A method for optimizing laser shock process parameters for steel structure welding connection

Abstract

The application discloses a kind of steel structure welding connection laser impact process parameter optimization method, belong to structural material surface treatment technical field.The method first obtains welding connection initial residual stress field and microstructure characteristics of five kinds of organization volume fraction, weighted average grain size;After being strengthened by different laser impact process parameters, new microstructure characteristics are measured, its prediction model is established, and the residual stress field after laser impact is obtained by finite element simulation;Combining fatigue test and simulation, the fatigue damage evolution equation considering the influence of microstructure is constructed;Finally, the microstructure characteristics and residual stress field are introduced into the finite element model to predict fatigue life, and the optimal process parameters that maximize fatigue life are determined by parameter analysis.The application establishes the correlation between process parameters, microstructure, residual stress and fatigue performance, realizes high-precision prediction of fatigue life, greatly reduces test cost, has high optimization efficiency, and is suitable for various steel structure welding connections.

Description

Technical Field

[0001] This invention relates to the field of surface treatment technology for structural materials, and in particular to a method for optimizing laser shock blasting process parameters for welded connections of steel structures. Background Technology

[0002] With the rapid development of my country's offshore wind power industry, the deployment of offshore wind turbines is gradually expanding from nearshore shallow water areas to deep-sea and mid-deep-sea areas. Due to its excellent mechanical properties and economic adaptability, jacket foundations have become the most widely used foundation structure for offshore wind turbines in medium-deep waters. Among these, the steel pipe joint, where the branch pipe is directly welded to the main pipe wall, is the most common joint type for jacket foundations. During actual service, the steel pipe joint is subjected to alternating marine environmental loads such as wind, waves, and currents. Due to geometrical abrupt changes and deterioration of connection performance near the intersecting welds, fatigue cracks are easily induced in the weld area or heat-affected zone, gradually proliferating and ultimately leading to fatigue failure of the steel pipe joint. Therefore, improving the fatigue performance of welded connections is of great significance for ensuring the safety of offshore wind power platform structures and extending their service life.

[0003] To improve the fatigue performance of welded joints, surface strengthening techniques are commonly used in engineering, such as shot peening, ultrasonic shock peening, high-frequency mechanical shock peening, and laser shock peening. Among these, laser shock peening is an advanced surface strengthening technique that utilizes a high-energy pulsed laser to act on the material surface, generating plasma shock waves under the action of an absorption layer and a confinement layer. This induces high-amplitude residual compressive stress in the surface and near-surface layers of the material and guides the evolution of the material's microstructure. Compared with traditional surface strengthening techniques, laser shock peening offers advantages such as greater strengthening depth, stable strengthening effect, the ability to achieve precise local strengthening, and applicability to complex curved surfaces. It can significantly improve the fatigue resistance, corrosion resistance, and wear resistance of critical structural components and is currently widely used in aerospace, energy and power, automotive manufacturing, and medical device industries.

[0004] Numerous studies have shown that the residual stress state and microstructure characteristics of materials are crucial factors influencing the fatigue performance of structures. During laser shock peening (LSP), different laser process parameters, such as laser spot diameter, single-pulse laser energy, laser pulse width, and overlap ratio, significantly affect the distribution of residual stress and the microstructure of the strengthened material. Therefore, the rational selection of LSP process parameters plays a vital role in improving the fatigue performance of welded joints. However, existing research largely focuses on analyzing the impact of laser shock on residual stress distribution, with relatively little attention paid to the evolution of the microstructure after laser shock. Furthermore, a systematic study of the cross-scale correlation between LSP process parameters, microstructure characteristics, residual stress, and fatigue performance is still lacking, which to some extent increases the difficulty of effectively controlling the strengthening effect of laser shock peening.

[0005] On the other hand, current methods for predicting the fatigue life of welded connections in steel structures are typically based on S-N curves established through fatigue tests. These methods often fail to adequately consider the combined effects of various factors, such as material microstructure characteristics, residual stress distribution, and the actual stress time history of the structure, resulting in limited accuracy in fatigue life prediction and failing to provide a reliable basis for optimizing laser shock peening process parameters. Chinese patents 202110067373.0 ("Rapid Prediction Method and Device for Crack Propagation Life of Laser Shock-Strengthened Components"), 202210103839.2 ("A Fatigue Life Prediction Method Considering the Effect of Laser Shock Strengthening"), and 202011236769.5 ("A Visual Evaluation Method for Fatigue Life of Materials After Laser Shot Peening") only consider the influence of residual stress on fatigue life and do not yet combine material microstructure characteristics with residual stress for comprehensive prediction.

[0006] Fatigue testing is typically time-consuming and costly, making it difficult to conduct on a large scale before engineering applications. This has become a significant factor restricting the optimization of laser shock blasting process parameters for welded steel structures. Therefore, there is an urgent need for a predictive method that can comprehensively consider the relationship between laser shock blasting process parameters, microstructure characteristics, residual stress, and fatigue performance. This method would enable high-precision prediction of the fatigue life of welded connections after laser shock blasting, providing a valid basis for optimizing laser shock blasting process parameters for welded steel structures. Summary of the Invention

[0007] To address the aforementioned issues, this invention aims to propose a method for optimizing laser shock blasting process parameters in steel structure welded connections. By establishing the correlation between laser shock blasting process parameters, microstructure characteristics, residual stress, and fatigue performance, and combining this with finite element simulation, a high-precision prediction of the fatigue life of welded connections after laser shock blasting is achieved, providing an efficient tool for optimizing laser shock blasting process parameters in welded connections.

[0008] To achieve the above objectives, the technical solution of the present invention is implemented as follows: A method for optimizing laser shock blasting process parameters for welded connections in steel structures includes the following steps: S1. Obtain the initial residual stress field and microstructure characteristics of the welded joint, wherein the microstructure characteristics include the volume fraction and weighted average grain size of martensite, bainite, ferrite, pearlite, and austenite. S2. Strengthen the welded joint under different laser shock process parameters to obtain the microstructure characteristics after laser shock. S3. Using laser shock process parameters as independent variables and microstructure characteristics after laser shock as dependent variables, establish a microstructure characteristic prediction model. S4. A finite element model is established by combining the measured dimensions of the welded connection with the initial residual stress field. The spatiotemporal distribution of the laser shock load and the material elastoplastic constitutive model are defined, and the residual stress field of the welded connection after laser shock is obtained through simulation. S5. Combining fatigue tests and finite element simulations, establish a fatigue damage evolution equation that considers the influence of microstructure characteristics; S6. Introduce the microstructure characteristics and residual stress field after laser shock into the finite element model for fatigue life prediction. Determine the combination of laser shock process parameters that maximizes the fatigue life of the welded connection through parameter analysis, and complete parameter optimization.

[0009] Furthermore, in step S1, the initial residual stress field of the welded joint is obtained through X-ray diffraction or neutron diffraction, and the microstructure characteristics are obtained through electron backscattering diffraction; the weighted average grain size is calculated according to the formula... Calculate, where d AVE f is the weighted average grain size for various microstructures; M f B f F f P and f A d represents the volume fraction of martensite, bainite, ferrite, pearlite, and austenite, respectively; M d B d F d P and d A These represent the average grain sizes of martensite, bainite, ferrite, pearlite, and austenite structures, respectively.

[0010] Furthermore, in step S2, the laser shock process parameters include laser spot diameter, single-pulse laser energy, laser pulse width, overlap rate, number of laser shocks, laser shock path, and laser shock area; the laser shock area is fixed as the weld seam area and heat-affected zone of the welded connection, and the laser shock path is fixed as a point-by-point serpentine scanning laser shock.

[0011] Furthermore, in step S3, the microstructure feature prediction model is established through regression analysis or machine learning methods. Before modeling, outlier detection and standardization preprocessing are performed on the sample data of process parameters and microstructure features.

[0012] Furthermore, in step S4, the spatiotemporal distribution of the laser shock load is calculated according to the following derived formula:

[0013]

[0014]

[0015]

[0016] Where P(r,t) is the laser shock load; P(t) is the shock load at the center of the incident laser; r is the distance from the center of the incident laser; t is the impact time; ρ is the laser spot diameter; P max τ is the peak impact load at the center of the incident laser; τ is the laser pulse width; Δτ is the time for the impact load to decay from the peak to zero; K is the ratio of plasma thermal energy to internal energy; Z1 and Z2 are the acoustic impedances of the steel and confinement layer materials, respectively; κ is the plasma adiabatic index; I0 is the laser power density; E s This refers to the energy of a single-pulse laser. The elastic-plastic constitutive model of the material is adopted using the Johnson-Cook model and calculated according to the following formula:

[0017] Where, σ 0 For flow stress; σ y Quasi-static yield strength; , and These are the equivalent plastic strain, equivalent plastic strain rate, and reference strain rate, respectively; K, m, and Y are material constants.

[0018] Furthermore, in step S5, the specific process of establishing the fatigue damage evolution equation is as follows: fatigue tests are carried out on the welded joints after different laser shock process parameters, and the cracking location and corresponding fatigue life are recorded; a finite element model is established based on the measured welded joint dimensions, the residual stress field after laser shock is introduced and fatigue load is applied, and the cyclic stress amplitude at the cracking location is obtained through finite element simulation; based on the fatigue life and cyclic stress amplitude at the cracking location of the welded joint, a fatigue damage evolution equation with microstructure characteristics as the independent variable is established.

[0019] Furthermore, the fatigue damage evolution equation is calculated according to the following formula:

[0020]

[0021] Where β, η, and M are the models to be established; f(R) is the stress ratio correction coefficient; D is the damage variable, and fracture failure occurs when D=1; N is the number of cycles; S ij,max and S ij,min This represents the maximum and minimum values ​​of the deviatoric stress tensor within a loading cycle.

[0022] Furthermore, in step S2, the Taguchi method is used to construct experimental schemes with different laser shock process parameters. After surface pretreatment of the weld and heat-affected zone of the welded joint, an absorption layer and a constraint layer are laid, and then laser shock strengthening treatment is carried out.

[0023] Furthermore, in the process of optimizing laser shock process parameters, in order to reduce the dimensionality of the process parameters to be optimized, two parameters, laser power density and laser coverage, are used to characterize the laser spot diameter, single-pulse laser energy, laser pulse width, overlap rate, and number of laser shocks; the laser coverage is calculated according to the derivation formula of the number of spots, overlap rate, number of laser shocks, and spot diameter.

[0024] Furthermore, when optimizing the laser shock process parameters for different types of welded connections, the initial microstructure characteristics, residual stress field, and selected process parameters of the welded connection are substituted into the established microstructure characteristic prediction model and fatigue damage evolution equation. The fatigue life is predicted by combining finite element simulation and the parameters are optimized to establish a laser shock process parameter library for welded connections.

[0025] Beneficial effects: This invention systematically establishes cross-scale correlations between laser shock process parameters, microstructure characteristics, residual stress, and fatigue performance. By incorporating microstructure characteristics into the fatigue life prediction system, it significantly improves the accuracy of fatigue life prediction compared to traditional methods that only consider residual stress, providing a scientific and reliable basis for process parameter optimization.

[0026] This invention establishes a microstructure feature prediction model and a fatigue damage evolution equation, and combines finite element simulation to realize digital prediction of fatigue life. This significantly reduces the time-consuming and costly actual fatigue tests, lowers the cost of process parameter optimization, and improves optimization efficiency.

[0027] This invention effectively reduces the dimensionality of the parameters to be optimized and simplifies the optimization process by fixing the laser impact area and path, and characterizing multiple process parameters as laser power density and laser coverage, making it more suitable for practical engineering applications.

[0028] The method of this invention can be extended to different types of steel structure welding connections. By establishing a process parameter library, it can provide standardized and regulated process guidance for laser shock strengthening treatment of welding connections in aerospace, energy and power, offshore wind power and other fields, improve the fatigue performance of welding connections, extend the service life of structures, and has good engineering application value and economic adaptability. Attached Figure Description

[0029] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of the main process of the laser shock welding process parameter optimization method according to an embodiment of the present invention; Figure 2 This refers to the point-by-point serpentine scanning laser impact path described in the embodiments of the present invention; Figure 3 This is a spatiotemporal distribution diagram of the laser shock load described in an embodiment of the present invention; in, Figure 3 Figure a shows the spatial distribution of the laser shock load, and Figure b shows the temporal distribution of the laser shock load. Figure 4 This is a schematic diagram illustrating the processing of the mating joint according to an embodiment of the present invention; Figure 5 This is a schematic diagram of X-ray diffraction measurement of residual stress in welded joints according to an embodiment of the present invention; Figure 6 The results of residual stress measurement in the welded connection described in the embodiments of the present invention; Figure 7 This is a schematic diagram showing the sampling location and shape of the fatigue specimen according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the laser shock strengthening process described in an embodiment of the present invention; Figure 9 This refers to the residual stress field of the fatigue specimen after laser shock as described in the embodiments of the present invention. Figure 10 This describes the microstructure of the fine-grained region of the heat-affected zone of the butt joint before and after laser shock, as described in this embodiment of the invention. in, Figure 10 Figure a shows the microstructure before laser shock. Figure 10 Figure b shows the microstructure after laser shock. Detailed Implementation

[0030] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0031] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] Definitions: Microstructure characteristics refer to the internal structure and morphology of materials at the microscale. Different types of microstructures have a significant impact on the strength, plasticity, toughness and fatigue performance of materials.

[0033] The following are some common types of steel microstructures and related explanations: Austenitic structure: Austenite is a solid solution structure in which a small amount of carbon is dissolved in γ-Fe, and its morphology is usually an equiaxed polygonal structure. Austenitic structure has good plasticity and toughness, but relatively low strength.

[0034] Ferrite microstructure: Ferrite is an interstitial solid solution of carbon dissolved in α-Fe, and its morphology is usually lamellar, massive, acicular, and network structure. Ferrite microstructure has good plasticity and toughness, but relatively low strength and hardness.

[0035] Martensite: Martensite is a supersaturated solid solution structure formed from austenite during rapid cooling. Its morphology is typically acicular or lath-like. Martensite has high strength and hardness, but relatively low plasticity and toughness.

[0036] Bainitic microstructure: Bainite is a mixed microstructure of ferrite and carbides formed during the moderate cooling process of austenite. Its morphology is typically feather-like or acicular. Bainitic microstructure possesses high strength and good toughness, resulting in excellent overall mechanical properties.

[0037] Pearlite microstructure: Pearlite is a dual-phase microstructure composed of alternating ferrite and cementite, formed by the transformation of austenite during slow cooling. Its morphology is typically lamellar. Pearlite microstructure possesses good strength and wear resistance while maintaining a certain degree of plasticity and toughness.

[0038] Weighted average grain size: The weighted average grain size refers to the average size of grains in the microstructure of a material, and is an important indicator for describing the coarseness of the grains. The finer the grains, the higher the strength and the better the overall mechanical properties of the material.

[0039] Example 1 See Figure 1-10 A method for optimizing laser shock blasting process parameters for welded connections in steel structures, comprising the following steps: S1. Obtain the initial residual stress field and microstructure characteristics of the welded joint, wherein the microstructure characteristics include the volume fraction and weighted average grain size of martensite, bainite, ferrite, pearlite, and austenite. S2. Strengthen the welded joint under different laser shock process parameters to obtain the microstructure characteristics after laser shock. Step S2 includes the following specific steps: S21. Select the laser spot diameter, single-pulse laser energy, laser pulse width and overlap rate as control factors, and set 5 levels for each factor; S22. Based on the selected control factors and the number of levels, the Taguchi method was used to construct the experimental scheme, and the L25 orthogonal array was selected for the experimental combination design, as shown in Table 1. Table 1

[0040] S23. Perform surface pretreatment on the weld seam area and heat-affected zone of the welded joint to remove oil, oxide layer and welding residue, and lay an absorption layer and a constraint layer on the weld seam area and heat-affected zone of the welded joint. S24. According to the test combinations in Table 1, use a laser shock peening device to perform point-by-point serpentine scanning laser shock on the weld area and heat-affected zone of the welded joint, such as... Figure 2 As shown; S25. Material samples are cut from the laser-shocked area and metallographic samples are prepared. The microstructure characteristics of the material are obtained by electron backscatter diffraction test, including the volume fraction of martensite, bainite, ferrite, pearlite and austenite and the weighted average grain size.

[0041] S3. Using laser shock process parameters as independent variables and microstructure characteristics after laser shock as dependent variables, establish a microstructure characteristic prediction model. Step S3 includes the following specific steps: S31. Organize the microstructure characteristics obtained under different combinations of process parameters in step S2, take the laser shock process parameters as independent variables, and take the volume fraction of martensite, bainite, ferrite, pearlite and austenite and the weighted average grain size as dependent variables to construct a data sample set between process parameters and microstructure characteristics. S32. Preprocess the sample data, including outlier detection and standardization. S33. A prediction model between process parameters and microstructure characteristics is established using multiple linear regression analysis to obtain the regression relationship between each process parameter and microstructure characteristics. S34. Conduct significance tests and goodness-of-fit analyses on the established regression model, and evaluate the reliability and prediction accuracy of the model through analysis of variance and coefficient of determination. When the prediction accuracy of the model does not meet the preset requirements, further adopt the multiple quadratic regression analysis method to re-establish the prediction model between process parameters and microstructure characteristics. S4. A finite element model is established by combining the measured dimensions of the welded connection with the initial residual stress field. The spatiotemporal distribution of the laser shock load and the material elastoplastic constitutive model are defined, and the residual stress field of the welded connection after laser shock is obtained through simulation. Step S4 includes the following specific steps: S41. Establish a finite element model based on the measured external dimensions of the welded connection; S42. Introduce the initial residual stress field obtained in step S1 into the finite element model of the welded connection; S43. Define the spatiotemporal distribution of laser shock load and the material elastoplastic constitutive model; S44. Conduct finite element simulation to obtain the residual stress field after laser shock strengthening.

[0042] S5. Combining fatigue tests and finite element simulations, establish a fatigue damage evolution equation that considers the influence of microstructure characteristics; Step S5 includes the following specific steps: S51. Conduct fatigue tests on welded connections treated with different laser shock process parameters, and record the cracking location and corresponding fatigue life of the welded connections. S52. Establish a finite element model based on the measured external dimensions of the welded connection; S53. Introduce the residual stress field after laser shock into the established finite element model, apply fatigue load, and obtain the cyclic stress amplitude at the crack location of the welded connection through finite element simulation. S54. Establish fatigue damage evolution equations based on fatigue life and cyclic stress amplitude at the crack location of welded joints.

[0043] S6. Introduce the microstructure characteristics and residual stress field after laser shock into the finite element model for fatigue life prediction. Determine the combination of laser shock process parameters that maximizes the fatigue life of the welded connection through parameter analysis, and complete parameter optimization.

[0044] This embodiment effectively solves the problem that existing technologies do not fully consider the impact of laser-induced microstructure evolution on fatigue performance, making it difficult to effectively control the laser-shock strengthening effect of welded steel structure connections in engineering applications and advanced analysis. By establishing the correlation between process parameters, microstructure characteristics, residual stress, and fatigue performance, this embodiment can achieve precise control of the laser-shock strengthening effect of welded steel structure connections, improving the fatigue performance of welded connections and the controllability of the laser-shock strengthening effect. This embodiment has the advantages of high sensitivity, accuracy, ease of operation, and effectiveness.

[0045] In the specific implementation, two sets of gas shielded welding processes were used to weld Q690E high-strength steel and BS690E high-strength steel to prepare welded butt joint test pieces. Specifically, for Q690E high-strength steel, welding parameters were used in two sets: a first set of welding current of 230A, a welding voltage of 25.4V, and a welding speed of 6.3mm / s; and a second set of welding parameters was used: a second set of welding current of 254A, a welding voltage of 29.2V, and a welding speed of 6.3mm / s. For BS690E high-strength steel, welding parameters were used in two sets: a first set of welding current of 228A, a welding voltage of 25.4V, and a welding speed of 6.3mm / s; and a second set of welding parameters was used: a second set of welding current of 264A, a welding voltage of 29.2V, and a welding speed of 6.3mm / s. The following description, based on the obtained experimental data, further illustrates the invention.

[0046] The processing procedure for the butt joint test specimen is as follows: Figure 4 As shown. X-ray diffraction was used to test the residual stress of the butt joint specimen to obtain the initial residual stress perpendicular to the weld direction, as shown. Figure 5 and Figure 6 As shown in the diagram. Subsequently, the butt joint test specimen was processed into fatigue test specimens using a wire EDM machine. A schematic diagram of the sampling locations and shapes of the fatigue test specimens is shown in the diagram. Figure 7 As shown. Based on this, an overlap rate of 50% and a laser power density of 4.95 GW / cm² were used. 2 The point-by-point serpentine scanning laser shock blasting process is used to strengthen the welds and heat-affected zones of fatigue specimens using laser shock blasting. Figure 8 As shown in the figure. X-ray diffraction was used to measure the residual stress after laser shock, and finite element simulation was used to predict the residual stress field of the fatigue specimen after laser shock. The results are as follows. Figure 9 As shown in Table 2. Subsequently, fatigue tests were conducted on fatigue specimens without and after laser shock treatment, and the results are shown in Table 2. Furthermore, material samples were taken from the coarse-grained and fine-grained regions of the heat-affected zone of the butt joint and metallographic samples were prepared. The microstructure morphology of the butt joint was observed using a scanning electron microscope, as shown in Table 2. Figure 10 As shown in Table 3, the volume fractions and weighted average grain sizes of martensite, bainite, ferrite, pearlite, and austenite were quantitatively obtained by electron backscatter diffraction experiments.

[0047] Table 2

[0048] Table 3

[0049] It can be seen that the stress cycle number of the fatigue test specimen of the butt joint before laser shock was 53528 and 50746, which increased to 145953 and 122452 after laser shock, and the fatigue life was significantly improved, which verifies the effectiveness of the optimized process parameters of the method of the present invention.

[0050] In a specific example, in step S1, the initial residual stress field of the welded joint is obtained by X-ray diffraction or neutron diffraction, and the microstructure characteristics are obtained by electron backscatter diffraction; the weighted average grain size is calculated according to the formula... Calculate, where d AVE f is the weighted average grain size for various microstructures; M f B f F f P and f A d represents the volume fraction of martensite, bainite, ferrite, pearlite, and austenite, respectively; M d B d F d P and d A These represent the average grain sizes of martensite, bainite, ferrite, pearlite, and austenite structures, respectively.

[0051] This embodiment ensures the accuracy and repeatability of the initial residual stress field and microstructure characteristics detection results through standardized experimental methods (X-ray / neutron diffraction, electron backscatter diffraction); it clarifies the calculation method of weighted average grain size through quantitative formulas, realizes the quantitative characterization of microstructure characteristics, provides unified and accurate basic data for subsequent modeling and simulation, avoids subsequent analysis errors caused by inconsistent detection / calculation methods, and improves the accuracy of the entire optimization method.

[0052] In a specific example, in step S2, the laser shock process parameters include laser spot diameter, single-pulse laser energy, laser pulse width, overlap rate, number of laser shocks, laser shock path, and laser shock area; in order to reduce the dimensionality of the process parameters to be optimized, the laser shock area is fixed as the weld seam area and heat-affected zone of the welded connection, and the laser shock path is fixed as a point-by-point serpentine scanning laser shock.

[0053] This embodiment fully defines all types of laser shock process parameters, avoiding incomplete optimization due to missing process parameters; by fixing the impact area (weld + heat-affected zone) and the impact path (point-by-point serpentine scanning), it reduces meaningless parameter optimization dimensions, greatly improves the efficiency of subsequent parameter analysis, and makes the laser shock operation more in line with engineering practice (focusing on weak areas of the welded connection), ensuring the targeted and stable strengthening effect.

[0054] In a specific example, in step S3, the microstructure feature prediction model is established through regression analysis or machine learning methods. Before modeling, outlier detection and standardization preprocessing are performed on the sample data of process parameters and microstructure features.

[0055] This embodiment effectively improves the fitting accuracy and prediction accuracy of the microstructure feature prediction model through standardized modeling methods and data preprocessing, achieving the goal of "inputting any laser shock process parameters and quickly outputting microstructure features", replacing a large number of repetitive microstructure detection experiments; the high reliability of the model makes the basic data for subsequent fatigue life prediction more accurate, further ensuring the accuracy of the entire optimization method, while improving the efficiency of process parameter optimization.

[0056] In a specific example, in step S4, the spatiotemporal distribution of the laser shock load is calculated according to the following derived formula:

[0057]

[0058]

[0059]

[0060] Where P(r,t) is the laser shock load; P(t) is the shock load at the center of the incident laser; r is the distance from the center of the incident laser; t is the impact time; ρ is the laser spot diameter; P max τ is the peak impact load at the center of the incident laser; τ is the laser pulse width; Δτ is the time for the impact load to decay from the peak to zero; K is the ratio of plasma thermal energy to internal energy; Z1 and Z2 are the acoustic impedances of the steel and confinement layer materials, respectively; κ is the plasma adiabatic index; I0 is the laser power density; E s This refers to the energy of a single-pulse laser. The elastic-plastic constitutive model of the material is adopted using the Johnson-Cook model and calculated according to the following formula:

[0061] Where, σ 0 For flow stress; σ y Quasi-static yield strength; , and These are the equivalent plastic strain, equivalent plastic strain rate, and reference strain rate, respectively; K, m, and Y are material constants.

[0062] This embodiment significantly improves the accuracy of finite element simulation by using precise load formulas and constitutive models that closely match the mechanical properties of steel. This ensures that the residual stress field obtained from the simulation closely matches the actual residual stress field after laser impact. It avoids deviations in the residual stress field caused by unscientific definitions of simulation parameters, providing accurate stress data support for subsequent fatigue life prediction. At the same time, it makes the finite element simulation method more replicable and easier for practical engineering applications.

[0063] In a specific example, the specific process of establishing the fatigue damage evolution equation in step S5 is as follows: fatigue tests are carried out on the welded joints after different laser shock process parameters, and the cracking location and corresponding fatigue life are recorded; a finite element model is established based on the measured external dimensions of the welded joint, the residual stress field after laser shock is introduced and fatigue load is applied, and the cyclic stress amplitude at the cracking location is obtained through finite element simulation; based on the fatigue life and cyclic stress amplitude at the cracking location of the welded joint, a fatigue damage evolution equation with microstructure characteristics as independent variables is established.

[0064] This embodiment utilizes dual data support—fatigue testing and finite element simulation—to make the establishment of the fatigue damage evolution equation more scientific and accurate. By incorporating microstructure characteristics into the influencing factors of the equation, it breaks through the limitation of traditional fatigue damage equations that only consider stress, achieving multi-factor quantitative characterization of fatigue damage. This allows subsequent fatigue life prediction to truly reflect the combined influence of microstructure and residual stress, significantly improving the accuracy of fatigue life prediction.

[0065] In a specific instance, the fatigue damage evolution equation is calculated according to the following formula:

[0066]

[0067] Where β, η, and M are the models to be established; f(R) is the stress ratio correction coefficient; D is the damage variable, and fracture failure occurs when D=1; N is the number of cycles; S ij,max and S ij,min This represents the maximum and minimum values ​​of the deviatoric stress tensor within a loading cycle.

[0068] It should be noted that the models to be established, β, η, and M, use micro-organism characteristics as independent variables:

[0069]

[0070]

[0071] Among them, F1(f M ,f B ,f F ,fP ,f A ,d AVE ), F2(f M ,f B ,f F ,f P ,f A ,d AVE ) and F3(f M ,f B ,f F ,f P ,f A ,d AVE The model is a multivariate multinomial regression model, calibrated using fatigue test data.

[0072] This embodiment achieves complete quantification of the fatigue damage evolution equation through specific quantitative formulas and parameter modeling requirements, making the fatigue life prediction calculation process more standardized and reproducible. The key parameters use microstructure characteristics as independent variables, allowing the equation to accurately reflect the impact of microstructure changes on fatigue damage. Combined with residual stress field data, it achieves high-precision prediction of fatigue life, providing accurate judgment basis for process parameter optimization.

[0073] In a specific example, in step S2, the Taguchi method is used to construct experimental schemes with different laser shock process parameters. After surface pretreatment of the weld and heat-affected zone of the welded joint, an absorption layer and a constraint layer are laid, and then laser shock strengthening treatment is carried out.

[0074] This embodiment constructs an experimental scheme using the Taguchi method, which can obtain the most comprehensive process parameters and microstructure characteristics correlation data with the fewest number of experiments, significantly reducing experimental costs and time. Through standardized surface pretreatment and absorption / constraint layer laying, the stability and consistency of laser shock peening effect are ensured, avoiding deviations in microstructure characteristics data caused by non-standard operation, providing high-quality sample data for subsequent modeling, and improving the reliability of the model.

[0075] In a specific example, during the optimization of laser shock process parameters, in order to reduce the dimensionality of the process parameters to be optimized, two parameters, laser power density and laser coverage, are used to characterize the laser spot diameter, single-pulse laser energy, laser pulse width, overlap rate, and number of laser shocks; the laser coverage is calculated according to the derivation formula of the number of spots, overlap rate, number of laser shocks, and spot diameter.

[0076] It should be noted that the laser coverage rate is:

[0077] Where v is the laser coverage; m is the number of laser impacts; n x n is the number of light spots in the x-direction;y γ is the number of light spots in the y-direction; x γ represents the overlap ratio in the x-direction; y The overlap ratio is in the y-direction.

[0078] This embodiment integrates multiple process parameters (spot diameter, single pulse energy, etc.) into two core parameters, significantly reducing the dimensionality of parameter optimization and greatly reducing the computational load of subsequent parameter analysis, thereby significantly improving the efficiency of process parameter optimization. The quantitative characterization of the two parameters makes the optimization of process parameters more targeted, facilitating rapid adjustment and selection of the optimal parameter combination in engineering practice. At the same time, the quantitative calculation of laser coverage makes the reduced parameters more scientific and avoids information loss due to parameter integration.

[0079] In a specific example, when optimizing the laser shock process parameters for different types of welded joints, the initial microstructure characteristics, residual stress field, and selected process parameters of the welded joint are substituted into the established microstructure characteristic prediction model and fatigue damage evolution equation. The fatigue life is predicted by combining finite element simulation and the parameter optimization is completed, thus establishing a laser shock process parameter library for welded joints.

[0080] The establishment of the laser shock blasting process parameter library for welding connections includes the following specific steps: A1. Determine the initial microstructure characteristics and residual stress field of each type of welded joint; A2. Select the laser shock process parameters to be optimized, input the selected process parameters into the microstructure feature prediction model after laser shock, and predict the microstructure features of the welded joint after laser shock. A3. Establish a finite element model based on the measured external dimensions of the welded connection, introduce the initial residual stress field obtained in step S1 into the finite element model, apply laser shock load, and obtain the residual stress field after laser shock strengthening through finite element simulation. A4. The microstructure characteristics predicted in step S2 and the residual stress field obtained in step S3 are introduced as field variables into the finite element model of the welded connection, fatigue load is applied, and fatigue life of the welded connection is predicted by combining the fatigue damage evolution equation. A5. Conduct finite element parameter analysis, change process parameters, and optimize laser shock strengthening process parameters based on lifetime prediction results.

[0081] The method in this embodiment can optimize the laser shock process parameters for different types of welded joints by establishing a predictive model of the microstructure characteristics after laser shock and a fatigue damage evolution equation for a specific type of welded joint. This method allows for the creation of a laser shock process parameter library for welded joints, enabling laser shock treatment of different types of welded joints in engineering applications.

[0082] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing laser shock blasting process parameters for welded connections of steel structures, characterized in that, Includes the following steps: S1. Obtain the initial residual stress field and microstructure characteristics of the welded joint, wherein the microstructure characteristics include the volume fraction and weighted average grain size of martensite, bainite, ferrite, pearlite, and austenite. S2. Strengthen the welded joint under different laser shock process parameters to obtain the microstructure characteristics after laser shock. S3. Using laser shock process parameters as independent variables and microstructure characteristics after laser shock as dependent variables, establish a microstructure characteristic prediction model. S4. A finite element model is established by combining the measured dimensions of the welded connection with the initial residual stress field. The spatiotemporal distribution of the laser shock load and the material elastoplastic constitutive model are defined, and the residual stress field of the welded connection after laser shock is obtained through simulation. In step S4, the spatiotemporal distribution of the laser shock load is calculated according to the following derived formula: Where P(r,t) is the laser shock load; P(t) is the shock load at the center of the incident laser; r is the distance from the center of the incident laser; t is the impact time; ρ is the laser spot diameter; P max τ is the peak impact load at the center of the incident laser; τ is the laser pulse width; Δτ is the time for the impact load to decay from the peak to zero; K is the ratio of plasma thermal energy to internal energy; Z1 and Z2 are the acoustic impedances of the steel and confinement layer materials, respectively; κ is the plasma adiabatic index; I0 is the laser power density; E s This refers to the energy of a single-pulse laser. The elastic-plastic constitutive model of the material is adopted using the Johnson-Cook model and calculated according to the following formula: Where, σ 0 For flow stress; σ y Quasi-static yield strength; , and These represent the equivalent plastic strain, equivalent plastic strain rate, and reference strain rate, respectively; K, m, and Y are material constants. S5. Combining fatigue tests and finite element simulations, establish a fatigue damage evolution equation that considers the influence of microstructure characteristics; The fatigue damage evolution equation is calculated according to the following formula: Where β, η, and M are the models to be established; f(R) is the stress ratio correction coefficient; D is the damage variable, and fracture failure occurs when D=1; N is the number of cycles; S ij,max and S ij,min Represents the maximum and minimum values ​​of the deviatoric stress tensor within a loading cycle; S6. Introduce the microstructure characteristics and residual stress field after laser shock into the finite element model for fatigue life prediction. Determine the combination of laser shock process parameters that maximizes the fatigue life of the welded connection through parameter analysis, and complete parameter optimization.

2. The method according to claim 1, characterized in that, In step S1, the initial residual stress field of the welded joint is obtained by X-ray diffraction or neutron diffraction, and the microstructure characteristics are obtained by electron backscatter diffraction; the weighted average grain size is calculated according to the formula. Calculate, where d AVE f is the weighted average grain size for various microstructures; M f B f F f P and f A d represents the volume fraction of martensite, bainite, ferrite, pearlite, and austenite, respectively; M d B d F d P and d A These represent the average grain sizes of martensite, bainite, ferrite, pearlite, and austenite structures, respectively.

3. The method according to claim 1, characterized in that, In step S2, the laser shock process parameters include laser spot diameter, single-pulse laser energy, laser pulse width, overlap rate, number of laser shocks, laser shock path, and laser shock area; the laser shock area is fixed as the weld seam area and heat-affected zone of the welded connection, and the laser shock path is fixed as a point-by-point serpentine scanning laser shock.

4. The method according to claim 1, characterized in that, In step S3, the microstructure feature prediction model is established through regression analysis or machine learning methods. Before modeling, outlier detection and standardization preprocessing are performed on the sample data of process parameters and microstructure features.

5. The method according to claim 1, characterized in that, In step S5, the specific process of establishing the fatigue damage evolution equation is as follows: fatigue tests are carried out on the welded joints after different laser shock process parameters, and the cracking location and corresponding fatigue life are recorded; a finite element model is established based on the measured welded joint dimensions, the residual stress field after laser shock is introduced and fatigue load is applied, and the cyclic stress amplitude at the cracking location is obtained through finite element simulation. Based on the fatigue life and cyclic stress amplitude at the crack location of the welded connection, a fatigue damage evolution equation with microstructure characteristics as the independent variable is established.

6. The method according to claim 1, characterized in that, In step S2, the Taguchi method is used to construct experimental schemes with different laser shock process parameters. After surface pretreatment of the weld and heat-affected zone of the welded joint, an absorption layer and a constraint layer are laid, and then laser shock strengthening treatment is carried out.

7. The method according to claim 1, characterized in that, In the process of optimizing laser shock process parameters, in order to reduce the dimensionality of the process parameters to be optimized, two parameters, laser power density and laser coverage, are used to characterize the laser spot diameter, single-pulse laser energy, laser pulse width, overlap rate, and number of laser shocks; the laser coverage is calculated according to the derivation formula of the number of spots, overlap rate, number of laser shocks, and spot diameter.

8. The method according to any one of claims 1-7, characterized in that, When optimizing laser shock process parameters for different types of welded joints, the initial microstructure characteristics, residual stress field, and selected process parameters of the welded joint are substituted into the established microstructure characteristic prediction model and fatigue damage evolution equation. The fatigue life is predicted by combining finite element simulation and the parameters are optimized to establish a laser shock process parameter library for welded joints.

Images