 welding process inversion method based on thermal simulation-real welding-finite element coupling

Abstract

The present application relates to the technical field of welding process optimization and numerical simulation, and particularly relates to an X80 welding process inversion method based on thermal simulation-real welding-finite element coupling, which comprises the following steps: obtaining X80 pipeline steel information to be welded, establishing a local thermal cycle-structure-performance database, performing real welding test, collecting temperature history, weld forming parameters and post-weld residual stress data, establishing and calibrating a welding thermal-mechanical coupling finite element global process response model; inputting candidate welding process parameters into the model to obtain corresponding indexes; inputting local thermal cycle characteristic parameters into the local thermal cycle-structure-performance database to obtain structure, hardness and low-temperature toughness results; constructing a process parameter inversion solving model to obtain target welding process parameter combination under the constraint of target joint performance requirements, and outputting final process parameters after real welding verification; the present application can reduce the number of trial welds, shorten the process development cycle, and take into account low-temperature toughness, residual stress control and weld forming quality.

Description

Technical Field

[0001] This invention relates to the field of welding process optimization and numerical simulation technology, and in particular to an X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling. Background Technology

[0002] X80 pipeline steel is a crucial steel grade for manufacturing high-pressure transmission pipelines and long-distance oil and gas pipelines. Its welded joints must simultaneously meet requirements for low-temperature toughness, hardness, dimensional stability, and controllable residual stress. The weld heat-affected zone (HAZ) is often the weakest point in the joint performance, influenced by peak temperature, cooling time, welding heat input, and molten pool geometry. The actual welded HAZ is narrow and has a highly non-uniform microstructure, making it difficult to efficiently determine the optimal process using only conventional trial-and-error welding methods.

[0003] Current optimization methods for X80 pipeline steel welding processes mainly rely on extensive trial welding experiments, which suffers from drawbacks such as long experimental cycles, high costs, and low efficiency. Numerical simulation methods, such as finite element analysis, have become important tools for studying the welding process; however, existing methods generally require determining welding parameters through empirical formulas, making it difficult to accurately reflect the actual welding process. Currently, the main optimization methods for X80 pipeline steel welding processes include the following: Patent application number 202310737859.X discloses a method for predicting the cyclic cohesion model of hydrogen-induced fatigue crack propagation in subsea pipeline steel. This method is based on secondary development of finite element method and realizes the prediction of fatigue damage at the crack tip by writing subroutines, which is significantly different from the method in this paper.

[0004] Patent application No. 202311299347.6 discloses a numerical analysis method for hydrogen embrittlement of pipes under multi-field coupling conditions. This method is based on the assumption of hydrogen diffusion behavior to carry out numerical calculation analysis of hydrogen-induced fracture under multi-field direct coupling; it combines electrochemical hydrogen production and charging, and optimizes the theoretical relationship between hydrogen adhesion rate and metal cohesion based on experimental results; it is significantly different from the method described in this paper.

[0005] Patent application number 201310523648.2 discloses a process for reducing the sensitivity of X80 pipeline steel to cold cracking during welding. The method mainly reduces cold cracking through preheating, welding and post-weld heat treatment steps, but does not involve the precise optimization and inversion of welding process parameters.

[0006] Patent application number 201510320075.2 discloses a method for optimizing the submerged arc welding process parameters of pipeline steel. This method establishes a mathematical model by controlling the welding surface energy, weld reinforcement height and remelting amount of inner and outer welds, but lacks coupling verification with the actual welding process.

[0007] Patent application number 202211516128.4 discloses a gas shielded welding wire for X80 pipeline steel, its preparation and welding process. The method mainly focuses on the optimization of the welding wire composition and welding process, but does not provide a complete welding process inversion method.

[0008] Patent application number 202311836854.9 discloses an X80 pipeline steel with excellent low-temperature toughness in the heat-affected zone of a ring weld joint and its preparation method. The method focuses on material composition design rather than welding process inversion.

[0009] Patent application number 202410925892.X discloses an X80 pipeline steel welding wire and its laser-arc hybrid welding process. This method mainly focuses on optimizing the process parameters of a specific welding method, but does not provide an inversion method based on thermal simulation-actual welding-finite element coupling. Summary of the Invention

[0010] This invention provides an X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling. Under the premise of reducing the number of full-size trial welds, it utilizes the local thermal cycle-microstructure-property law obtained by thermal simulation, establishes a reliable finite element model using actual welding measurement data, and determines the X80 pipe submerged arc welding process parameters from the target toughness, target residual stress and target weld formation constraints through digital solution. This provides a new approach for the precise design of X80 pipeline steel welding process.

[0011] To avoid symbol confusion, the point of distance from the heat dissipation cycle in the Gleeble thermal simulation database is denoted as... ,in This represents the j-th preset peak temperature level. The k-th preset cooling time level is represented; the local thermal cycle characteristics at the i-th critical location under the candidate process parameter combination P are denoted as... , where i represents the heat-affected zone, the area adjacent to the fusion line, or other key locations to be evaluated.

[0012] To achieve the above objectives, the present invention employs the following technical solution: A method for inverting the X80 welding process based on thermal simulation-actual welding-finite element coupling includes the following steps: S1. Obtain information on the base material, joint type, plate thickness, welding method, welding material information, and boundary conditions of the X80 pipeline steel to be welded, and determine the performance requirements of the target joint; S2. Conduct Gleeble welding thermal simulation tests based on the X80 pipeline steel to be welded, at multiple preset peak temperatures T pUnder multiple preset cooling time combinations t8 / 5, the microstructure, hardness, and low-temperature toughness results under corresponding thermal cycling conditions were obtained, and a local thermal cycling-microstructure-property database D was established. loc ; where D loc The operating point of the heat dissipation cycle is obtained from Gleeble thermal simulation, and the operating point of the heat dissipation cycle is represented as... ,in This represents the j-th preset peak temperature level. This represents the k-th preset cooldown time level; S3. Conduct actual welding tests on X80 pipeline steel samples according to the benchmark welding process parameters to obtain experimental data for numerical model calibration; the experimental data includes: temperature-time history during welding, weld cross-sectional geometric parameters, and post-weld residual stress measurement results; S4. Based on the specimen geometry, material thermophysical parameters, high-temperature constitutive relations, joint type, and reference welding process parameters, establish a global process response model f using the coupled thermo-mechanical finite element method. glob (P), and set a finite element heat source model in the global process response model. The finite element heat source model is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process. The model is calibrated using the temperature-time history, weld cross-sectional geometric forming parameters and post-weld residual stress measurement results obtained in step S3. S5. Based on the feasible process range and target performance requirements of the joint to be welded, construct a candidate process parameter space. ; S6. Transfer candidate process parameter space The combination of process parameters P is input into the calibrated global process response model f. glob (P), to obtain the response results corresponding to each candidate process; the response results include two types of information: the first type is the local thermal cycle characteristic parameters of the i-th key position. The second category is the global welding response result, namely the maximum residual stress S after welding. max (P) and weld formation index G(P); S7. The local thermal cycle characteristic parameter C of the i-th key location obtained in step S6 is... i (P) Input the local thermal cycle-organism-performance database D established in step S2. loc By matching, interpolation, or fitting, the microstructure results M of the corresponding heat-affected zone and key areas near the fusion line are obtained. i (P), Hardness result HV i (P) and low-temperature toughness results A i (P); The tissue results M i (P), Hardness result HVi (P) and low-temperature toughness results A i (P) and the maximum residual stress S obtained in step S6 after welding max The welding process parameter inversion solution model is jointly input with the weld formation index G(P). This model uses the candidate process parameter space Ω as the search space and the target joint performance requirements as constraints to inversely determine the target welding process parameter combination. By constructing joint constraints including low-temperature toughness constraints, hardness constraints, post-weld maximum residual stress constraints, and weld formation allowable domain constraints, the target welding process parameter combination P that meets the target requirements is obtained. * ; S8. Using the target welding process parameter combination P obtained in step S7 * A verification weld was performed, and the low-temperature toughness, hardness, residual stress, and weld formation of the verification joint were tested. When the verification results met the preset target joint performance requirements, the target welding process parameter combination P was applied. * As the final process parameter output, if the verification result does not meet the preset target requirements, the verification result is fed back to step S4 to affect the global process response model f. glob (P) After correction, the inversion solution is re-executed until the final combination of process parameters that meets the target requirements is obtained.

[0013] Furthermore, the base material information includes chemical composition, initial microstructure, room temperature and high temperature mechanical properties, thermophysical parameters, and phase transformation-related parameters; the joint type includes bevel type, assembly dimensions, and weld geometry requirements; the target joint performance requirements include low-temperature toughness requirements, local hardness control requirements, post-weld maximum residual stress control requirements, and weld formation requirements for the heat-affected zone and critical areas adjacent to the fusion line; the target joint performance requirements are used to define the constraint boundaries for subsequent process parameter inversion.

[0014] Furthermore, the peak temperature T p The temperature range is 700–1300℃, and the t8 / 5 range is 10–100 s; where t8 / 5 is the time required to cool from 800℃ to 500℃; the local thermal cycle-microstructure-property database D loc Different heat dissipation cycle operating points For input, where For the j-th preset peak temperature level, For the k-th preset cooling time level; the database D loc With the corresponding microstructure results Hardness results and low temperature toughness results The output is used to characterize the mapping relationship between local thermal cycling conditions and local tissue properties.

[0015] Furthermore, the temperature-time history is collected by thermocouples arranged near the weld centerline and at different locations in the heat-affected zone; the weld geometry parameters include at least one of weld penetration and weld width; and the post-weld residual stress is obtained by blind hole method, strain method or X-ray method.

[0016] Furthermore, the global process response model f glob (P) The process parameter vector P is taken as input, and the local thermal cycle characteristics, maximum residual stress, and weld formation results at key locations are taken as output; the process parameter vector P includes welding current I, welding voltage U, welding speed v, thermal efficiency η, and preheating temperature T. pre Interlayer temperature T inter One or more of the following: line energy Q, assembly constraints, or structural boundary parameters Γ; a finite element heat source model is set in the welding thermo-mechanical coupled finite element global process response model, which is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process; the finite element heat source model adopts a double ellipsoidal heat source model, and is jointly calibrated by the weld cross-sectional geometry, temperature history at characteristic locations, and post-weld residual stress. After calibration, the global process response model f glob (P) is used to calculate the characteristic parameter C of local thermal cycling at critical locations under a given combination of process parameters. i (P), Maximum residual stress after welding S max (P) and weld formation index G(P).

[0017] Furthermore, the candidate process parameter space It consists of multiple combinations of process parameters to be evaluated, and each combination of process parameters corresponds to a unique process parameter vector P; the candidate process parameter space is constructed using a discrete enumeration method or a continuous interval method.

[0018] Furthermore, the construction includes a combined constraint condition encompassing low-temperature toughness constraints, hardness constraints, post-weld maximum residual stress constraints, and weld formation constraints, specifically including: Low-temperature toughness meets the following requirements: ; Hardness requirement: ; The maximum residual stress after welding must satisfy: ; The weld formation meets the preset forming requirements: ; in, This represents the low-temperature toughness result at the i-th critical location under candidate process parameter combination P. This represents the minimum low-temperature toughness target value for the i-th critical location; This represents the hardness result at the i-th critical location under candidate process parameter combination P. This represents the upper limit of the allowable hardness at the i-th critical location; The maximum residual stress after welding under candidate process parameter combination P. Ω represents the upper limit of the maximum allowable residual stress after welding; G(P) is the weld formation index under the candidate process parameter combination P. G This refers to the allowable area for weld formation.

[0019] Furthermore, based on simultaneously satisfying low-temperature toughness constraints, hardness constraints, post-weld maximum residual stress constraints, and weld formation constraints, an objective function is constructed as follows: ; in, P is the normalized maximum residual stress index; A (P) represents a penalty for insufficient low-temperature toughness; P HV (P) represents the penalty for exceeding the hardness limit; P G (P) is the penalty item for weld formation deviation; λ1~λ4 are the weight coefficients of the corresponding indicators, and their values ​​are set according to the requirements of engineering applications; Target welding process parameter combination P * Represented as: ; It also simultaneously satisfies low-temperature toughness constraints, hardness constraints, maximum residual stress constraints after welding, and weld formation constraints.

[0020] Compared with the prior art, the beneficial effects of the present invention are: 1) This invention combines Gleeble thermal simulation, actual welding tests, finite element analysis, and optimization algorithm analysis to establish a local thermal cycle-microstructure-performance database and a global process response model, enabling quantitative reverse calculation of welding process parameters from target joint performance. Compared with traditional experience-based trial welding methods, it can more accurately optimize the welding process of X80 pipeline steel while simultaneously considering low-temperature toughness, residual stress, and weld formation quality. 2) This invention can quickly screen candidate processes before actual welding, reduce repeated trial welding, repeated testing and material consumption, significantly improve process development efficiency and shorten the R&D cycle; it is expected to reduce the number of welding process development tests by more than 30% and shorten the overall R&D cycle by more than 20%, thereby reducing R&D costs and equipment occupancy costs. 3) This invention optimizes the process by incorporating the toughness, hardness, maximum residual stress, and weld formation of key areas into a unified constraint framework. This helps reduce the risk of welding rework, scrap, and service failure, improves the success rate of process formulation at the first time, and enhances the stability of on-site implementation. It has good engineering application value and promotion prospects. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0022] Figure 2 The database D described in this invention loc A flowchart illustrating the creation process.

[0023] Figure 3 This is a schematic diagram of the actual welding and process output process for verifying the combination of target welding process parameters of the present invention. Detailed Implementation

[0024] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings: See Figure 1 This is a schematic diagram of the method flow of the present invention. The present invention provides an X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling. This method takes the welding of hot-rolled coil X80 pipeline steel pipes as the object, the target joint performance requirements as input, and welding process parameters as the inversion object. Through a multi-layer coupling technical route combining a "local thermal cycle-microstructure-performance database" and a "global process response model," it achieves quantitative inversion, optimization screening, and verification output of welding process parameters. The method of the present invention includes the following steps: S1. Obtain information on the base material, joint type, plate thickness, welding method, welding material information, and boundary conditions of the X80 pipeline steel to be welded, and determine the performance requirements of the target joint; The base material information includes chemical composition, initial microstructure, room temperature and high temperature mechanical properties, thermophysical parameters, and phase transformation-related parameters; the joint type includes bevel type, assembly dimensions, and weld geometry requirements; the target joint performance requirements include low-temperature toughness requirements, local hardness control requirements, maximum residual stress control requirements after welding, and weld formation requirements in the heat-affected zone and critical areas adjacent to the fusion line; the target joint performance requirements are used to define the constraint boundaries for subsequent process parameter inversion.

[0025] S2. Conduct Gleeble welding thermal simulation tests based on the X80 pipeline steel to be welded, at multiple preset peak temperatures T p Under multiple preset cooling time combinations t8 / 5, the microstructure, hardness, and low-temperature toughness results under corresponding thermal cycling conditions were obtained, and a local thermal cycling-microstructure-property database D was established. loc Wherein, the distance from the heat dissipation cycle operating point is represented as ; The peak temperature T p The temperature range is 700–1300℃, and the cooling time t8 / 5 ranges from 10 to 100 seconds, where t8 / 5 is the time required to cool from 800℃ to 500℃; the local thermal cycle-microstructure-property database D locDifferent heat dissipation cycle operating points For input, where For the j-th preset peak temperature level, For the k-th preset cooling time level; the database D loc With the corresponding microstructure results Hardness results and low temperature toughness results The output is used to characterize the mapping relationship between local thermal cycling conditions and local tissue properties.

[0026] S3. Conduct actual welding tests on X80 pipeline steel samples according to the reference welding process parameters to obtain experimental data for numerical model calibration; the experimental data includes: temperature-time history during welding, weld cross-sectional geometric parameters, and post-weld residual stress measurement results; the temperature-time history is collected by thermocouples arranged near the weld centerline and at different positions in the heat-affected zone; the weld geometric parameters include at least one of penetration depth and weld width; the post-weld residual stress is obtained by blind hole method, strain method or X-ray method.

[0027] S4. Based on the specimen geometry, material thermophysical parameters, high-temperature constitutive relations, joint type, and reference welding process parameters, establish a global process response model f using the coupled thermo-mechanical finite element method. glob (P), and set a finite element heat source model in the global process response model. The finite element heat source model is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process. The model is calibrated using the temperature-time history, weld section geometry parameters and post-weld residual stress measurement results obtained in step S3. The global process response model f glob (P) The process parameter vector P is taken as input, and the local thermal cycle characteristics, maximum residual stress, and weld formation results at key locations are taken as output; the process parameter vector P includes welding current I, welding voltage U, welding speed v, thermal efficiency η, and preheating temperature T. pre Interlayer temperature T inter One or more of the following: line energy Q, assembly constraints, or structural boundary parameters Γ; a finite element heat source model is set in the welding thermo-mechanical coupled finite element global process response model. The finite element heat source model is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process. The finite element heat source model adopts a double ellipsoidal heat source model and is jointly calibrated by the weld section geometry, temperature history at characteristic locations, and post-weld residual stress to improve the model's characterization accuracy of the welding thermal process and stress evolution process; after calibration, the global process response model f glob (P) is used to calculate the characteristic parameter C of the local thermal cycle at key locations. i(P), Maximum residual stress after welding S max (P) and weld formation index G(P).

[0028] S5. Based on the feasible process range and target performance requirements of the joint to be welded, construct a candidate process parameter space. ; The candidate process parameter space The candidate process parameter space consists of multiple combinations of process parameters to be evaluated, each combination corresponding to a unique process parameter vector P; The candidate process parameter space is constructed using either discrete enumeration or continuous interval methods. At least the welding current, welding voltage, and welding speed should be set so that the candidate parameters cover a reasonable process window that meets the welding requirements of X80 pipeline steel pipes.

[0029] S6. Transfer candidate process parameter space The combination of process parameters P is input into the calibrated global process response model f. glob (P), to obtain the response results corresponding to each candidate process; the response results include two types of information: the first type is the local thermal cycle characteristic parameters of the i-th key position. The second category is the global welding response result, namely the maximum residual stress S after welding. max (P) and weld formation index G(P); provide input for subsequent local microstructure performance evaluation and process parameter inversion.

[0030] S7. The local thermal cycle characteristic parameter C of the i-th key location obtained in step S6 is... i (P) Input the local thermal cycle-organism-performance database D established in step S2. loc By matching, interpolation, or fitting, the microstructure results M of the corresponding heat-affected zone and key areas near the fusion line are obtained. i (P), Hardness result HV i (P) and low-temperature toughness results A i (P); The hardness result HV i (P) and low-temperature toughness results A i (P) and the maximum residual stress S obtained in step S6 after welding maxThe welding process parameter inversion solution model is jointly input with the weld formation index G. This model is a constraint optimization model that uses the candidate process parameter space Ω as the search space and the target joint performance requirements as constraints to inversely determine the target welding process parameter combination. It constructs joint constraint conditions including low-temperature toughness constraints, hardness constraints, maximum post-weld residual stress constraints, and weld formation allowable domain constraints. Simultaneously satisfying local toughness constraints, hardness constraints, maximum post-weld residual stress constraints, and weld formation constraints, the final target welding process parameter combination P that meets the target requirements is obtained. * ; The solution process applies the following joint constraints to the candidate process parameter combination P; Its low-temperature toughness should meet the following requirements: ; Hardness requirement: ; The maximum residual stress after welding must satisfy: ; The weld formation meets the preset forming requirements: ; in, This represents the low-temperature toughness result at the i-th critical location under candidate process parameter combination P. This represents the minimum low-temperature toughness target value for the i-th critical location; This represents the hardness result at the i-th critical location under candidate process parameter combination P. This represents the upper limit of the allowable hardness at the i-th critical location; The maximum residual stress after welding under candidate process parameter combination P. Ω represents the upper limit of the maximum allowable residual stress after welding; G(P) is the weld formation index under the candidate process parameter combination P. G This refers to the allowable area for weld formation.

[0031] Based on the above constraints, an objective function F(P) is further constructed to optimize the feasible solution. Preferably, the objective function is: ; in, P is the normalized maximum residual stress index; A (P) represents a penalty for insufficient resilience; P HV (P) represents the penalty for exceeding the hardness limit; P G (P) is the penalty item for weld formation deviation; λ1~λ4 are the weight coefficients of the corresponding indicators, and their values ​​are set according to the requirements of engineering applications; Target welding process parameter combination P * It can be represented as: ; It simultaneously satisfies the low-temperature toughness constraint, hardness constraint, maximum residual stress constraint after welding, and weld formation constraint; the iterative search or optimization solution can employ particle swarm optimization algorithm, genetic algorithm, non-dominated sorting genetic algorithm, Bayesian optimization algorithm, or a combination thereof.

[0032] S8. Using the target welding process parameter combination P obtained in step S7 * A verification weld was performed, and the low-temperature toughness, hardness, residual stress, and weld formation of the verification joint were tested. When the verification results met the preset target joint performance requirements, the target welding process parameter combination P was applied. * As the final process parameter output, if the verification result does not meet the preset target requirements, the verification result is fed back to step S4 to affect the global process response model f. glob (P) After correction, the inversion solution is re-executed until the final combination of process parameters that meets the target requirements is obtained.

[0033] The following embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments. Unless otherwise specified, the methods used in the following embodiments are conventional methods.

[0034] Example: See Figure 1 A method for inverting the X80 welding process based on thermal simulation-actual welding-finite element coupling includes the following steps: S1. First, obtain the basic information of the X80 pipeline steel to be welded, including the base material chemical composition, plate thickness, initial microstructure, room temperature mechanical properties, high temperature mechanical properties, thermophysical parameters, joint type, welding material information, welding method, assembly conditions, and boundary constraints. In this embodiment, the welding method is preferably submerged arc welding. Set the target joint performance requirements according to the engineering requirements. The target joint performance requirements include at least one or more of the following: the heat-affected zone and the critical area adjacent to the fusion line have low-temperature toughness that meets the service requirements; the hardness at the critical position is not higher than the preset upper limit; the maximum residual stress after welding is controlled within the allowable range; and weld formation requirements.

[0035] S2, see Figure 2 Welding thermal simulation tests were conducted on X80 pipeline steel using the Gleeble thermal simulation equipment; specifically, multiple peak temperatures T were set. p Multiple cooling times t8 / 5 were used, where t8 / 5 is the time required to cool from 800℃ to 500℃. Peak temperatures were selected at fixed t8 / 5 conditions, including 700℃, 850℃, 1000℃, 1150℃, and 1300℃; and multiple t8 / 5 conditions, including 10s, 25s, 50s, 75s, and 100s, were selected at fixed peak temperatures. The cooling cycle operating points were considered. Samples were prepared, and their microstructure, hardness, and low-temperature impact toughness were tested respectively. A local thermal cycling-microstructure-property database D was constructed. loc .

[0036] S3. Conduct actual welding tests on X80 pipeline steel samples according to the reference welding process parameters to obtain the experimental data required for establishing and calibrating the global process response model; during the welding process, thermocouples are arranged in the area near the center line of the weld to record the temperature-time history during the welding process; in this embodiment, the blind hole method is used to determine the welding residual stress, and the cross-section of the welded joint is cut after welding to measure the weld penetration depth, weld width and other cross-sectional forming parameters.

[0037] S4. Based on the geometric dimensions of the welded specimen, material thermophysical parameters, high-temperature constitutive relations, joint type, and reference welding process parameters, establish a global process response model f using the coupled finite element method for welding thermo-mechanical processes. glob (P); A finite element heat source model is set in the model, which is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process. In this embodiment, a double ellipsoidal heat source model is preferably used to simulate welding heat input. By adjusting the heat source parameters and related model parameters, the weld cross-sectional dimensions, temperature-time history, and post-weld residual stress results calculated by the global process response model are made as consistent as possible with the measured results obtained in step S3, thereby achieving model calibration.

[0038] S5. Based on the feasible scope of on-site production processes, equipment capabilities, and target joint performance requirements, construct a candidate process parameter space around welding current, welding voltage, and welding speed. Each set of candidate process parameter combinations corresponds to a unique process parameter vector.

[0039] S6. Transfer candidate process parameter space Each combination of process parameters P is input into the global process response model f one by one. glob (P), to obtain the local thermal cycle characteristic parameters of the i-th critical location under this combination of process parameters. Maximum residual stress S after welding max (P) and weld formation index G(P).

[0040] S7. The local thermal cycle characteristic parameter C of the i-th key location obtained in step S6 is... i (P) Input the local thermal cycle-organism-performance database D established in step S2. loc By matching, interpolation, or fitting, the microstructure results M of the corresponding heat-affected zone and key areas near the fusion line are obtained. i (P), Hardness result HV i (P) and low-temperature toughness results A i(P); The HV i (P), A i (P), S max Both P and G(P) are input into the welding process parameter inversion solution model; the welding process parameter inversion solution model is a constrained optimization model that uses the candidate process parameter space Ω as the search space and the target joint performance requirements as constraints to inversely obtain the target welding process parameter combination, and finally obtains the target welding process parameter combination P that meets the target requirements. * In this embodiment, the inversion solution process adopts the particle swarm optimization algorithm, and finally obtains the target welding process parameter combination that meets the performance requirements of the target joint in the candidate process parameter space Ω.

[0041] S8, see Figure 3 The target welding process parameter combination P obtained in step S7 is used. * A verification weld was performed, and the low-temperature toughness, hardness, residual stress, and weld formation of the verification joint were tested. When the verification results met the preset target joint performance requirements, the target welding process parameter combination P was applied. * As the final process parameter output, if the verification result does not meet the preset target requirements, the verification result is fed back to step S4 to affect the global process response model f. glob (P) After correction, the inversion solution is re-executed until the final combination of process parameters that meets the target requirements is obtained; Table 1 shows the inversion optimization welding process parameter combination P. * Comparison of low-temperature impact toughness results of welds under the same welding process parameter combination as the original: Table 1 Comparison of low-temperature impact toughness results of welds at -20℃ before and after welding process parameter optimization. Unoptimized 145 160 238 302 optimization 194 263 286 302 .

Claims

1. A method for inverting X80 welding process based on thermal simulation-actual welding-finite element coupling, characterized in that, Includes the following steps: S1. Obtain information on the base material, joint type, plate thickness, welding method, welding material information, and boundary conditions of the X80 pipeline steel to be welded, and determine the performance requirements of the target joint; S2. Conduct Gleeble welding thermal simulation tests based on the X80 pipeline steel to be welded, at multiple preset peak temperatures T p Under multiple preset cooling time combinations t8 / 5, the microstructure, hardness, and low-temperature toughness results under corresponding thermal cycling conditions were obtained, and a local thermal cycling-microstructure-property database D was established. loc ; Among them, D loc The operating point of the heat dissipation cycle is obtained from Gleeble thermal simulation, and the operating point of the heat dissipation cycle is represented as... , This represents the j-th preset peak temperature level. This represents the k-th preset cooldown time level; S3. Conduct actual welding tests on X80 pipeline steel samples according to the benchmark welding process parameters to obtain experimental data for numerical model calibration; the experimental data includes: temperature-time history during welding, weld cross-sectional geometric parameters, and post-weld residual stress measurement results; S4. Based on the specimen geometry, material thermophysical parameters, high-temperature constitutive relations, joint type, and reference welding process parameters, establish a global process response model f using the coupled thermo-mechanical finite element method. glob (P), and set a finite element heat source model in the global process response model. The finite element heat source model is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process. The model is calibrated using the temperature-time history, weld cross-sectional geometric forming parameters and post-weld residual stress measurement results obtained in step S3. S5. Based on the feasible process range and target performance requirements of the joint to be welded, construct a candidate process parameter space. ; S6. Transfer candidate process parameter space The combination of process parameters P is input into the calibrated global process response model f. glob (P), to obtain the response results corresponding to each candidate process; the response results include two types of information: the first type is the local thermal cycle characteristic parameters of the i-th key position. The second category is the global welding response result, namely the maximum residual stress S after welding. max (P) and weld formation index G(P); S7. The local thermal cycle characteristic parameter C of the i-th key location obtained in step S6 is... i (P) Input the local thermal cycle-organism-performance database D established in step S2. loc By matching, interpolation, or fitting, the microstructure results M of the corresponding heat-affected zone and key areas near the fusion line are obtained. i (P), Hardness result HV i (P) and low-temperature toughness results A i (P); The tissue results M i (P), Hardness result HV i (P) and low-temperature toughness results A i (P) and the maximum residual stress S obtained in step S6 after welding max The welding process parameter inversion solution model is jointly input with the weld formation index G(P). This model uses the candidate process parameter space Ω as the search space and the target joint performance requirements as constraints to inversely determine the target welding process parameter combination. By constructing joint constraints including low-temperature toughness constraints, hardness constraints, post-weld maximum residual stress constraints, and weld formation allowable domain constraints, the target welding process parameter combination P that meets the target requirements is obtained. * ; S8. Using the target welding process parameter combination P obtained in step S7 * A verification weld was performed, and the low-temperature toughness, hardness, residual stress, and weld formation of the verification joint were tested. When the verification results met the preset target joint performance requirements, the target welding process parameter combination P was applied. * As the final process parameter output, if the verification result does not meet the preset target requirements, the verification result is fed back to step S4 to affect the global process response model f. glob (P) After correction, the inversion solution is re-executed until the final combination of process parameters that meets the target requirements is obtained.

2. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 1, characterized in that, The base material information includes chemical composition, initial microstructure, room temperature and high temperature mechanical properties, thermophysical parameters, and phase transformation-related parameters; the joint type includes bevel type, assembly dimensions, and weld geometry requirements; the target joint performance requirements include low-temperature toughness requirements, local hardness control requirements, maximum residual stress control requirements after welding, and weld formation requirements in the heat-affected zone and critical areas adjacent to the fusion line; the target joint performance requirements are used to define the constraint boundaries for subsequent process parameter inversion.

3. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 1, characterized in that, The peak temperature T p The temperature range is 700–1300℃, and the t8 / 5 range is 10–100s; the local thermal cycle-microstructure-property database D loc Different heat dissipation cycle operating points For input, where For the j-th preset peak temperature level, For the k-th preset cooling time level; the database D loc With the corresponding microstructure results Hardness results and low temperature toughness results The output is used to characterize the mapping relationship between local thermal cycling conditions and local tissue properties.

4. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 1, characterized in that, The temperature-time history is collected by thermocouples placed near the weld centerline and at different locations in the heat-affected zone; the weld geometry parameters include at least one of weld penetration and weld width; the post-weld residual stress is obtained by blind hole method, strain method or X-ray method.

5. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 1, characterized in that, The global process response model f glob (P) takes the process parameter vector P as input and outputs the local thermal cycle characteristics, maximum residual stress and weld formation results at key locations; The process parameter vector P includes welding current I, welding voltage U, welding speed v, thermal efficiency η, and preheating temperature T. pre Interlayer temperature T inter One or more of the following: line energy Q, assembly constraints, or structural boundary parameters Γ; a finite element heat source model is set in the welding thermo-mechanical coupled finite element global process response model, which is used to describe the spatial distribution of welding heat input in the workpiece and the moving loading process; The finite element heat source model adopts a double ellipsoidal heat source model, and is jointly calibrated by weld cross-sectional geometry, temperature history at characteristic locations, and post-weld residual stress. After calibration, the global process response model f is obtained. glob (P) is used to calculate the characteristic parameter C of local thermal cycling at critical locations under a given combination of process parameters. i (P), Maximum residual stress after welding S max (P) and weld formation index G(P).

6. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 5, characterized in that, The candidate process parameter space It consists of multiple combinations of process parameters to be evaluated, and each combination of process parameters corresponds to a unique process parameter vector P; the candidate process parameter space is constructed using a discrete enumeration method or a continuous interval method.

7. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 1, characterized in that, The construction includes combined constraint conditions encompassing low-temperature toughness constraints, hardness constraints, post-weld maximum residual stress constraints, and weld formation constraints, specifically including: Low-temperature toughness meets the following requirements: ; Hardness requirement: ; The maximum residual stress after welding must satisfy: ; The weld formation meets the preset forming requirements: ; in, This represents the low-temperature toughness result at the i-th critical location under candidate process parameter combination P. This represents the minimum low-temperature toughness target value for the i-th critical location; This represents the hardness result at the i-th critical location under candidate process parameter combination P. This represents the upper limit of the allowable hardness at the i-th critical location; The maximum residual stress after welding under candidate process parameter combination P. Ω represents the upper limit of the maximum allowable residual stress after welding; G(P) is the weld formation index under the candidate process parameter combination P. G This refers to the allowable area for weld formation.

8. The X80 welding process inversion method based on thermal simulation-actual welding-finite element coupling according to claim 7, characterized in that, Based on simultaneously satisfying low-temperature toughness constraints, hardness constraints, maximum post-weld residual stress constraints, and weld formation constraints, an objective function is constructed as follows: ； in, P is the normalized maximum residual stress index; A (P) represents a penalty for insufficient low-temperature toughness; P HV (P) represents the penalty for exceeding the hardness limit; P G (P) is the penalty item for weld formation deviation; λ1~λ4 are the weight coefficients of the corresponding indicators, and their values ​​are set according to the requirements of engineering applications; Target welding process parameter combination P * Represented as: ; It also simultaneously satisfies the low-temperature toughness constraint, hardness constraint, maximum residual stress constraint after welding, and weld formation constraint.

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