Workpiece residual stress optimization method and system, electronic equipment and storage medium

Abstract

The invention provides a workpiece residual stress optimization method and system, electronic equipment and a storage medium. The method comprises the steps that the wall thickness and residual stress data of a target area of a workpiece in the LPBF forming process are determined; according to a compensation model, compensation process parameters matched with the wall thickness and residual stress data are determined; wherein the compensation model represents stress distribution rules of workpieces with different wall thicknesses under different process parameters; and performing reverse compensation on the workpiece based on the compensation process parameters. In the workpiece forming process, reverse compensation is conducted on the workpiece based on the compensation technological parameters, and stress regulation and control over the target area of the workpiece are achieved. And through the reverse deformation compensation technology, the thermally induced buckling deformation trend can be counteracted in advance in the forming stage, so that the risk of warping, deformation or edge cracks after the workpiece is formed is effectively reduced.

Description

Technical Field

[0001] This disclosure relates to the field of metal additive manufacturing technology, and in particular to a method, system, electronic device, and storage medium for optimizing residual stress in a workpiece. Background Technology

[0002] Laser Powder Bed Fusion (LPBF) is a high-precision, high-degree-of-freedom metal additive manufacturing technology. This technology utilizes a high-energy laser beam to melt metal powder material layer by layer in a powder bed, stacking components according to a three-dimensional model. LPBF is widely used in high-value-added fields such as aerospace, energy, and medical devices, and has significant advantages in manufacturing complex structural components (such as cooling blades, stents, and implants). However, traditional LPBF forming technologies generally employ a small layer thickness strategy of 30-50 μm to ensure good density and surface quality. While this method can obtain high-quality structures, it suffers from long manufacturing cycles, low printing efficiency, and limited component size, thus hindering its widespread application in the manufacturing of medium to large-scale complex metal structures.

[0003] To further improve the processing efficiency of LPBF (Liquid-Layer Brush) technology, some manufacturers have attempted to extend the layer thickness to ≥100μm in recent years. However, in typical thin-walled structures such as turbine blades, the use of large-layer-thickness LPBF forming is often accompanied by significant residual stress. Especially for slender blades with a wall thickness of 2-4mm, their small cross-sectional dimensions, intense thermal gradients, and high ratio of heated to cooled areas easily lead to strong tensile residual stress concentrations in the edge regions, resulting in component warping, deformation, or even cracking. In existing technologies, methods such as overall annealing, hot isostatic pressing, or scanning strategy optimization are commonly used to reduce the residual stress level of components. Although these methods have some effect on overall stress release, their ability to control local stress in complex thermal gradients and thin-walled edge regions is limited, thus restricting the further application of large-layer-thickness LPBF technology in high-precision complex structural components. Summary of the Invention

[0004] To address the aforementioned problems, this disclosure aims to provide a method, system, electronic device, and storage medium for optimizing residual stress in workpieces, thereby overcoming the insufficient local stress control in existing technologies. To achieve the above objective, this disclosure adopts the following technical solution:

[0005] This disclosure solves the above-mentioned technical problems through the following technical solution:

[0006] Firstly, a method for optimizing residual stress in a workpiece is provided, comprising:

[0007] Determine the wall thickness and residual stress data of the target region of the workpiece during the LPBF forming process;

[0008] Based on the compensation model, compensation process parameters matching the wall thickness and residual stress data are determined; wherein, the compensation model characterizes the stress distribution law of workpieces with different wall thicknesses under different process parameters;

[0009] The workpiece is subjected to reverse compensation based on the compensation process parameters.

[0010] Optionally, the compensation model is obtained in the following way:

[0011] The LPBF forming process of the workpiece sample was simulated using multiple sets of different target process parameters, and simulation data was obtained. The simulation data included the wall thickness and residual stress data of the workpiece during the simulation process.

[0012] The workpiece sample is manufactured using the target process parameters, and test data is obtained, including the measured wall thickness and residual stress data during the manufacturing process of the workpiece sample.

[0013] The compensation model is obtained by performing regression analysis on the target process parameters, the simulation data, and the experimental data; or the compensation model is obtained by training a neural network using the target process parameters, the simulation data, and the experimental data as training samples.

[0014] Optionally, before the step of determining the wall thickness and residual stress data of the target region of the workpiece during the LPBF forming process, the method further includes:

[0015] Finite element analysis is performed on the temperature field and stress field of the workpiece to determine the target region; wherein, the target region includes the thermal stress concentration area and / or the configuration thermal coupling area of ​​the workpiece; the configuration thermal coupling area is the stress state transition area at the sudden change in wall thickness of the workpiece.

[0016] Optionally, the compensation process parameters include melting parameters and heat treatment process parameters; reverse compensation of the workpiece based on the compensation process parameters includes:

[0017] The workpiece is remelted based on the melting parameters;

[0018] The molten workpiece is heat-treated based on the aforementioned heat treatment process parameters.

[0019] Optionally, heat treatment is performed on the molten workpiece based on the heat treatment process parameters, including:

[0020] The workpiece was placed in a box-type resistance furnace and kept at 900°C for 2 hours, and then air-cooled with the furnace.

[0021] Optionally, after the step of performing reverse compensation on the workpiece based on the compensation process parameters, the method further includes: in response to the residual stress data not conforming to manufacturing standards, returning to the step of determining compensation process parameters that match the wall thickness and residual stress data.

[0022] Secondly, a residual stress optimization system for a workpiece is provided, comprising:

[0023] The first determining module is used to determine the wall thickness and residual stress data of the target area of ​​the workpiece during the LPBF forming process.

[0024] The second determining module is used to determine the compensation process parameters that match the wall thickness and residual stress data according to the compensation model; wherein, the compensation model characterizes the stress distribution law of workpieces with different wall thicknesses under different process parameters;

[0025] The compensation module is used to perform reverse compensation on the workpiece based on the compensation process parameters.

[0026] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and for running on the processor, wherein the processor executes the computer program to implement the residual stress optimization method for a workpiece as described in any one of the first aspects.

[0027] Thirdly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing residual stress of a workpiece as described in any one of the first aspects.

[0028] Fourthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements a method for optimizing residual stress in a workpiece as described in any one of the first aspects.

[0029] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.

[0030] The positive and progressive effects of this disclosure are as follows: During the workpiece forming process, this disclosure performs a reverse compensation operation on the workpiece based on compensation process parameters, thereby achieving directional stress control of residual stress in the target area of ​​the workpiece. By pre-counteracting the warping tendency caused by heat through reverse deformation compensation technology, the risk of warping, deformation, and edge cracks in the formed workpiece can be effectively reduced. Attached Figure Description

[0031] Figure 1 A flowchart illustrating a method for optimizing residual stress in a workpiece, provided as an exemplary embodiment of this disclosure;

[0032] Figure 2aAn application scenario diagram illustrating the sampling analysis of a workpiece using a residual stress optimization method provided in an exemplary embodiment of this disclosure;

[0033] Figure 2b for Figure 2a The front view of the workpiece is shown;

[0034] Figure 2c for Figure 2a The rear view of the workpiece is shown;

[0035] Figure 2d For heat treatment technologies provided by existing technologies Figure 2b A schematic diagram of the test results of a secondary residual stress test performed on a workpiece after processing;

[0036] Figure 2e For heat treatment technologies provided by existing technologies Figure 2c A schematic diagram of the test results of a secondary residual stress test performed on a workpiece after processing;

[0037] Figure 2f To optimize the residual stress using the residual stress optimization method provided in the embodiments of this disclosure. Figure 2b A schematic diagram of the test results of a secondary residual stress test performed on the workpiece after it has been processed;

[0038] Figure 2g To optimize the residual stress using the residual stress optimization method provided in the embodiments of this disclosure. Figure 2c A schematic diagram of the test results of a secondary residual stress test performed on the workpiece after it has been processed;

[0039] Figure 3a A schematic diagram of the simulation results of residual stress distribution in the workpiece without optimization.

[0040] Figure 3b This is a schematic diagram of the simulation results of the residual stress distribution of a workpiece after optimization using the residual stress optimization method provided in the embodiments of this disclosure;

[0041] Figure 3c for Figure 3a and Figure 3b A comparison chart of the simulation results and the measured results of residual stress in the middle.

[0042] Figure 4 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present disclosure. Detailed Implementation

[0043] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.

[0044] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the claims or the context of the embodiments, and should not be construed as an unnecessary limitation. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.

[0045] This disclosure provides a method for optimizing the residual stress of a workpiece, primarily targeting the residual stress of workpieces with large layer thickness (≥100μm) and thin-walled structures. The aim is to improve the stability of the workpiece structure by optimizing the residual stress. This disclosure does not specifically limit the type of workpiece; for example, a steam turbine blade.

[0046] See Figure 1 The residual stress optimization method includes the following steps:

[0047] Step 101: Determine the wall thickness and residual stress data of the target area of ​​the workpiece during the LPBF forming process.

[0048] Preferably, step 101 determines the wall thickness and residual stress data of the deposited workpiece.

[0049] The target region is the area in the workpiece where residual stress is likely to exist and needs to be optimized. Taking a slender blade with a wall thickness of 2-4 mm as an example, its cross-sectional dimensions are small, its thermal gradient is large, and its heated to cooled area ratio is high, making it prone to significant tensile residual stress concentration in the edge region. Therefore, the edge region of the blade is defined as the target region. The target region can be specified manually or determined through simulation.

[0050] The wall thickness and residual stress data of the target area of ​​the workpiece can be measured by various methods. For example, residual stress data can be obtained through techniques such as drilling. Residual stress data may include, but is not limited to, at least one of the following parameters: stress gradient change, stress state transition behavior, etc.

[0051] Step 102: Based on the compensation model, determine the compensation process parameters that match the wall thickness and residual stress data.

[0052] The compensation model characterizes the stress distribution of workpieces with different wall thicknesses under different process parameters. These compensation process parameters include heat treatment parameters, laser power density, scanning speed, scanning path sequence, and the number of local remelting cycles.

[0053] The compensation model is used to guide the reverse compensation process of the workpiece, thereby achieving effective control of the residual stress of the workpiece.

[0054] Step 103: Perform reverse compensation on the workpiece based on the compensation process parameters.

[0055] In this embodiment, the entire process of workpiece forming is monitored, and reverse compensation is performed on the workpiece according to steps 101 to 103. Reverse compensation can be performed periodically, with the execution interval dynamically adjusted based on the residual stress level of the workpiece. For example, the interval between two adjacent reverse compensations is related to the residual stress of the workpiece; the lower the residual stress, the longer the time interval between two adjacent compensations can be.

[0056] In this embodiment, during the workpiece forming process, a reverse compensation operation is performed on the workpiece based on the compensation process parameters, thereby achieving stress control in the target area of ​​the workpiece. By pre-counteracting the warping tendency caused by heat through reverse deformation compensation technology, the probability of warping, deformation, or even edge cracks in the manufactured workpiece can be effectively reduced.

[0057] In one embodiment, by parametrically modeling the geometry of a workpiece (e.g., a blade) and optimizing its wall thickness distribution, the workpiece is divided into multiple regions with continuous or partitioned thickness gradients during the design phase. By controlling the wall thickness transition slope, fillet radius, and structural support path, the local heat dissipation capacity and heat flux density distribution are adjusted, thereby achieving temperature gradient homogenization. This design allows thermal stress in stress concentration areas to diffuse to adjacent thicker areas, forming a stress redistribution path and achieving a balanced distribution of residual stress. Furthermore, since the geometry directly affects the molten pool temperature field and heat conduction path, different thickness regions exhibit differences in thermal inertia during the heating and cooling stages, resulting in inconsistent thermal strain. When the heat release in the thick-walled region is slower than that in the thin-walled region, a phase reversal of compressive and tensile stresses occurs at the interface, forming a tensile-compressive stress transition zone. This transition zone can effectively buffer the strain differences caused by the thermal gradient, reduce the risk of edge warping and cracking, thereby achieving directional control and overall release of residual stress. In the aforementioned thick-thin interface region, the stress distribution law is extracted through finite element analysis, and the stress state transition model is verified. The configuration scheme is repeatedly revised based on stress monitoring data. The optimized configuration not only improves the synergy between structural stiffness and heat dissipation, but also significantly reduces the residual stress concentration at long sides and sharp corners, achieving coupled optimization of configuration and thermal stress and balanced control of stress field.

[0058] In one embodiment, the compensation model is obtained as follows:

[0059] S1. Simulate the LPBF forming process of the workpiece sample based on multiple sets of different target process parameters, and obtain simulation data.

[0060] The target process parameters include laser power, scanning speed, layer thickness, scanning spacing, and preheating temperature. The simulation data includes the wall thickness distribution, residual stress field, and temperature field evolution curves of the workpiece in each layer thickness range.

[0061] Preferably, in S1, finite element analysis is used to analyze the deformation trend of the workpiece during the forming process in order to obtain simulation data.

[0062] The target process parameters can be set according to actual needs. The more target process parameters there are, the wider the coverage, and the more comprehensive and accurate the compensation model will be.

[0063] S2. Prepare workpiece samples using the same target process parameters and obtain experimental data.

[0064] The test data include the measured wall thickness, residual stress gradient, stress state transition point, and stress release rate after reverse compensation during the manufacturing process of the workpiece sample.

[0065] S3. Perform multidimensional regression analysis or neural network fitting on the target process parameters, simulation data and experimental data to establish the mapping relationship of "process parameters - stress distribution - compensation response" and obtain the compensation model.

[0066] This compensation model can predict the optimal compensation process parameters based on the input wall thickness and stress variation trends, which can then guide subsequent reverse compensation operations.

[0067] In this embodiment, residual stress data of the workpiece in multiple dimensions (e.g., height, in-plane, and thickness) is obtained through a combination of simulation calculations and actual measurements. At the boundary between the workpiece's thickness and thinness response domains, local stress gradient changes and stress state transitions are captured based on stress distribution patterns to optimize process parameters. Multidimensional stress sensing enables real-time monitoring and feedback of stress evolution in various configuration regions of the workpiece, providing a comprehensive characterization of the internal stress configuration during printing and after forming. This characterization serves as the data foundation for constructing a compensation model. Based on this model, accurate identification and significant stress reduction can be achieved in target areas of the workpiece (e.g., high-risk stress areas on the long side of blades), mitigating stress concentration trends and improving the structural stability and deformation resistance of the formed workpiece.

[0068] In one embodiment, the target process parameters, simulation data, and experimental data are used as training samples to train the neural network to obtain the compensation model. The model training process is described in the relevant technical description and will not be repeated here.

[0069] In one embodiment, prior to the step of determining the target region of the workpiece based on the wall thickness and residual stress data during the LPBF forming process, the method further includes: performing finite element analysis on the temperature field and stress field of the workpiece to determine the target region.

[0070] The target region includes the workpiece's thermal stress concentration area and / or configuration thermal coupling area. The configuration thermal coupling area is the stress state transition region at the point of abrupt change in wall thickness.

[0071] It should be noted that the process parameters used in the finite element analysis are the same as those used in LPBF forming.

[0072] In this embodiment, the thermal stress concentration area and / or configuration thermal coupling area of ​​the workpiece can be effectively identified, providing a basis for subsequent reverse compensation to gradually achieve stress state equalization.

[0073] In one embodiment, the compensation process parameters include melting parameters and heat treatment process parameters; reverse compensation of the workpiece based on the compensation process parameters includes: remelting the workpiece based on the melting parameters, and heat treating the melted workpiece based on the heat treatment process parameters.

[0074] In this embodiment, the target area of ​​the workpiece is melted and heat-treated to achieve thermal-structural coupling control, which effectively relaxes the high-stress areas in various parts of the workpiece, makes the spatial distribution of residual stress more uniform, and ultimately achieves stress configuration equilibrium and local stress decoupling state, thereby significantly reducing the risk of deformation and cracking of the workpiece caused by residual stress.

[0075] In one embodiment, heat treatment of a molten workpiece based on heat treatment process parameters includes: placing the workpiece in a box-type resistance furnace, holding it at 900°C for 2 hours, and then air-cooling it with the furnace.

[0076] In this embodiment, a 900°C × 2h heat treatment process is used to systematically release the internal residual stress, which can further reduce the overall residual stress level of the workpiece. By comparing the residual stress distribution of 2mm and 4mm wall thickness blades in the deposited and heat-treated states, it is revealed that there is a general pattern of residual stress concentration in the blade edge region (especially the long side). At the same time, it is confirmed that annealing treatment can significantly reduce the principal stress level in this region: some high stress points even change from tensile stress to compressive stress, demonstrating excellent residual stress release effect and structural recovery ability.

[0077] In one embodiment, a combined strategy of graded heat treatment and structural synergistic regulation is implemented on the workpiece after reverse compensation to gradually achieve stress state equilibration. Specifically: First, a full-area preheating treatment is performed: the entire workpiece is heated to approximately 600 °C and held for 0.5–1 h, allowing the temperature difference between different wall thickness areas to gradually converge and alleviating the initial stress peak; Second, zoned annealing and localized directional heating are performed: for the thermal coupling area and the thickness transition area, local infrared heating or resistance heating is used to raise the local temperature to 800–850 °C and maintain it for 0.5 h, allowing the stress concentration points in this area to relax and strain to reverse; Finally, an overall heat preservation and slow cooling stage is performed: the entire workpiece is held at 900 °C for 2 h and then cooled in the furnace to ensure that thermal stress release and microstructure reequilibrium occur simultaneously.

[0078] Through the aforementioned graded heat treatment path of "overall preheating - local strengthening - global equilibration", heat energy is conducted in a gradient within the structure, which promotes the coordinated matching of stress release rate and geometric stiffness characteristics in different regions, thereby achieving dynamic equilibrium of stress configuration and decoupling stability of local stress, significantly reducing the risk of warping and cracking of workpieces due to residual stress.

[0079] Compared to traditional additive manufacturing heat treatment methods, which struggle to effectively address residual stress concentration at component edges, this embodiment explores a universal annealing stress release path applicable to complex profiles and thin-walled structures with varying wall thicknesses. This embodiment significantly improves the service reliability of large, high-precision blade components, providing a novel solution and theoretical support for high-performance post-processing of LPBF additively manufactured metal components.

[0080] In one embodiment, after the step of reverse compensation of the workpiece based on the compensation process parameters, the method further includes: in response to the residual stress data not conforming to manufacturing standards, returning to the step of determining compensation process parameters that match the wall thickness and residual stress data.

[0081] Manufacturing standards can be set according to actual needs.

[0082] In this embodiment, after each reverse compensation, it is determined whether the residual stress data of the workpiece meets the manufacturing standards. If not, multiple reverse compensations are performed on the workpiece until the residual stress data meets the manufacturing standards. This effectively reduces the residual stress of the workpiece. Taking a blade as an example, the method provided in this embodiment achieves an average residual stress reduction of 40%–70% in the long side region of the blade, effectively mitigating the thermal deformation trend of the workpiece. The numerical simulation results are in high agreement with the measured data, indicating that this stress control method has good predictability and controllability.

[0083] The following example uses LPBF forming of a 316L stainless steel blade with a layer thickness of 100 μm, a laser power of 400 W, a scanning speed of 650 mm / s, and a scanning interval of 0.11 mm. By comparing the edge stress distribution of the deposited and heat-treated samples, the method of this embodiment demonstrates a significant effect in stress relief. The formed samples are standard hollow turbine stator blades with wall thicknesses of 2 mm and 4 mm, respectively.

[0084] The implementation steps are as follows:

[0085] Step 1: Construct a residual stress simulation model. Finite element analysis was performed on the temperature and stress fields of blades with wall thicknesses of 2 mm and 4 mm using Simufact Additive and Xpert analysis tools, respectively. Simulation results show that the edge region of the long side of the blade is a thermal stress concentration area, and the evolution of residual stress in this region needs to be closely monitored.

[0086] Step 2: Sample Preparation. Based on the simulation model, the printing path was designed, and a 316L stainless steel hollow stator blade component was constructed using a large layer thickness (100μm). After forming, no heat treatment was performed, resulting in the deposited sample. Preliminary surface treatment was performed after printing to remove adhering powder.

[0087] Step 3: Setting up stress test points. For example... Figures 2a-2c As shown, 18 residual stress test points were arranged on the inner and outer sides of the blade, covering the long side, short side and middle area. The points were marked as L1–L3 (long side), S1–S3 (short side) and C1–C3 (middle).

[0088] Step 4: Stress Testing. Residual stress is detected using the borehole method to obtain residual stress data at various points in the sedimentary state.

[0089] Step 5: Reverse Compensation. Based on the compensation model, determine the compensation process parameters that match the wall thickness and residual stress data, and perform reverse compensation on the sample based on the compensation process parameters.

[0090] Step 5: Heat Treatment. Place the sample in a box-type resistance furnace and hold it at 900°C for 2 hours, followed by air cooling with the furnace. This treatment order is not interchangeable; printing must be performed before heat treatment to release residual stress.

[0091] Step Six: Post-Heat Treatment Stress Testing. A second residual stress test is performed at the same location after heat treatment to obtain residual stress data for the heat-treated state.

[0092] Experimental results show that:

[0093] See Figure 2d-2eThe principal stresses at points L1, L2, and L3 on the long side of the 2mm thick blade in the sedimentary state were 344.59 MPa, 365.3 MPa, and 231.09 MPa, respectively. The principal stresses at points S1 and S3, located at the blade edge and in locally thin areas, were -257.39 MPa and -320.91 MPa, respectively. The principal stresses at points S4 and S6 on the corresponding outer side of the blade were -295.5 MPa and 287.75 MPa, respectively. These results indicate that the long side region of the blade in the sedimentary state mainly exhibits high tensile residual stress, while some edge / transition regions show compressive residual stress. A clear stress gradient and stress concentration characteristics exist between the two types of regions.

[0094] Subsequently, the residual stress control scheme proposed in this disclosure was implemented on the blade, namely, a reverse compensation process based on a compensation model, followed by staged heat treatment. The blade was then tested again after treatment (in the heat-treated state), see [link to relevant documentation]. Figure 2f-2g The principal stresses at measuring points L1, L2, and L3 along the long side decreased to 279.58 MPa, 71.9 MPa, and 30 MPa, respectively; the principal stresses at measuring points S1 and S3 in the edge and transition regions were -47.88 MPa and -38.24 MPa, respectively; and the principal stresses at measuring points S4 and S6 on the outer side were 178.89 MPa and -220.55 MPa, respectively. It can be seen that the stress level in the long side regions (especially L2 and L3), which were originally under high tensile residual stress, was significantly reduced to the tens of MPa level after heat treatment. The significant compressive / tensile stress in the edge regions was also significantly released, and even stress redistribution occurred. This indicates that the control process can effectively weaken local stress concentration and improve the overall structural stability.

[0095] Both sets of data above show that:

[0096] This embodiment can significantly reduce the residual stress at the edge of the turbine blade, especially exhibiting excellent stress relief capability in the long side region;

[0097] from Figures 3a-3b As can be seen, the residual stress distribution obtained from the finite element simulation is highly consistent with the measured results: in the deposited state, there is significant tensile residual stress concentration in the long side and tip region of the blade, with the maximum principal stress being approximately 380 MPa; after reverse compensation and staged heat treatment, the overall stress field is significantly homogenized, the stress level in the long side and sharp corner region drops to below 80 MPa, and the edge region is partially transformed into slight compressive stress. This trend is completely consistent with the measured data (stress at points L1, L2, and L3 drops to 279.58 MPa, 71.9 MPa, and 30 MPa, respectively), verifying the accuracy and effectiveness of the simulation model and heat treatment regime.

[0098] Figures 3a-3c The results, presented qualitatively and quantitatively, illustrate the situation before and after residual stress optimization, such as... Figure 3a The simulation results of residual stress distribution in the workpiece without optimization are presented. It can be seen that there is significant tensile residual stress concentration in the long side and sharp corner areas of the workpiece, with high principal stress peaks, uneven overall residual stress distribution, and obvious stress gradient.

[0099] Figure 3b A simulation diagram of the residual stress distribution in the workpiece after optimization through reverse compensation and heat treatment processes is presented. Compared to Figure 3a The stress distribution is more uniform, the original high stress concentration area is significantly weakened, the overall level of residual stress drops significantly, and the principal stress on the long side drops from high tensile stress to low tensile stress or slight compressive stress range. Figure 3c for Figure 3a and Figure 3b The comparison between the simulation results and the measured results of residual stress shows that the simulation results both indicate a larger deformation on the longer side. This demonstrates a high degree of consistency between the compensation model's predicted results and the measured stress values, verifying the effectiveness and accuracy of the stress optimization model and process path in this invention.

[0100] This processing path is applicable to samples with different wall thicknesses, demonstrating good structural adaptability and engineering versatility.

[0101] It should be noted that the above steps are performed sequentially. Since the formation and accumulation of residual stress depend on the heat input path during additive manufacturing, the "print first, anneal later" processing order is not interchangeable. Those skilled in the art can extend this method to thin-walled components of other sizes and shapes.

[0102] The results in this embodiment demonstrate that the annealing residual stress control method can effectively release the edge thermal stress of LPBF 316L stainless steel blades, reduce stress concentration and warping risks during the forming process, and enhance the service stability and structural integrity of the components. This method has good adaptability and can be widely applied to laser additive manufacturing of metal components with complex wall thicknesses and multi-curved contours, possessing significant engineering value and industrial application potential.

[0103] Corresponding to the aforementioned embodiments of the residual stress optimization method for workpieces, this disclosure also provides embodiments of a residual stress optimization system for workpieces.

[0104] This disclosure also provides a residual stress optimization system for a workpiece, which is used to implement the method provided in any of the above embodiments. The system includes:

[0105] The first determining module is used to determine the wall thickness and residual stress data of the target area of ​​the workpiece during the LPBF forming process;

[0106] The second determining module is used to determine the compensation process parameters that match the wall thickness and residual stress data according to the compensation model; wherein, the compensation model characterizes the stress distribution law of workpieces with different wall thicknesses under different process parameters;

[0107] The compensation module is used to perform reverse compensation on the workpiece based on the compensation process parameters.

[0108] Optionally, the compensation model is obtained in the following way:

[0109] The LPBF forming process of the workpiece sample was simulated using multiple sets of different target process parameters, and simulation data was obtained. The simulation data included the wall thickness and residual stress data of the workpiece during the simulation process.

[0110] The workpiece sample is manufactured using the target process parameters, and test data is obtained, including the measured wall thickness and residual stress data during the manufacturing process of the workpiece sample.

[0111] The compensation model is obtained by performing regression analysis on the target process parameters, the simulation data, and the experimental data; or the compensation model is obtained by training a neural network using the target process parameters, the simulation data, and the experimental data as training samples.

[0112] Optionally, the system also includes:

[0113] A simulation model is used to perform finite element analysis on the temperature field and stress field of the workpiece to determine the target region; wherein, the target region includes the thermal stress concentration area and / or the configuration thermal coupling area of ​​the workpiece; the configuration thermal coupling area is the stress state transition area at the sudden change in wall thickness of the workpiece.

[0114] Optionally, the compensation process parameters include melting parameters and heat treatment process parameters; the compensation module is specifically used for:

[0115] The workpiece is remelted based on the melting parameters;

[0116] The molten workpiece is heat-treated based on the aforementioned heat treatment process parameters.

[0117] Optionally, the compensation module is specifically used for:

[0118] The workpiece was placed in a box-type resistance furnace and kept at 900°C for 2 hours, and then air-cooled with the furnace.

[0119] The compensation module can be implemented, but is not limited to, through a robot.

[0120] Optionally, the system also includes:

[0121] The judgment module, in response to the residual stress data not conforming to the manufacturing standard, calls the second determination module.

[0122] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.

[0123] Figure 4 This is a schematic diagram of the structure of an electronic device according to an example embodiment of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the residual stress optimization method for a workpiece as described in any of the above embodiments. Figure 4 The electronic device 40 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0124] like Figure 4 As shown, the electronic device 40 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 40 may include, but are not limited to: at least one processor 41, at least one memory 42, and a bus 43 connecting different system components (including memory 42 and processor 41).

[0125] Bus 43 includes a data bus, an address bus, and a control bus.

[0126] The memory 42 may include volatile memory, such as random access memory (RAM) 421 and / or cache memory 422, and may further include read-only memory (ROM) 423.

[0127] The memory 42 may also include a program tool 425 (or utility) having a set (at least one) program module 424, such program module 424 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0128] The processor 41 executes various functional applications and data processing by running computer programs stored in the memory 42, such as the residual stress optimization method for workpieces provided in any of the above embodiments.

[0129] Electronic device 40 can also communicate with one or more external devices 44 (e.g., keyboard, pointing device, etc.). This communication can be performed via input / output (I / O) interface 45. Furthermore, electronic device 40 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 46. As shown, network adapter 46 communicates with other modules of electronic device 40 via bus 43. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 40, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0130] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0131] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the residual stress optimization method for a workpiece provided in any of the above embodiments.

[0132] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0133] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the residual stress optimization method for a workpiece as described in any of the preceding embodiments.

[0134] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0135] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.

Claims

1. A method for optimizing residual stress in a workpiece, characterized in that, include: Determine the wall thickness and residual stress data of the target region of the workpiece during the LPBF forming process; Based on the compensation model, compensation process parameters matching the wall thickness and residual stress data are determined; wherein, the compensation model characterizes the stress distribution law of workpieces with different wall thicknesses under different process parameters; The workpiece is subjected to reverse compensation based on the compensation process parameters.

2. The method for optimizing residual stress in a workpiece according to claim 1, characterized in that, The compensation model is obtained in the following way: The LPBF forming process of the workpiece sample was simulated using multiple sets of different target process parameters, and simulation data was obtained. The simulation data included the wall thickness and residual stress data of the workpiece during the simulation process. The workpiece sample is manufactured using the target process parameters, and test data is obtained, including the measured wall thickness and residual stress data during the manufacturing process of the workpiece sample. The compensation model is obtained by performing regression analysis on the target process parameters, the simulation data, and the experimental data; or the compensation model is obtained by training a neural network using the target process parameters, the simulation data, and the experimental data as training samples.

3. The method for optimizing residual stress in a workpiece according to claim 1, characterized in that, Before determining the wall thickness and residual stress data of the target region of the workpiece during the LPBF forming process, the following steps are also included: Finite element analysis is performed on the temperature field and stress field of the workpiece to determine the target region; wherein, the target region includes the thermal stress concentration area and / or the configuration thermal coupling area of ​​the workpiece; the configuration thermal coupling area is the stress state transition area at the sudden change in wall thickness of the workpiece.

4. The method for optimizing residual stress in a workpiece according to claim 1, characterized in that, The compensation process parameters include melting parameters and heat treatment process parameters; Reverse compensation of the workpiece based on the compensation process parameters includes: The workpiece is remelted based on the melting parameters; The molten workpiece is heat-treated based on the aforementioned heat treatment process parameters.

5. The method for optimizing residual stress in a workpiece according to claim 4, characterized in that, The heat treatment of the molten workpiece based on the aforementioned heat treatment process parameters includes: The workpiece was placed in a box-type resistance furnace and kept at 900°C for 2 hours, and then air-cooled with the furnace.

6. The method for optimizing residual stress in a workpiece according to any one of claims 1-5, characterized in that, After the step of performing reverse compensation on the workpiece based on the compensation process parameters, the method further includes: in response to the residual stress data not conforming to the manufacturing standard, returning to the step of determining compensation process parameters that match the wall thickness and residual stress data.

7. A residual stress optimization system for a workpiece, characterized in that, include: The first determining module is used to determine the wall thickness and residual stress data of the target area of ​​the workpiece during the LPBF forming process. The second determining module is used to determine the compensation process parameters that match the wall thickness and residual stress data according to the compensation model; wherein, the compensation model characterizes the stress distribution law of workpieces with different wall thicknesses under different process parameters; The compensation module is used to perform reverse compensation on the workpiece based on the compensation process parameters.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and for running on the processor, characterized in that, When the processor executes the computer program, it implements the residual stress optimization method for the workpiece according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the residual stress optimization method for the workpiece according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the residual stress optimization method for the workpiece as described in any one of claims 1 to 6.

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