A laser additive stress and tissue synergistic regulation method and system

Abstract

This application relates to a method and system for synergistic control of stress and microstructure in laser additive manufacturing. The method includes the following steps: constructing a fluid simulation model of the laser additive remanufacturing process for a target workpiece; based on the fluid simulation model, obtaining the stress distribution simulation calculation results and the predicted microstructure of the remanufacturing area of ​​the target workpiece; based on the stress distribution simulation calculation results and the predicted microstructure, generating process parameter optimization instructions with the goal of reducing residual stress and stabilizing the molten pool; and performing laser additive remanufacturing on the target workpiece according to the process parameter optimization instructions. The system is used to execute the above method. This application achieves process optimization based on multiphysics simulation and, combined with heat treatment, can obtain components with excellent performance, providing advanced process solutions for welding repair and remanufacturing of key equipment in aero-engine blades, gas turbine hot-end components, ship propulsion systems, and nuclear power plants.

Description

Technical Field

[0001] This application relates to the technical field of laser additive remanufacturing, specifically to a method and system for synergistic control of stress and microstructure in laser additive manufacturing. Background Technology

[0002] As high-end equipment develops towards higher performance and longer lifespan, traditional laser additive remanufacturing technology faces severe challenges in meeting the repair needs of critical components. In high-end application fields such as aerospace and energy, the service performance of repaired components not only needs to meet static mechanical indicators, but also needs to withstand dynamic loads and environmental erosion under complex working conditions.

[0003] The main limitation of existing technologies lies in the inability to achieve synergistic optimization of stress distribution and microstructure: the temperature gradient generated by rapid thermal cycling leads to significant thermal stress accumulation, which not only causes macroscopic deformation and dimensional deviations of components, but also induces microcrack initiation and propagation due to stress relaxation during subsequent service; at the same time, the non-equilibrium structure formed by rapid solidification of the molten pool is often accompanied by severe element segregation and coarse precipitates. The synergistic effect of this structural defect and residual stress will further degrade material properties, becoming a key bottleneck restricting the quality of repair.

[0004] In-depth analysis reveals that the interaction between stress and microstructure exhibits complex multi-scale characteristics. At the macroscopic level, the coupling effect between residual stress field and corrosive medium significantly accelerates the stress corrosion cracking process, posing a serious threat to components serving in harsh environments such as nuclear power plants and ships. At the microscopic level, stress concentration areas induce local lattice distortion, altering the nuclear energy barrier of precipitated phases and leading to uneven distribution of strengthening phases.

[0005] More challengingly, stress control and microstructure optimization in traditional technologies often conflict with each other: heat treatment to reduce residual stress may cause grain coarsening, while processes that refine the microstructure may exacerbate stress concentration. This "one-sided" phenomenon essentially stems from insufficient understanding of the multi-physics coupling mechanism. Existing numerical models are mostly limited to single-physics field analysis, making it difficult to accurately predict the complex interaction between melt flow, stress evolution, and microstructure formation, resulting in a lack of scientific basis for process design. Summary of the Invention

[0006] To address the problems existing in the prior art, this application aims to provide a method and system for the synergistic control of stress and microstructure in laser additive manufacturing. This application utilizes multiphysics simulation to optimize the process and, combined with heat treatment, can obtain components with superior performance, providing advanced process solutions for the welding repair and remanufacturing of aero-engine blades, hot-end components of gas turbines, ship propulsion systems, and key equipment in nuclear power plants.

[0007] The laser additive manufacturing method for synergistic control of stress and tissue as described in this application includes the following steps:

[0008] S01. A fluid simulation model of the laser additive remanufacturing process of the target workpiece is constructed using the VOF method;

[0009] S02. Based on the fluid simulation model, obtain the stress distribution simulation calculation results of the target workpiece and the predicted microstructure of the remanufactured area;

[0010] S03. Based on the stress distribution simulation calculation results and predicted microstructure, with the goal of reducing residual stress and stabilizing the molten pool, generate process parameter optimization instructions.

[0011] S04. According to the process parameter optimization instructions, perform laser additive remanufacturing on the target workpiece.

[0012] Preferably, step S01 includes:

[0013] S101. Conduct laser additive remanufacturing experiments and / or simulation experiments on the target workpiece to obtain experimental data, including the process parameters of laser additive remanufacturing and the workpiece parameters of the target workpiece.

[0014] S102. Based on the experimental data, the fluid simulation model is constructed using the VOF method;

[0015] S103. Calculate the component differences in the properties of the substrate:

[0016] ,

[0017] in, This represents the sum of various thermophysical parameters of a material. This represents a specific thermophysical property parameter of a single component. This represents the mass fraction of a single component in a material.

[0018] Preferably, in step S101, after performing the laser additive remanufacturing experiment on the target workpiece, the method further includes:

[0019] The target workpiece is cut and sampled to obtain a specimen, and the specimen is subjected to microstructure characterization analysis to obtain the characterization experimental results;

[0020] In step S103, when calculating the thermophysical parameters of each component, the calculated thermophysical parameters are compared with the characterization experiment results, and the thermophysical parameters of the material are adjusted in real time according to the characterization experiment results.

[0021] Preferably, in step S02, obtaining the stress distribution simulation calculation results of the target workpiece based on the fluid simulation model includes the following steps:

[0022] S02a1. Construct a stress solution model based on the fluid simulation model;

[0023] S02a2. Input the thermophysical parameters into the stress solution model, and calculate the transient temperature field of the processing process through the stress solution model;

[0024] S02a3. Analyze the influence of the transient temperature field during the processing on the thermal deformation and thermal stress of the material, and obtain the simulation calculation results of the stress distribution;

[0025] Wherein, the input energy of the stress solution model is equal to the energy of the fluid simulation model, satisfying:

[0026] ;

[0027] in, This represents the input energy of the stress solution model. Indicates laser power. Indicates laser absorption efficiency. Indicates the effective laser radius. Indicates the scanning speed. Indicates processing time;

[0028] Heat loss is calculated using the convection-diffusion equation:

[0029] ,

[0030] in, Indicates heat loss, Indicates the convective heat transfer coefficient. Represents Boltzmann's constant. Indicates radiative emissivity. This indicates the ambient temperature during processing.

[0031] Preferably, in step S02, obtaining the predicted microstructure of the remanufactured region based on the fluid simulation model includes the following steps:

[0032] S02b1. Based on the fluid simulation model, obtain the simulated molten pool flow field, the simulated processing temperature field, and the simulated solute field;

[0033] S02b2. Obtain thermal history data based on the simulated molten pool flow field and the simulated processing temperature field;

[0034] S02b3, Calculate the temperature gradient of the entire region based on the aforementioned thermal history data. and solidification rate ;

[0035] S02b4. The critical conditions for columnar / equiaxed crystal transformation are calculated using the KGT model to predict the grain morphology and type distribution.

[0036] The temperature gradient in the KGT model and solidification rate Calculate using the following formula:

[0037] ,

[0038] ,

[0039] ,

[0040] in, Indicates the difference in temperature change. Indicates unit distance, Indicates the position of the normal direction. Indicates a unit of time. This indicates the critical cooling rate.

[0041] Preferably, in step S02, obtaining the predicted microstructure of the remanufactured region based on the fluid simulation model further includes the following steps:

[0042] S02b5. Based on the simulated solute field, real-time concentration distribution data of solute elements at different locations in the molten pool are obtained using the Scheil-Gulliver non-equilibrium solidification model.

[0043] The Scheil-Gulliver nonequilibrium solidification model is expressed as follows:

[0044] ,

[0045] in, Indicates component density, Indicates the liquid volume fraction. Indicates components Mass fraction in the liquid phase Indicates components Mass fraction in the solid phase Indicates components Molecular diffusion coefficient in the liquid phase, Indicates components The interphase mass transfer coefficient;

[0046] S02b6. Predict the distribution density of the secondary phase based on the real-time concentration distribution data of the solute elements:

[0047] ,

[0048] ,

[0049] in, Represents element The content of eutectic components, Represents element The calculated content value, Represents element The allocation coefficient, Indicates the solid fraction. Indicates matrix Solid solution elements The solid solution content, Eutectic The calculated content value.

[0050] Preferably, step S03 includes:

[0051] S301. Set constraints on process parameters, including heat input threshold, melt pool range, and forming quality requirements. Use a multi-objective optimization method to construct a mapping relationship between process parameters, stress distribution, and microstructure characteristics:

[0052] ,

[0053] ,in, Indicates the molten pool size conditions. Indicates the fill condition. Indicates stress condition, Indicates the range of melting depth. Indicates the defined range of melt width. Indicates laser power. Indicates the rotation speed of the powder feeding turntable. Indicates the scan rate. Indicates the selection of scanning strategy;

[0054] S302. Preset the range of process parameters to be used, including the range of laser power, the range of powder feeding turntable speed, the range of scanning rate and scanning strategy;

[0055] S303. Within the range of the stated process parameters, select multiple combinations of process parameters for simulation processing;

[0056] S304. Calculate the residual thermal stress of the target workpiece after simulation processing for each combination of process parameters.

[0057] S305. Select the combination of process parameters that results in complete fusion at the forming joint and minimizes the residual thermal stress as the optimal combination of process parameters.

[0058] S306. Based on the optimal combination of process parameters, generate the process parameter optimization instruction.

[0059] Preferably, the laser additive stress and tissue synergistic control method further includes:

[0060] S05. Perform a heat treatment process on the target workpiece, the heat treatment process including:

[0061] The target workpiece is subjected to solution treatment within a first temperature range.

[0062] The target workpiece is cooled;

[0063] The target workpiece is subjected to aging treatment within a second temperature range;

[0064] The first temperature range is larger than the second temperature range.

[0065] This application discloses a laser additive manufacturing stress and tissue synergistic control system, comprising:

[0066] The simulation model building module is used to build a fluid simulation model of the laser additive remanufacturing process of the target workpiece;

[0067] The stress field calculation module is used to obtain the stress distribution simulation calculation results of the target workpiece based on the fluid simulation model.

[0068] The tissue morphology prediction module is used to predict the tissue morphology of the remanufactured area based on the fluid simulation model.

[0069] The process parameter optimization module is used to generate process parameter optimization instructions based on the stress distribution simulation calculation results and predicted microstructure, with the goal of reducing residual stress and stabilizing the molten pool.

[0070] A laser additive remanufacturing module is used to perform laser additive remanufacturing on the target workpiece according to the process parameter optimization instructions.

[0071] The SLM-AlSi10Mg plate welded component of this application is prepared by the laser additive stress and microstructure synergistic control method described above.

[0072] The laser additive manufacturing stress and tissue synergistic control method and system described in this application have the following advantages:

[0073] This application constructs a fluid simulation model to calculate the stress distribution of the target workpiece and predict the microstructure of the remanufactured area. Based on the simulation results and predicted microstructure, the process parameters are optimized with the core objectives of reducing residual stress, stabilizing the weld pool formation, and ensuring welding quality. This achieves coordinated control of multiple physical fields, effectively avoids the overlap between high-stress areas and weak microstructure areas, and achieves the unity of stress distribution optimization and microstructure performance enhancement, effectively improving the service reliability of the component.

[0074] This application constructs a fluid simulation model based on experimental data. Through high-precision fluid simulation modeling and dynamic calibration with experimental data, it can accurately predict and control the flow, solidification, and forming behavior of the molten pool under complex bevel joint morphology. This method effectively overcomes the problems of discrete calculations, low accuracy, and limited physical output of traditional models when dealing with irregular joints, significantly improving the forming quality and first-pass success rate of welding surfaces with complex geometric features.

[0075] This application adds an "offline heat treatment process" after the "online laser additive manufacturing process control." The online control ensures low defects and good initial performance during the manufacturing process; the subsequent precisely controlled solution-aging heat treatment further eliminates residual stress and promotes the uniform precipitation of nano-reinforcing phases. This multi-process hybrid strategy produces a significant performance synergy effect, enabling the component to achieve high strength while maintaining excellent toughness, resulting in a significant improvement in overall mechanical properties. Attached Figure Description

[0076] Figure 1 This is a flowchart illustrating the steps of a laser additive stress and tissue synergistic control method described in this application;

[0077] Figure 2 This is a structural block diagram of a laser additive stress and tissue synergistic control system described in this application;

[0078] Figure 3 This is a schematic diagram of a laser additive remanufacturing processing device;

[0079] Figure 4 This is a comparison chart of the fluid simulation results of this application;

[0080] Figure 5 This is a diagram showing the stress simulation calculation results of this application;

[0081] Figure 6 This is a diagram showing the predicted organizational morphology of this application;

[0082] Figure 7 Here are SEM images of AlSi10Mg powder delivery welding.

[0083] Figure 8 Here are SEM images of Al-Mg-Sc powder delivery welding.

[0084] Figure 9 It is a tensile stress-strain curve before and after heat treatment.

[0085] Figure labeling: 101-Simulation model construction module, 102-Stress field calculation module, 103-Microstructure prediction module, 104-Process parameter optimization module, 105-Laser additive remanufacturing module, 201-Argon environment, 202-Laser beam, 203-Powder, 204-Base, 205-Substrate, 206-Laser head, 207-Molten pool, 208-Protective gas. Detailed Implementation

[0086] like Figure 1 As shown, the laser additive manufacturing stress and tissue synergistic control method described in this application includes the following steps:

[0087] S01. Construct a fluid simulation model of the laser additive remanufacturing process for the target workpiece;

[0088] Step S01 includes:

[0089] S101. Conduct laser additive remanufacturing experiments and / or simulation experiments on the target workpiece to obtain experimental data, including the process parameters of laser additive remanufacturing and the workpiece parameters of the target workpiece.

[0090] For example, using AlSi10Mg-SLM sheet as the target workpiece, a V-groove joint welding experiment was performed on the target workpiece, and the processing equipment was as follows: Figure 3 As shown.

[0091] Before the experiment, the aluminum alloy sheet needs to be surface treated: first, use 600-grit metallographic sandpaper to polish and remove the oxide film, then wipe it clean with acetone and anhydrous ethanol and blow it dry. Roughly grind the bevel joint to remove the oxide layer and surface defects, ensuring a smooth and clean surface.

[0092] AlSi10Mg-SLM plates (60 mm × 35 mm × 3 mm, with a 45° bevel and a 0.5 mm blunt edge) and the corresponding filler powder were placed in a vacuum drying oven and dried at 200°C for 5 hours. After removal, they were stored in a constant temperature and humidity cabinet and used within 24 hours to prevent re-oxidation or moisture absorption. The experiment employed a gas-filled protection system using 99% pure argon as the protective gas. A low-oxygen environment was created through a sealed chamber and a heat-resistant film, and real-time monitoring with an oxygen content analyzer ensured that the oxygen concentration remained below 50 ppm.

[0093] The preset process parameters are laser power of 1500 W and 2500 W, and scanning speed of 6 mm / s and 8 mm / s. A sequential scanning strategy is adopted, and the processing height is two layers. Two sets of repeated experiments are conducted for each set of parameters to complete the aforementioned laser additive remanufacturing experimental process. In other optional embodiments, simulation experiments can be conducted based on actual experiments to expand the experimental data.

[0094] After completing the additive remanufacturing, a set of connectors was used to prepare metallographic samples.

[0095] The specific procedure is as follows: Samples are taken by cutting along the cross-sectional line of the connecting area, then heat-mounted and successively polished with sandpaper ranging from 240 to 4000 grit, followed by polishing with diamond polishing compound to a mirror finish. After cleaning and drying with anhydrous ethanol, the samples are etched using a self-prepared Keller's reagent; an exemplary ratio is 2 mL of HF solution. 5 mL of solution, 3 mL of HCl solution, and 190 mL of H2O were added. The sample was rinsed with water and ethanol and then dried. Another sample, cut to 20 mm × 15 mm × 10 mm, was resin-mounted, polished, and then used for metallographic analysis. Finally, the microstructure of the sample was characterized and compared with the simulation results for verification.

[0096] S102. Based on the experimental data, the fluid simulation model is constructed using the VOF method;

[0097] This invention is based on the Fluent module in Ansys software for secondary development, and the fluid simulation model is constructed based on the VOF method.

[0098] A proportional calculation model is constructed based on the actual welding sample (i.e., the target workpiece). This model includes an argon gas domain, an irregular joint domain, and a base plate domain. The irregular joint domain and the base plate domain are merged and initialized to achieve the effect of a unified plate.

[0099] The fluid simulation model needs to be constructed based on four major conservation equations: energy conservation equation, momentum conservation equation, mass conservation equation, multi-component conservation equation, and energy source term equation, mass source term equation, momentum source term equation, and component source term equation required for calculation.

[0100] In the argon region of the computational domain, the two end boundaries are defined as convective heat transfer boundary conditions, with the ambient temperature set at 300K and the convective heat transfer coefficient at 100 W / m²·K; the upper boundary adopts a pressure outlet condition to maintain the entire computational domain at standard atmospheric pressure.

[0101] The bottom and side boundaries of the substrate are considered as adiabatic walls with no energy or mass exchange, and their initial temperature is uniformly set to 300K. To ensure the stability of numerical calculations and avoid calculation anomalies caused by zero component concentration, the initial composition of the gas domain is set to 99.99% argon gas and 0.01% metal vapor. This setting helps to achieve stable solutions for heat and mass transfer under multiphysics coupling in the simulation of laser-directed energy deposition.

[0102] S103. To facilitate subsequent stress distribution simulation calculations, component differences in the substrate properties are calculated, and the calculated thermophysical parameters of each component are dynamically adjusted during multiple simulations to achieve the thermophysical parameters that best reflect the experimental results. The specific calculation of component differences in the substrate properties is as follows:

[0103] ,

[0104] in, It represents the sum of various thermophysical properties of a material, including, for metallic materials, density, specific heat, thermal conductivity, or viscosity, etc. This represents a specific thermophysical property parameter of a single component. This represents the mass fraction of a single component in a material.

[0105] After integrating the above information, the initial temperature, initial laser loading position, convergence parameters, and calculation parameters are set, and the calculation begins. During the calculation process, the results are compared with those from the characterization experiment, and the material's thermophysical parameters are adjusted in real time.

[0106] For example, based on literature materials, the initial solidus of the substrate is 850 K, the liquidus is 930 K, and the specific heat is 1000 K. Thermal conductivity is 120 Through multiple simulation and control experiments, under these experimental conditions, the solidus was 830 K, the liquidus was 870 K, and the specific heat was 1105 K. Thermal conductivity is 150 The simulation results correspond well with the experimental results.

[0107] S02. Based on the fluid simulation model, obtain the stress distribution simulation calculation results of the target workpiece and the predicted microstructure of the remanufactured area;

[0108] First, stress distribution simulation calculations are performed. The specific process is as follows:

[0109] The proportional calculation model of the fluid simulation model is synchronously imported into the stress field calculation module, and the material thermophysical parameters obtained from the aforementioned calculations are imported into the stress solution model. The stress solution model uses the coupled-field transient-thermal stress solution module in ANSYS. Next, the energy source input is defined using the APDL programming language. Subsequently, the stress solution model calculates the transient temperature field during processing based on thermo-elastic-plastic theory, thereby analyzing its influence on material thermal deformation and thermal stress, and obtaining the stress distribution simulation calculation results, such as... Figure 5 As shown.

[0110] Wherein, the input energy of the stress solution model is equal to the energy of the fluid simulation model, satisfying:

[0111] ;

[0112] in, This represents the input energy of the stress solution model. Indicates laser power. Indicates laser absorption efficiency. Indicates the effective laser radius. Indicates the scanning speed. The processing time is indicated; the starting point in the stress solution model is (X=0, Y=0, Z=0), and the upper surface of the central connecting layer of the substrate is used as the heat source loading working surface.

[0113] Heat loss includes both convective and radiative losses, and a convective-diffusion equation that varies synchronously with the calculation is used:

[0114] ,

[0115] in, Indicates heat loss, Indicates the convective heat transfer coefficient. Represents Boltzmann's constant. Indicates radiative emissivity. This indicates the ambient temperature during processing.

[0116] After obtaining the stress distribution simulation results from the aforementioned calculations, this step predicts the microstructure. Specifically, based on the molten pool flow field and thermal history data, the elemental distribution characteristics are analyzed to predict the microstructure of the remanufactured area.

[0117] By combining the simulation results of multiphysics fields of coupled molten pool flow field, temperature field, and solute field with the KGT model, the grain morphology, type distribution, and precipitated phase characteristics of different regions in the remanufacturing process can be quantitatively predicted.

[0118] Specifically, the following steps are included:

[0119] S02b1. Based on the fluid simulation model, obtain the simulated molten pool flow field, the simulated processing temperature field, and the simulated solute field;

[0120] S02b2. Obtain thermal history data based on the simulated molten pool flow field and the simulated processing temperature field;

[0121] S02b3, Calculate the temperature gradient of the entire region based on the aforementioned thermal history data. and solidification rate ;

[0122] S02b4. The critical conditions for columnar / equiaxed crystal transformation (CET) are calculated using the KGT model to predict the grain morphology and type distribution.

[0123] The temperature gradient in the KGT model and solidification rate Calculate using the following formula:

[0124] ,

[0125] ,

[0126] ,

[0127] in, Indicates the difference in temperature change. Indicates unit distance, Indicates the position of the normal direction. Indicates a unit of time. This indicates the critical cooling rate.

[0128] Exemplary prediction results are as follows Figure 6 As shown, based on the measured and analyzed cooling rates in different regions of the molten pool, the cooling rate in the central region of the molten pool reached 858℃ / s, significantly higher than... The critical upper limit (450℃ / s); while the cooling rate of the molten pool edge region is 171℃ / s, significantly lower than that of the critical upper limit (450℃ / s); while the cooling rate of the molten pool edge region is 171℃ / s, which is significantly lower than that of the critical upper limit (450℃ / s); The critical lower limit (350℃ / s).

[0129] Combining the above regional differences in cooling rate and Based on the criteria for judgment, a comprehensive calculation of the overall solidification process of the molten pool shows that the solidification of the aluminum alloy molten pool will exhibit regional crystal distribution characteristics: the central region tends to form equiaxed crystals due to the cooling rate being higher than the critical value, while the edge region tends to form columnar crystals due to the cooling rate being lower than the critical value. This allows for the prediction of the grain morphology distribution of the target workpiece.

[0130] The Scheil-Gulliver nonequilibrium solidification model was used to simulate the solute field and provide real-time concentration distribution data of solute elements (such as Si and Mg) at different locations in the molten pool, which reveals the transport and accumulation trends of elements on a macroscopic scale.

[0131] The Scheil-Gulliver nonequilibrium solidification model is expressed as follows:

[0132] ,

[0133] in, Indicates component density, Indicates the liquid volume fraction. Indicates components Mass fraction in the liquid phase Indicates components Mass fraction in the solid phase Indicates components Molecular diffusion coefficient in the liquid phase, Indicates components The interphase mass transfer coefficient;

[0134] S02b6. Quantitative calculation formula for secondary phase based on element content distribution to predict the distribution density of secondary phase.

[0135] The quantitative calculation formula for the secondary phase is expressed as follows:

[0136] , ,

[0137] in, Represents element The content of eutectic components, Represents element The calculated content value, Represents element The allocation coefficient, Indicates the solid fraction. Indicates matrix Solid solution elements The solid solution content, Eutectic Calculated content value;

[0138] For example, when AlSi10Mg is used as powder welding, then It can be represented as This is used to indicate the eutectic content of Si element. This represents the calculated content of element Si. It can be represented as , is used to represent the solid solution content of Si in the Al matrix. It can be represented as , is the calculated value of eutectic silicon content.

[0139] In this embodiment, two alloy powders, AlSi10Mg and Al-Mg-Sc, were used for comparative welding experiments. The results showed that the Si phase in the core region of the molten pool differed significantly between the two powders after welding. Simulated elemental distribution calculations showed that when using AlSi10Mg powder, the Si content in the central region of the molten pool was 10.5%, corresponding to a calculated eutectic silicon phase proportion of 9.1%. Actual SEM characterization confirmed this. Figure 7 As shown, the calculated proportion of eutectic silicon is 10.4%.

[0140] When using Al-Mg-Sc powder welding, the Si content in the central region of the molten pool is 6.5%, corresponding to a calculated eutectic silicon phase ratio of 5.6%. SEM characterization tests on this region, such as... Figure 8As shown, the actual measured proportion of eutectic silicon phase was 5.2%. By combining the elemental content distribution characteristics of different regions, the distribution density of secondary phases (such as eutectic silicon) and the morphology of crystals can be predicted, thus establishing a quantitative prediction method from process parameters to the final microstructure.

[0141] S03. Based on the stress distribution simulation calculation results and predicted microstructure, with the goal of reducing residual stress and stabilizing the molten pool, generate process parameter optimization instructions.

[0142] S301. Set constraints on process parameters, including heat input threshold, melt pool range, and forming quality requirements. Use a multi-objective optimization method to construct a mapping relationship between process parameters, stress distribution, and microstructure characteristics:

[0143] ,

[0144] ,in, Indicates the molten pool size conditions. Indicates the fill condition. Indicates stress condition, Indicates the range of melting depth. Indicates the defined range of melt width. Indicates laser power. Indicates the rotation speed of the powder feeding turntable. Indicates the scan rate. Indicates the selection of scanning strategy;

[0145] S302. Preset the range of process parameters to be used, including the range of laser power, the range of powder feeding turntable speed, the range of scanning rate and scanning strategy;

[0146] S303. Within the range of the stated process parameters, select multiple combinations of process parameters for simulation processing;

[0147] S304. Calculate the residual thermal stress of the target workpiece after simulation processing for each combination of process parameters.

[0148] S305. Select the combination of process parameters that results in complete fusion at the forming joint and minimizes the residual thermal stress as the optimal combination of process parameters.

[0149] S306. Based on the optimal combination of process parameters, generate the process parameter optimization instruction.

[0150] S04. According to the process parameter optimization instructions, perform laser additive remanufacturing on the target workpiece.

[0151] For example, during the process optimization, with the core objectives of reducing residual stress, stabilizing the molten pool formation, and ensuring welding quality, constraints are set and systematic parameter optimization is carried out: the influence of the scanning strategy on heat accumulation is analyzed through stress simulation, the optimal path scheme that can significantly reduce residual thermal stress in the joint is selected, and sequential scanning is determined as the optimal scanning method; at the same time, the test range of key process parameters is defined before the test, such as laser power of 1000-4000W, powder feeding turntable speed of 0.4-1.6r / min, and scanning rate of 4-10mm / s.

[0152] After multiple rounds of data fitting calculations and verifications, when using the combined process parameters of 2500W laser power, 8mm / s scanning rate, and 0.8r / min powder feeding rate, as follows: Figure 4 As shown, the size of the molten pool is stably controlled within the preset range, and the weld meets the macroscopic quality requirements of being defect-free, densely formed, and fully penetrated. Ultimately, the complete fusion of the V-groove and the effective metallurgical bonding between the weld and the base material are achieved.

[0153] This application uses a fluid model to simulate the complex flow field and multiphase flow interaction process of the molten pool. Through secondary data processing, the microstructure distribution and two-phase content distribution of the additive manufacturing zone are obtained. The stress solution abandons fixed empirical parameters and adopts thermophysical parameters fitted by the flow field, which improves accuracy. Simultaneously, by collaboratively optimizing the molten pool size, filling effect, and thermal stress distribution through multi-objective functions, the optimal process parameters are obtained.

[0154] Furthermore, the laser additive manufacturing stress and tissue synergistic control method of this embodiment also includes:

[0155] S05. Perform a heat treatment process on the target workpiece, the heat treatment process including:

[0156] The target workpiece is subjected to solution treatment within a first temperature range.

[0157] The target workpiece is cooled;

[0158] The target workpiece is subjected to aging treatment within a second temperature range;

[0159] The first temperature range is larger than the second temperature range.

[0160] For example, when the target workpiece is an aluminum alloy joint, a heat treatment process is applied to the laser-welded aluminum alloy joint to further improve the material properties.

[0161] The specific process parameters are as follows: solution treatment at 535~545℃ for 2 hours, followed by water quenching and then aging treatment at 170℃ for 10 hours. The heat treatment process obtains a supersaturated solid solution welded joint through quenching, and then, during low-temperature aging, dispersed nanoscale second Si phase precipitates in the matrix, thereby significantly improving the tensile strength of the alloy. Figure 9 As shown.

[0162] During the solution treatment stage, solute atoms achieve homogenization and diffusion, eutectic silicon particles undergo spheroidization transformation, and intermetallic compounds dissolve into the matrix. Upon entering the aging stage, high-density nanoscale strengthening phases precipitate in situ in the matrix, further enhancing the alloy's strength.

[0163] For tensile property testing, in accordance with GB / T 228.1-2021 standard, dog-bone tensile specimens were used for room temperature tensile testing on a WDW-20G microcomputer-controlled universal testing machine. The beam displacement rate was set as follows: 0.375 mm / min before yielding and 2 mm / min after yielding.

[0164] This demonstrates that, after optimization using a multi-process synergistic strategy, the mechanical properties of the bonded specimens were significantly improved, with their average yield strength and ultimate tensile strength increasing to 261 MPa and 303 MPa, respectively, surpassing the strength level of the SLM-AlSi10Mg substrate.

[0165] like Figure 2 As shown, this application also provides a laser additive manufacturing stress and tissue synergistic control system, comprising:

[0166] Simulation model building module 101 is used to build a fluid simulation model of the laser additive remanufacturing process of the target workpiece;

[0167] The stress field calculation module 102 is used to obtain the stress distribution simulation calculation results of the target workpiece based on the fluid simulation model.

[0168] The tissue morphology prediction module 103 is used to predict the tissue morphology of the remanufactured area based on the fluid simulation model.

[0169] The process parameter optimization module 104 is used to generate process parameter optimization instructions based on the stress distribution simulation calculation results and predicted microstructure, with the goal of reducing residual stress and stabilizing the molten pool.

[0170] The laser additive remanufacturing module 105 is used to perform laser additive remanufacturing on the target workpiece according to the process parameter optimization instructions.

[0171] The laser additive stress and tissue synergistic control system of this embodiment belongs to the same inventive concept as the aforementioned method, and can be understood with reference to the above description, which will not be repeated here.

[0172] This application also provides a high-performance SLM-AlSi10Mg plate welded component, which is prepared by the laser additive stress and microstructure synergistic control method described above.

[0173] Furthermore, the repair components prepared using the laser additive stress and microstructure synergistic control method of this application can be applied to the welding repair and remanufacturing of aero-engine blades, gas turbine hot-end components, ship propulsion systems, and key equipment of nuclear power plants.

[0174] In the description of this application, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is usually based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this application and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this application.

[0175] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this application.

Claims

1. A method for synergistic control of stress and tissue in laser additive manufacturing, characterized in that, Includes the following steps: S01. A fluid simulation model of the laser additive remanufacturing process of the target workpiece is constructed using the VOF method; S02. Based on the fluid simulation model, obtain the stress distribution simulation calculation results of the target workpiece and the predicted microstructure of the remanufactured area; S03. Based on the stress distribution simulation calculation results and predicted microstructure, with the goal of reducing residual stress and stabilizing the molten pool, generate process parameter optimization instructions. S04. Perform laser additive remanufacturing on the target workpiece according to the process parameter optimization instructions; Step S01 includes: S101. Conduct laser additive remanufacturing experiments and / or simulation experiments on the target workpiece to obtain experimental data, including the process parameters of laser additive remanufacturing and the workpiece parameters of the target workpiece. S102. Based on the experimental data, the fluid simulation model is constructed using the VOF method; S103. Calculate the component differences in the properties of the substrate: ， in, This represents the sum of various thermophysical parameters of a material. This represents a specific thermophysical property parameter of a single component. This represents the mass fraction of a single component in the material. In step S101, after performing the laser additive remanufacturing experiment on the target workpiece, the following is also included: The target workpiece is cut and sampled to obtain a specimen, and the specimen is subjected to microstructure characterization analysis to obtain the characterization experimental results; In step S103, when calculating the thermophysical parameters of each component, the calculated thermophysical parameters are compared with the characterization experiment results, and the thermophysical parameters of the material are adjusted in real time according to the characterization experiment results. In step S02, obtaining the stress distribution simulation calculation results of the target workpiece based on the fluid simulation model includes the following steps: S02a1. Construct a stress solution model based on the fluid simulation model; S02a2. Input the thermophysical parameters into the stress solution model, and calculate the transient temperature field of the processing process through the stress solution model; S02a3. Analyze the influence of the transient temperature field during the processing on the thermal deformation and thermal stress of the material, and obtain the simulation calculation results of the stress distribution; Wherein, the input energy of the stress solution model is equal to the energy of the fluid simulation model, satisfying: ； in, This represents the input energy of the stress solution model. Indicates laser power. Indicates laser absorption efficiency. Indicates the effective laser radius. Indicates the scanning speed. Indicates processing time. The x-coordinate represents the position where the laser is applied. The vertical coordinate represents the position where the laser is applied; Heat loss is calculated using the convection-diffusion equation: ， in, Indicates heat loss, Indicates the convective heat transfer coefficient. Represents Boltzmann's constant. Indicates radiative emissivity. Indicates the processing environment temperature. This indicates the actual temperature of the material being processed.

2. The laser additive manufacturing stress and tissue synergistic control method according to claim 1, characterized in that, In step S02, obtaining the predicted microstructure of the remanufactured area based on the fluid simulation model includes the following steps: S02b1. Based on the fluid simulation model, obtain the simulated molten pool flow field, the simulated processing temperature field, and the simulated solute field; S02b2. Obtain thermal history data based on the simulated molten pool flow field and the simulated processing temperature field; S02b3, Calculate the temperature gradient of the entire region based on the aforementioned thermal history data. and solidification rate ; S02b4. The critical conditions for columnar / equiaxed crystal transformation are calculated using the KGT model to predict the grain morphology and type distribution. The temperature gradient in the KGT model and solidification rate Calculate using the following formula: ， ， ， in, Indicates the difference in temperature change. Indicates unit distance, Indicates the position of the normal direction. Indicates a unit of time. This indicates the critical cooling rate.

3. The laser additive manufacturing stress and tissue synergistic control method according to claim 2, characterized in that, In step S02, obtaining the predicted microstructure of the remanufactured region based on the fluid simulation model further includes the following steps: S02b5. Based on the simulated solute field, real-time concentration distribution data of solute elements at different locations in the molten pool are obtained using the Scheil-Gulliver non-equilibrium solidification model. The Scheil-Gulliver nonequilibrium solidification model is expressed as follows: ， in, Indicates component density, Indicates the liquid volume fraction. Indicates components Mass fraction in the liquid phase Indicates components Mass fraction in the solid phase Indicates components Molecular diffusion coefficient in the liquid phase, Indicates components The interphase mass transfer coefficient, Indicates the liquid flow velocity. Indicates the solid-phase diffusion rate. Indicates the source term; S02b6. Predict the distribution density of the secondary phase based on the real-time concentration distribution data of the solute elements: ， ， in, Represents element The content of eutectic components, Represents element The calculated content value, Represents element The allocation coefficient, Indicates the solid fraction. Indicates matrix Solid solution elements The solid solution content, Eutectic The calculated content value.

4. The laser additive manufacturing stress and tissue synergistic control method according to claim 3, characterized in that, Step S03 includes: S301. Set constraints on process parameters, including heat input threshold, melt pool range, and forming quality requirements. Use a multi-objective optimization method to construct a mapping relationship between process parameters, stress distribution, and microstructure characteristics: ， in, Represents the variable Perform multi-objective function optimization. Represents variables, This indicates the transpose operation. Indicates the molten pool size conditions. Indicates the fill condition. Indicates stress condition, Indicates the range of melting depth. Indicates the depth of the weld bevel. Indicates the height of the cladding layer. Indicates the defined range of melt width. Indicates the width of the weld bevel. Indicates the width of the cladding layer. Indicates laser power. Indicates the rotation speed of the powder feeding turntable. Indicates the scan rate. Indicates the selection of scanning strategy. Indicates a sequential scanning strategy. Indicates a serpentine scanning strategy; S302. Preset the range of process parameters to be used, including the range of laser power, the range of powder feeding turntable speed, the range of scanning rate and scanning strategy; S303. Within the range of the stated process parameters, select multiple combinations of process parameters for simulation processing; S304. Calculate the residual thermal stress of the target workpiece after simulation processing for each combination of process parameters. S305. Select the combination of process parameters that results in complete fusion at the forming joint and minimizes the residual thermal stress as the optimal combination of process parameters. S306. Based on the optimal combination of process parameters, generate the process parameter optimization instruction.

5. The laser additive manufacturing stress and tissue synergistic control method according to claim 1, characterized in that, Also includes: S05. Perform a heat treatment process on the target workpiece, the heat treatment process including: The target workpiece is subjected to solution treatment within a first temperature range. The target workpiece is cooled; The target workpiece is subjected to aging treatment within a second temperature range; The first temperature range is larger than the second temperature range.

6. A laser additive manufacturing stress and tissue synergistic control system, characterized in that, include: The simulation model building module is used to construct a fluid simulation model of the laser additive remanufacturing process for the target workpiece, including: Laser additive remanufacturing experiments and / or simulation experiments are conducted on the target workpiece to obtain experimental data, including process parameters for laser additive remanufacturing and workpiece parameters of the target workpiece. Based on the experimental data, the fluid simulation model was constructed using the VOF method; Calculate the component differences in the substrate properties: ， in, This represents the sum of various thermophysical parameters of a material. This represents a specific thermophysical property parameter of a single component. This represents the mass fraction of a single component in the material. After performing laser additive remanufacturing experiments on the target workpiece, the following steps are also included: The target workpiece is cut and sampled to obtain a specimen, and the specimen is subjected to microstructure characterization analysis to obtain the characterization experimental results; When calculating the thermophysical parameters of each component, the calculated thermophysical parameters are compared with the characterization experiment results, and the thermophysical parameters of the material are adjusted in real time according to the characterization experiment results. Based on the fluid simulation model, the simulation calculation results of the stress distribution of the target workpiece are obtained through the following steps: A stress solution model is constructed based on the fluid simulation model. The thermophysical parameters are input into the stress solution model, and the transient temperature field of the processing process is calculated through the stress solution model. The influence of the transient temperature field during the processing on the thermal deformation and thermal stress of the material is analyzed, and the simulation calculation results of the stress distribution are obtained. Wherein, the input energy of the stress solution model is equal to the energy of the fluid simulation model, satisfying: ； in, This represents the input energy of the stress solution model. Indicates laser power. Indicates laser absorption efficiency. Indicates the effective laser radius. Indicates the scanning speed. Indicates processing time. The x-coordinate represents the position where the laser is applied. The vertical coordinate represents the position where the laser is applied; Heat loss is calculated using the convection-diffusion equation: ， in, Indicates heat loss, Indicates the convective heat transfer coefficient. Represents Boltzmann's constant. Indicates radiative emissivity. Indicates the processing environment temperature. Indicates the actual temperature of the processed material; The stress field calculation module is used to obtain the stress distribution simulation calculation results of the target workpiece based on the fluid simulation model. The tissue morphology prediction module is used to predict the tissue morphology of the remanufactured area based on the fluid simulation model. The process parameter optimization module is used to generate process parameter optimization instructions based on the stress distribution simulation calculation results and predicted microstructure, with the goal of reducing residual stress and stabilizing the molten pool. A laser additive remanufacturing module is used to perform laser additive remanufacturing on the target workpiece according to the process parameter optimization instructions.

7. A welded component made of SLM-AlSi10Mg plate, characterized in that, It was prepared using the laser additive stress and tissue synergistic control method as described in any one of claims 1-5.

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