Multi-physics field coupling simulation method and device, equipment and storage medium

Abstract

The invention discloses a multi-physics field coupling simulation method and device, equipment and a storage medium. The invention provides a multi-physics field coupling simulation method and device, equipment and a storage medium. The method comprises the steps that a simulation input data set is acquired; performing simulation operation preparation according to the simulation input data set to obtain a simulation input result; and performing simulation optimization on the simulation input result based on the multi-physics field coupling model to obtain an equipment optimization result. According to the method, the simulation input data is subjected to simulation optimization through the multi-physics field coupling model, so that the corresponding equipment optimization result is obtained, the research and development period of the equipment is shortened, the production cost is reduced, and a core technical support is provided for high-precision, high-efficiency and intelligent development of a metal additive manufacturing process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal additive manufacturing, and particularly relates to a multi-physical field coupling simulation method, device, equipment and storage medium. BACKGROUND

[0002] At present, in the field of metal additive manufacturing, especially in the powder bed fusion technology, process optimization and quality control face complex challenges brought by multi-physical field coupling. The existing simulation method is usually limited to single field or simplified coupling modeling idea, and it is difficult to fully capture the interaction between laser melting, heat conduction, gas flow and powder behavior. Especially the heat management mechanism of the forming cylinder system, the flow field characteristics of the protective gas and its influence on the stability of the molten pool and the transport of spatter have not been systematically integrated in the simulation, resulting in that the current process development largely depends on the trial verification method, which restricts the further improvement of efficiency and accuracy.

[0003] In order to overcome the above limitations, it is urgent to obtain a systematic simulation method capable of fully reflecting the multi-physical field coupling effect, so as to reduce the cost of equipment development, improve the production efficiency, accuracy and intelligent level of the equipment. SUMMARY

[0004] Embodiments of the present application provide a multi-physical field coupling simulation method, device, equipment and storage medium to solve the above technical problems.

[0005] The first aspect of the present application provides a multi-physical field coupling simulation method, comprising: obtaining a simulation input data set; performing simulation operation preparation according to the simulation input data set to obtain simulation input results; performing simulation optimization on the simulation input results based on a multi-physical field coupling model to obtain equipment optimization results.

[0006] In some embodiments, the simulation operation preparation according to the simulation input data set to obtain simulation input results comprises: parsing the simulation input data set to obtain a geometric model data set, a material parameter set and a process parameter set, respectively; performing model construction according to the geometric model data set to obtain a first three-dimensional geometric model; performing parameter setting on the first three-dimensional geometric model according to the material parameter set to obtain a second three-dimensional geometric model; performing parameter setting on the second three-dimensional geometric model according to the process parameter set to obtain the simulation input results.

[0007] In some embodiments, the simulation optimization on the simulation input results based on the multi-physical field coupling model to obtain the equipment optimization results comprises: The process parameter optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a first simulation optimization result; The heat dissipation scheme optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a second simulation optimization result; The substrate design optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a third simulation optimization result; The wind speed setting optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a fourth simulation optimization result; The first simulation optimization result, the second simulation optimization result, the third simulation optimization result, and the fourth simulation optimization result are integrated to obtain the device optimization result.

[0008] In some embodiments, the process parameter optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a first simulation optimization result, including: The heat conduction simulation is performed on the simulation input result based on the physical field coupling model to obtain a set of thermal simulation calculation results; The stress analysis simulation is performed on the set of thermal simulation calculation results based on the physical field coupling model to obtain a set of stress simulation calculation results; The set of stress simulation calculation results is optimized and screened according to a preset process parameter standard to obtain the first simulation optimization result.

[0009] In some embodiments, the heat dissipation scheme optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a second simulation optimization result, including: The simulation coupling calculation is performed on the simulation input result based on the multi-physical field coupling model to obtain a set of heat dissipation simulation results; The set of heat dissipation simulation results is optimized and screened according to a preset heat dissipation design standard to obtain the second simulation optimization result.

[0010] In some embodiments, the substrate design optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a third simulation optimization result, including: The heat exchange analysis is performed on the simulation input result based on the multi-physical field coupling model to obtain a heat exchange analysis result; The substrate deformation analysis is performed on the simulation input result based on the multi-physical field coupling model to obtain a substrate deformation analysis result; The substrate design optimization is performed according to the heat exchange analysis result and the substrate deformation analysis result to obtain the third simulation optimization result.

[0011] In some embodiments, the wind speed setting optimization is performed on the simulation input result based on the multi-physical field coupling model to obtain a fourth simulation optimization result, including: perform particle motion simulation on the simulation input result based on the multi-physical field coupling model to obtain a particle motion simulation result set; perform optimization analysis on the particle motion simulation result set according to a preset particle wind speed standard to obtain fourth simulation optimization result.

[0012] The second aspect of the present application provides a multi-physical field coupling simulation device, comprising a simulation input module, a simulation preparation module and a simulation optimization module, wherein the simulation input module is used to obtain a simulation input data set; The simulation preparation module is used to perform simulation operation preparation according to the simulation input data set to obtain a simulation input result; The simulation optimization module is used to perform simulation optimization on the simulation input result based on the multi-physical field coupling model to obtain a device optimization result.

[0013] The third aspect of the present application provides an electronic device, comprising a memory, a processor, one or more computer programs stored in the memory, and instructions for implementing the multi-physical field coupling simulation method described above.

[0014] The fourth aspect of the present application provides a computer readable storage medium storing a computer program, wherein the storage medium stores a multi-physical field coupling simulation program, and the multi-physical field coupling simulation program is executed by a processor to implement the steps of the multi-physical field coupling simulation method described above.

[0015] The multi-physical field coupling simulation method, device, equipment and storage medium provided by the present application first obtain a simulation input data set, then perform simulation operation preparation according to the simulation input data set to obtain a simulation input result, and finally perform simulation optimization on the simulation input result based on the multi-physical field coupling model to obtain a device optimization result. The present application performs simulation optimization on the simulation input data through the multi-physical field coupling model to obtain the corresponding device optimization result, thereby shortening the research and development cycle of the device, reducing the production cost, and providing core technical support for the development of metal additive manufacturing process to high precision, high efficiency and intelligentization. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a structural schematic diagram of the multi-physical field coupling simulation device provided by the embodiments of the present application.

[0017] Figure 2 is a flowchart of the multi-physical field coupling simulation method provided by the embodiments of the present application.

[0018] Figure 3 is Figure 2 is a sub-flowchart of step S20 in FIG. 8.

[0019] Figure 4 yes Figure 2 A schematic diagram of the sub-process of step S30.

[0020] Figure 5 yes Figure 4 A schematic diagram of the sub-process of step S31.

[0021] Figure 6 yes Figure 4 A schematic diagram of the sub-process of step S32.

[0022] Figure 7 This is a structural block diagram of the multiphysics coupling simulation device provided in the embodiments of this application.

[0023] Figure 8 This is another structural block diagram of the multiphysics coupling simulation device provided in the embodiments of this application. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0025] Currently, in the field of metal additive manufacturing, especially in powder bed melting technologies represented by selective laser melting (SLM) and electron beam melting (EBM), process optimization and quality control consistently face complex challenges arising from the coupling effects of multiple physical fields. This process involves various physical phenomena, including laser energy absorption, molten pool dynamics, solid-state phase transitions, thermal stress evolution, protective gas flow, and powder-gas interaction. These phenomena exhibit strong nonlinear coupling relationships, collectively forming a complex physical system with cross-scale and multi-mechanism coupling. However, existing simulation methods are typically limited to modeling single fields or simplified coupling approaches, making it difficult to comprehensively capture the transient dynamics during laser melting, the non-equilibrium phase transition mechanisms during heat conduction, the turbulence effects in gas flow, and the momentum-energy exchange behavior between the gas and powder particles.

[0026] Specifically, in terms of thermal management, the interaction between heat conduction, convection cooling, and dynamic thermal behavior of the molten pool in the forming cylinder system has not yet been systematically integrated in simulations. As a key heat dissipation component, the temperature distribution of the forming cylinder directly affects the thermal deformation of the substrate and the residual stress distribution of the parts. However, most current studies still simplify it to an isothermal or adiabatic boundary, ignoring the non-uniform heat accumulation effect in the actual process. Regarding gas-solid coupling, the flow field characteristics of the protective gas have a significant impact on molten pool stability, spatter transport, dust removal, and thermal field distribution. However, existing models often decouple gas flow from thermophysical processes, failing to effectively construct a two-way coupling mechanism between airflow, thermal field, and spatter movement, resulting in an inability to accurately predict the formation patterns of forming defects under different airflow parameters. Furthermore, research on the synergistic optimization between system-level heat dissipation design and process strategies is still lacking. There are complex interactions between multiple parameters such as forming cylinder cooling conditions, protective gas flow rate and temperature, laser power, and scanning strategy. Traditional simulation methods often focus on local optimization within a single process window, failing to construct a multi-objective, multi-constraint synergistic control mechanism at the system level. This means that current process development still heavily relies on "trial and error" experimental verification, which not only leads to long R&D cycles and high material costs, but also makes it difficult to achieve precise control over the robustness of the forming quality, severely restricting the development of metal additive manufacturing technology towards industrialization and large-scale production.

[0027] Therefore, embodiments of this application provide a multiphysics coupling simulation method, apparatus, device, and storage medium. This method first acquires a set of simulation input data; then prepares for simulation calculations based on the set of simulation input data to obtain simulation input results; finally, it optimizes the simulation input results based on a multiphysics coupling model to obtain optimized device results. This application optimizes simulation input data using a multiphysics coupling model to obtain corresponding optimized device results, shortening the device development cycle, reducing production costs, and providing core technical support for the high-precision, high-efficiency, and intelligent development of metal additive manufacturing processes.

[0028] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure according to an embodiment of this application. Figure 1As shown, the electronic device 1000 may include: a processor 1001, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The processor 1001 may be, for example, a Central Processing Unit (CPU). The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0029] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device 1000, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0030] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a multiphysics coupling simulation program.

[0031] Understandable, Figure 1 In the illustrated electronic device 1000, the network interface 1004 is mainly used for data communication with a network server. The user interface 1003 is mainly used for data interaction with the user. In this application, the electronic device 1000 uses the processor 1001 to call the control program stored in the memory 1005 to execute the multiphysics coupling simulation method provided in the embodiments of this application.

[0032] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the multiphysics coupling simulation method provided in an embodiment of this application. In some embodiments, the multiphysics coupling simulation method may be... Figure 1 The electronic device 1000 in the system performs this operation. Specifically, for example... Figure 2 As shown, the multiphysics coupling simulation method includes the following steps: Step S10: Obtain the simulation input data set.

[0033] It is understood that, in some embodiments, acquiring the simulation input data set is a multi-source data integration process of a system. Its purpose is to integrate geometric model data, material parameters, and process parameters through experimental measurements, equipment calibration, and numerical processing, thereby ultimately constructing a high-precision and verifiable input foundation for subsequent multiphysics coupling simulations. Geometric model data includes, but is not limited to, CAD model data of the molding cylinder substrate, powder layer parameters, and gas flow domain division data; material parameters include, but are not limited to, density, specific heat capacity, and thermal conductivity of metallic materials, and viscosity, density, and thermal conductivity of the protective gas; process parameters include, but are not limited to, laser spot morphology data, laser power, laser path data, protective gas flow rate, and protective gas temperature.

[0034] Step S20: Prepare for simulation calculation based on the simulation input data set to obtain the simulation input results.

[0035] It is understood that, in some embodiments, the process of preparing for simulation calculations based on the simulation input data set includes, but is not limited to, first constructing a three-dimensional geometric model containing a forming cylinder, powder bed, and gas flow domain based on geometric model data; then assigning thermal and mechanical parameters to the metallic material as a function of temperature based on material parameters; defining transport characteristic parameters for protective gases, such as argon and nitrogen; finally configuring a moving laser heat source model based on process parameters; and setting flow field boundary conditions based on gas control parameters to obtain a complete simulation model that can be submitted to the solver for calculation, i.e., the simulation input result. The transport characteristic parameters of the protective gas include, but are not limited to, the viscosity, density, and thermal conductivity of the protective gas. The gas control parameters include, but are not limited to, gas flow rate, temperature, and flow direction.

[0036] Please refer to this as well. Figure 3 , Figure 3 yes Figure 2 A schematic diagram of the sub-process of step S20. In some embodiments, simulation input results can be obtained based on steps S21 to S24.

[0037] Step S21: Analyze the simulation input data set to obtain the geometric model data set, material parameter set, and process parameter set, respectively.

[0038] Understandably, in some embodiments, the geometric model dataset is used to define the physical carrier and computational domain for subsequent simulations, thereby determining the objects and boundaries of thermal effects. The material parameter set is used to describe the constitutive response of the material constituting the geometric entity under different physical fields, thereby determining the intrinsic mechanisms of heat transfer, stress evolution, and molten pool behavior. The process parameter set serves as the external input energy and operational commands driving the simulation process, controlling the execution mode of the manufacturing process by setting laser characteristics, scanning paths, and environmental conditions.

[0039] Step S22: Construct the model based on the geometric model data set to obtain the first three-dimensional geometric model.

[0040] It is understood that, in some embodiments, the process of building a model based on a geometric model dataset includes, but is not limited to, first importing the CAD model data of the molding cylinder substrate into the simulation platform and checking and repairing it to ensure its sealing, then extracting the gas-solid interaction domain based on the gas flow domain and generating the powder bed geometric representation based on the powder layer parameters, while integrating the substrate, parts and gas domain through multi-body assembly, and naming each region to distinguish material properties and boundary conditions, and finally using a local densification strategy to generate a high-quality computational mesh, and configuring the cell birth and death model to simulate the dynamic evolution of materials, thereby completing a discretized computational model that can be used for multi-physics coupling simulation, namely the first three-dimensional geometric model.

[0041] Step S23: Set the parameters of the first three-dimensional geometric model according to the material parameter set to obtain the second three-dimensional geometric model.

[0042] It is understood that, in some embodiments, the process of setting parameters for the first three-dimensional geometric model based on the set of material parameters involves assigning various types of data from the set of material parameters to the corresponding domains in the first three-dimensional geometric model. For example, the density, specific heat capacity, and thermal conductivity of the metal material are assigned to the solid part region in the first three-dimensional geometric model to bind the properties of the metal material; the viscosity, density, and thermal conductivity of the protective gas are assigned to the gas flow domain in the first three-dimensional geometric model to bind the properties of the protective gas and activate the compressible flow model, etc.

[0043] Step S24: Set the parameters of the second three-dimensional geometric model according to the set of process parameters to obtain the simulation input results.

[0044] It is understood that, in some embodiments, setting parameters for the second three-dimensional geometric model based on the set of process parameters involves mapping the processing instructions and energy input methods contained in the set of process parameters to the corresponding regions of the second three-dimensional geometric model. This process includes, but is not limited to, first converting the laser spot shape and power data in the set of process parameters into a heat source energy distribution model and mapping it onto the powder bed surface, then driving the heat source to move along the actual trajectory based on the laser path data, and assigning the protective gas flow rate and temperature parameters to the flow domain inlet boundary to define the velocity inlet and heat exchange conditions, thereby ultimately constructing a digital twin model that fully reflects the dynamic manufacturing process, i.e., obtaining the simulation input results.

[0045] Step S30: Optimize the simulation input results based on the multiphysics coupling model to obtain the equipment optimization results.

[0046] It is understood that, in some embodiments, a multiphysics coupling model is a computational framework for describing and simulating complex interactions between multiple physical phenomena. It can achieve holistic prediction and analysis of the behavior of complex systems by integrating the governing equations of different physical fields and establishing constitutive relations and boundary condition coupling mechanisms between these equations. The simulation subsystems integrated into the multiphysics coupling model include, but are not limited to, thermal simulation systems, fluid simulation systems, powder simulation systems, and structural stress simulation systems.

[0047] Please refer to this as well. Figure 4 , Figure 4 yes Figure 2 A schematic diagram of the sub-process of step S30. In some embodiments, the equipment optimization result can be obtained based on the following steps S31 to S35.

[0048] Step S31: Optimize the process parameters based on the simulation input results using a multiphysics coupling model to obtain the first simulation optimization result.

[0049] It is understood that in some embodiments, the process of optimizing process parameters based on the simulation input results using a multiphysics coupling model is achieved through a thermal-structural coupling calculation framework formed by a thermal simulation system and a result stress simulation system. This includes, but is not limited to, first performing heat conduction calculations on the process of melting powder using a Gaussian moving heat source, and then combining this with thermal stress analysis of the powder bed melting channel to finally determine suitable process parameters, such as optimized laser power and scanning speed. By integrating these optimized process parameters, the first simulation optimization result can be obtained.

[0050] Please refer to this as well. Figure 5 , Figure 5 yes Figure 4 A schematic diagram of the sub-process of step S31. In some embodiments, the first simulation optimization result can be obtained based on steps S311 to S313.

[0051] Step S311: Perform heat conduction simulation on the simulation input results based on the physical field coupling model to obtain a set of thermal simulation calculation results.

[0052] It is understood that in some embodiments, the process of performing heat conduction simulation on the simulation input results based on the physical field coupling model includes, but is not limited to, first driving the model with a moving Gaussian heat source, then using dynamic adaptive mesh technology to efficiently capture the physical field changes in key areas, thereby outputting key thermophysical quantities such as molten pool morphology, temperature gradient, and cooling rate in real time, and finally performing transient thermal analysis calculations to obtain a high-precision temperature field history, that is, obtaining the corresponding set of thermal simulation calculation results.

[0053] Step S312: Perform stress analysis simulation on the thermal simulation calculation result set based on the physical field coupling model to obtain the stress simulation calculation result set.

[0054] It is understood that, in some embodiments, the process of performing stress analysis simulation on the set of thermal simulation calculation results based on the physical field coupling model includes, but is not limited to, first using the temperature field in the set of thermal simulation calculation results as the core load to drive structural analysis, and then using the elastoplastic constitutive model and fixed substrate boundary conditions to solve the thermal strain driven by the coefficient of thermal expansion and the stress-strain field it causes, and finally accurately characterizing the residual stress distribution of the tensile stress at the center of the melt channel, the compressive stress on both sides, as well as the plastic deformation and warping trend, thereby obtaining the corresponding set of stress simulation calculation results.

[0055] Step S313: Optimize and filter the set of stress simulation calculation results according to the preset process parameter standards to obtain the first simulation optimization result.

[0056] It is understood that in some embodiments, preset process parameter standards are a set of quantitative technical indicators and constraints set by the user or the system, used to select and determine suitable process parameters from the set of simulation calculation results, such as laser power and laser scanning speed. These preset process parameter standards include, but are not limited to, quality-related, laser process-related, efficiency-related, and multi-objective trade-off parameter standards. For example, quality-related parameter standards may be set as residual stress ≤ 80% of material yield strength and deformation ≤ 50% of layer thickness; laser process-related parameter standards may be set as the equipment range for laser power / scanning speed and a volumetric energy density window of 30-60 J / mm³, etc.

[0057] Step S32: Optimize the heat dissipation scheme based on the simulation input results using the multiphysics coupling model to obtain the second simulation optimization result.

[0058] It is understood that, in some embodiments, the process of optimizing the heat dissipation scheme based on the simulation input results using a multiphysics coupling model is achieved through a thermal-fluid coupling computational framework formed by a thermal simulation system and a fluid simulation system. This includes, but is not limited to, first defining a multi-objective optimization function based on the simulation input results, with the maximum temperature, system pressure drop, and heat dissipation efficiency as the core, and parameterizing the geometry and manifold variables; then constructing a conjugate heat transfer model; accurately analyzing the fluid-solid interface heat transfer using automatic boundary layer mesh technology; then using experimental design methods to generate a sample space and perform batch coupled simulations to extract thermal-fluid response data; and then using a surrogate model, such as the response surface methodology, to drive a multi-objective optimization algorithm to solve the problem and obtain the second simulation optimization result.

[0059] Please refer to this as well. Figure 6 , Figure 6 yes Figure 4A schematic diagram of the sub-process of step S32. In some embodiments, a second simulation optimization result can be obtained based on steps S321 to S322.

[0060] Step S321: Perform simulation coupling calculations on the simulation input results based on the multiphysics coupling model to obtain a set of heat dissipation simulation results.

[0061] It is understood that, in some embodiments, the process of performing simulation coupling calculations on the simulation input results based on the multiphysics coupling model is achieved by performing batch coupling simulation solutions on the sample space generated by the experimental design method, and the final set of solution results is the set of heat dissipation simulation results.

[0062] Step S322: Optimize and filter the heat dissipation simulation result set according to the preset heat dissipation design standard to obtain the second simulation optimization result.

[0063] It is understood that in some embodiments, the preset heat dissipation design standard is a set of quantitative indicators that integrate thermodynamics, fluid mechanics and reliability requirements set by the user or system. It can construct a comprehensive evaluation specification that takes into account heat dissipation performance, system energy consumption and space feasibility by setting key temperature thresholds, such as junction temperature ≤125℃, flow resistance tolerance, such as pressure drop ≤200Pa, geometric constraints, such as heat sink size / weight boundaries and multi-objective weights, such as temperature-pressure drop-cost weighted functions.

[0064] Step S33: Optimize the substrate design based on the simulation input results using the multiphysics coupling model to obtain the third simulation optimization result.

[0065] It is understood that, in some embodiments, the process of optimizing the substrate design based on the simulation input results using a multiphysics coupling model is achieved through a thermal-fluid-structure coupled computational framework formed by a thermal simulation system, a fluid simulation system, and a structural simulation system. This includes, but is not limited to, firstly, accurately calculating the temperature field and heat dissipation efficiency of the substrate under the action of a laser heat source and protective gas flow by coupling the thermal simulation system and the fluid simulation system, and then transferring the temperature field as a thermal load to the structural simulation system to calculate thermal stress and deformation. Next, parametric design and experimental design methods are used to explore the design space of variables such as substrate thickness, material, and geometric layout. Then, multi-objective optimization algorithms are used to simultaneously optimize thermal performance (temperature uniformity), structural performance (deformation and stress), and lightweight objectives. Finally, simulation and experimental verification are used to iteratively converge to the optimal solution, and the third simulation optimization result is obtained.

[0066] Step S34: Optimize the wind speed setting based on the simulation input results using the multiphysics coupling model to obtain the fourth simulation optimization result.

[0067] It is understood that in some embodiments, the process of optimizing the wind speed setting based on the multiphysics coupling model of the simulation input results is achieved through the fluid-discrete element coupling calculation framework formed by the fluid simulation system and the powder simulation system. This includes, but is not limited to, first parameterizing the wind speed variable in the simulation input results and running transient simulations in batches, then quantitatively extracting response indicators such as splash removal rate, powder bed disturbance degree and flow field uniformity under different wind speeds, and then balancing removal efficiency and process stability based on a multi-objective optimization algorithm, and finally determining a robust wind speed window that can simultaneously achieve high removal rate and low powder disturbance, which is the fourth simulation optimization result.

[0068] Step S35: Integrate the first simulation optimization result, the second simulation optimization result, the third simulation optimization result, and the fourth simulation optimization result to obtain the equipment optimization result.

[0069] It is understood that, in some embodiments, the equipment optimization result is a set of optimal process parameters and system configuration schemes that have been verified by multiphysics, which can determine process parameters, heat dissipation schemes, substrate design and spatter removal airflow.

[0070] Furthermore, the substrate design is optimized based on the simulation input results using a multiphysics coupling model to obtain a third simulation optimization result, including: performing heat transfer analysis on the simulation input results using a multiphysics coupling model to obtain heat transfer analysis results; performing substrate deformation analysis on the simulation input results using a multiphysics coupling model to obtain substrate deformation analysis results; and optimizing the substrate design based on the heat transfer analysis results and substrate deformation analysis results to obtain the third simulation optimization result.

[0071] It is understood that, in some embodiments, the heat transfer analysis process includes, but is not limited to, calculating the temperature field distribution, convective heat transfer coefficient, and heat dissipation efficiency of the substrate under the action of the laser heat source and protective gas using a heat-fluid coupling model, evaluating its thermal management performance, and identifying weak heat dissipation areas, such as high-temperature accumulation areas, thereby obtaining heat transfer analysis results. The substrate deformation analysis process includes, but is not limited to, using a heat-structure coupling model, applying the temperature field obtained from the heat transfer analysis as a thermal load to the structural field, calculating the thermal stress distribution and deformation morphology of the substrate, such as warpage, analyzing its mechanical reliability and deformation mechanism, thereby obtaining board deformation analysis results. The substrate design optimization process includes, but is not limited to, combining the heat transfer analysis results and the substrate deformation analysis results, using multi-objective optimization methods, such as parametric scanning and response surface methodology, to adjust the substrate design variables, thereby ultimately obtaining the optimal substrate design scheme that combines efficient heat dissipation and high structural stability, i.e., the third simulation optimization result. The substrate design variables include, but are not limited to, substrate thickness, material, stiffener layout, and flow channel design.

[0072] Furthermore, the simulation input results are optimized based on the multiphysics coupling model to obtain the fourth simulation optimization result, which includes: performing particle motion simulation on the simulation input results based on the multiphysics coupling model to obtain a set of particle motion simulation results; and performing optimization analysis on the set of particle motion simulation results according to the preset particle wind speed standard to obtain the fourth simulation optimization result.

[0073] It is understood that in some embodiments, the particle motion simulation process includes, but is not limited to, performing simulations using a fluid-discrete element coupled computational framework to calculate particle motion trajectories, distribution uniformity, and conveying efficiency under different wind speeds, thereby obtaining a set of particle motion simulation results. The optimization analysis process includes, but is not limited to, performing multi-objective optimization analysis on the set of particle motion simulation results based on a preset particle wind speed standard. By balancing efficiency, quality, and energy consumption indicators, the optimal wind speed window that meets all process requirements is selected, such as 17-19 m / s, thus obtaining the fourth simulation optimization result. The preset particle wind speed standard is a performance quantification indicator set by the user or the system, for example, it can be set to conveying efficiency ≥95% and distribution uniformity ≥0.9.

[0074] Reference Figure 7 , Figure 7 This is a structural block diagram of the multiphysics coupling simulation device provided in the embodiments of this application. Figure 7 As shown, the multiphysics coupling simulation device includes a simulation input module 10, a simulation preparation module 20, and a simulation optimization module 30. The simulation input module 10 is used to acquire a set of simulation input data. The simulation preparation module 20 is used to prepare for simulation calculations based on the simulation input data set to obtain simulation input results. The simulation optimization module 30 is used to optimize the simulation input results based on the multiphysics coupling model to obtain optimized device results.

[0075] This embodiment provides a multiphysics coupling simulation method, apparatus, device, and storage medium. It first acquires a set of simulation input data; then, it prepares for simulation calculations based on the simulation input data set to obtain simulation input results; finally, it optimizes the simulation input results based on a multiphysics coupling model to obtain optimized equipment results. This embodiment optimizes the simulation input data using a multiphysics coupling model to obtain corresponding optimized equipment results, fundamentally solving the pain point of traditional process development relying on experience-based trial and error. It significantly shortens the R&D cycle, reduces production costs, and provides core technical support for the development of metal additive manufacturing processes towards high precision, high efficiency, and intelligence.

[0076] In addition, refer to Figure 8 , Figure 8This is another structural block diagram of the multiphysics coupling simulation device provided in this application embodiment. The multiphysics coupling simulation device 2000 includes a processor 1001 and a memory 1005. The memory 1005 is used to store programs, instructions, or code for executing the aforementioned multiphysics coupling simulation method. The processor 1001 is used to execute the programs, instructions, or code stored in the memory 1005. The programs, instructions, or code stored in the memory 1005 are executable. Figures 2 to 6 The embodiments shown include some or all of the steps of the multiphysics coupling simulation method.

[0077] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0078] Furthermore, embodiments of this application also propose a computer-readable storage medium storing a computer program, wherein the storage medium stores a multiphysics coupling simulation program, and when the multiphysics coupling simulation program is executed by a processor, it implements the steps of the multiphysics coupling simulation method described above.

[0079] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0080] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0081] In addition, for technical details not described in detail in this embodiment, please refer to the multiphysics coupling simulation method provided in any embodiment of this application, which will not be repeated here.

[0082] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0083] The sequence numbers of the embodiments in this application are for description only and do not represent the superiority or inferiority of the embodiments.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0085] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A multiphysics coupling simulation method, characterized in that, The method includes: Obtain the simulation input data set; The simulation input data set is used to prepare for simulation calculations, and the simulation input results are obtained. The simulation input results are optimized based on a multiphysics coupling model to obtain the equipment optimization results.

2. The multiphysics coupling simulation method according to claim 1, characterized in that, The step of preparing for simulation calculations based on the simulation input data to obtain simulation input results includes: The simulation input data set is parsed to obtain the geometric model data set, the material parameter set, and the process parameter set, respectively; A first three-dimensional geometric model is obtained by constructing a model based on the geometric model data set. The first three-dimensional geometric model is parameterized according to the set of material parameters to obtain the second three-dimensional geometric model. The second three-dimensional geometric model is parameterized according to the set of process parameters to obtain the simulation input results.

3. The multiphysics coupling simulation method according to any one of claims 1 to 2, characterized in that, The simulation optimization of the simulation input results based on the multiphysics coupling model yields the equipment optimization results, including: Based on the multiphysics coupling model, the simulation input results are optimized for process parameters to obtain the first simulation optimization result; The heat dissipation scheme is optimized based on the multiphysics coupling model to obtain the second simulation optimization result. The substrate design is optimized based on the simulation input results using a multiphysics coupling model, resulting in a third simulation optimization result. Based on the multiphysics coupling model, the simulation input results are optimized by setting the wind speed to obtain the fourth simulation optimization result; The first simulation optimization result, the second simulation optimization result, the third simulation optimization result, and the fourth simulation optimization result are integrated to obtain the equipment optimization result.

4. The multiphysics coupling simulation method according to claim 3, characterized in that, The process parameter optimization based on the simulation input results using a multiphysics coupling model yields a first simulation optimization result, including: Based on the physical field coupling model, heat conduction simulation is performed on the simulation input results to obtain a set of thermal simulation calculation results. Based on the physical field coupling model, stress analysis simulation is performed on the thermal simulation result set to obtain the stress simulation result set. The stress simulation calculation result set is optimized and filtered according to the preset process parameter standard to obtain the first simulation optimization result.

5. The multiphysics coupling simulation method according to claim 3, characterized in that, The heat dissipation scheme is optimized based on the multiphysics coupling model of the simulation input results to obtain a second simulation optimization result, including: Based on the multiphysics coupling model, the simulation input results are subjected to simulation coupling calculations to obtain a set of heat dissipation simulation results. The heat dissipation simulation result set is optimized and filtered according to the preset heat dissipation design standard to obtain the second simulation optimization result.

6. The multiphysics coupling simulation method according to claim 3, characterized in that, The substrate design optimization based on the simulation input results using a multiphysics coupling model yields a third simulation optimization result, including: Based on the multiphysics coupling model, heat transfer analysis is performed on the simulation input results to obtain heat transfer analysis results; Based on the multiphysics coupling model, the simulation input results are analyzed to obtain the substrate deformation analysis results. Based on the heat transfer analysis results and the substrate deformation analysis results, the substrate design is optimized to obtain the third simulation optimization result.

7. The multiphysics coupling simulation method according to claim 3, characterized in that, The wind speed setting is optimized based on the multiphysics coupling model of the simulation input results to obtain a fourth simulation optimization result, including: Based on the multiphysics coupling model, particle motion simulation is performed on the simulation input results to obtain a set of particle motion simulation results. The set of particle motion simulation results is optimized and analyzed based on the preset particle wind speed standard to obtain the fourth simulation optimization result.

8. A multiphysics coupling simulation device, characterized in that, The device includes: Simulation input module: Acquires the simulation input data set; Simulation preparation module: Prepares for simulation calculations based on the simulation input data set to obtain simulation input results; Simulation optimization module: Based on the multiphysics coupling model, the simulation input results are optimized to obtain the equipment optimization results.

9. An electronic device, characterized in that, The electronic device includes: a memory; a processor; and one or more computer programs stored in the memory, the one or more computer programs including instructions that, when executed by the processor, enable the implementation of the multiphysics coupling simulation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes instructions that, when executed by a processor, enable the implementation of the multiphysics coupling simulation method as described in any one of claims 1 to 7.

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