Additive manufacturing process parameter optimization method and related device based on deformation analysis

Abstract

The present invention discloses a method and related device for optimizing additive manufacturing process parameters based on deformation analysis, relating to the field of additive manufacturing technology. The method comprises: constructing a thermo-solid coupling model based on the heterogeneous material parameters, structural characteristic parameters, and preset processing parameters of a target workpiece; constructing a melt channel model based on the preset processing parameters and structural characteristic parameters to generate a structural mesh of the target workpiece; performing temperature field distribution analysis based on the structural mesh in combination with a heat source model; performing thermodynamic simulation of the deposition process based on the structural mesh and combining it with the thermo-solid coupling model to perform elastic-plastic strain analysis; performing stress-deformation analysis of the workpiece during laser additive manufacturing based on elastic-plastic strain components and temperature field distribution data; and optimizing preset processing parameters based on the stress-deformation data in combination with a multi-objective function to perform laser additive manufacturing of the workpiece. The present invention improves the reliability of processing parameter optimization and effectively enhances the quality of laser additive manufacturing of the workpiece.

Description

Technical Field

[0001] The present invention relates to the field of additive manufacturing technology, and in particular to a method for optimizing additive manufacturing process parameters based on deformation analysis and related devices. Background Art

[0002] Laser additive manufacturing (LAM) is a method for manufacturing workpieces that uses a high-energy laser beam as an energy source to deposit materials layer by layer. Because LAM offers significant advantages over traditional material forming methods for manufacturing complex workpieces, it is increasingly being used in industrial manufacturing. Heterogeneous materials, structures composed of materials with different physical and chemical properties, have gradually been applied in additive manufacturing due to their diverse physical and chemical properties. During the LAM process using heterogeneous materials, the rapid and intense heating and cooling cycles can induce stress and deformation, resulting in defects and even distortion in the workpiece structure. Therefore, LAM stress and deformation analysis is often performed in advance to optimize processing parameters. Currently, finite element models and point heat source models are commonly used for stress and deformation analysis of workpieces during LAM. However, this approach requires extensive iterative calculations and is not very accurate for stress and deformation analysis of complex or large workpieces. This makes it difficult to optimize processing parameters and, consequently, fails to effectively improve the LAM quality of the workpiece. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology. The present invention provides an additive manufacturing process parameter optimization method based on deformation analysis and related devices, which improves the reliability of processing process parameter optimization and effectively improves the laser additive manufacturing quality of workpieces.

[0004] In order to solve the above technical problems, the present invention provides a method for optimizing additive manufacturing process parameters based on deformation analysis, the method comprising:

[0005] Construct a thermo-mechanical coupling model based on the target workpiece's heterogeneous material parameters, structural feature parameters, and preset machining process parameters;

[0006] A melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of a target workpiece is generated based on the melt channel model;

[0007] Perform temperature field distribution analysis based on the structural mesh combined with the heat source model to obtain temperature field distribution data;

[0008] Perform thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and perform elastic-plastic strain analysis based on the thermodynamic simulation data and the thermo-solid coupling model to obtain the elastic-plastic strain components;

[0009] Based on the elastic-plastic strain components and temperature field distribution data, stress and deformation analysis of the workpiece laser additive manufacturing is performed to obtain stress and deformation data;

[0010] The preset processing parameters are optimized based on stress and deformation data combined with multi-objective functions to obtain optimized processing parameters, and laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters.

[0011] Optionally, the step of constructing a thermo-mechanical coupling model based on heterogeneous material parameters, structural feature parameters, and preset processing parameters of the target workpiece includes:

[0012] Constructing a geometric numerical model of the target workpiece based on the structural characteristic parameters, and setting heterogeneous material parameters for the geometric numerical model to obtain a first numerical model;

[0013] Based on the preset processing parameters, the heating and cooling analysis steps and the thermal and mechanical boundary conditions are set for the first numerical model to obtain a thermal-solid coupling model.

[0014] Optionally, the step of constructing a melt channel model based on preset processing parameters and structural feature parameters, and generating a structural mesh of a target workpiece based on the melt channel model, includes:

[0015] Determine melt path data and deposition data based on preset machining process parameters, and generate a sliced ​​two-dimensional model of the target workpiece based on structural feature parameters;

[0016] Construct a melt channel model based on melt channel data and sedimentation data combined with a slice 2D model;

[0017] Constructing a boundary curve based on the structural characteristic parameters, and dividing the melt channel model into model grids based on the boundary curve and the structural characteristic parameters to obtain a plurality of model grids;

[0018] Based on the melt data and deposition data, several model meshes are spliced ​​to obtain the structural mesh of the target workpiece.

[0019] Optionally, performing temperature field distribution analysis based on the structural grid in combination with a heat source model to obtain temperature field distribution data includes:

[0020] Generate molten pool morphology data based on the structural mesh and preset processing parameters;

[0021] Matching corresponding heat source model parameters based on the molten pool morphology data, and constructing a heat source model based on the heat source model parameters;

[0022] Determine the time step of the heat source model, perform temperature field distribution analysis based on the time step of the heat source model, and obtain temperature field distribution data.

[0023] Optionally, performing a thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and performing elastic-plastic strain analysis based on the thermodynamic simulation data and a thermo-solid coupling model to obtain elastic-plastic strain components, includes:

[0024] Determine the deposition simulation thermal conductivity based on the heterogeneous material parameters, and perform thermodynamic simulation of the deposition process based on the deposition simulation thermal conductivity and the structural mesh combined with the birth-death cell technology to obtain thermodynamic simulation data;

[0025] Determine the plastic strain curve based on the thermodynamic simulation data, and perform elastic strain curve analysis based on the thermo-solid coupling model to obtain the elastic strain curve;

[0026] Elastic-plastic strain analysis is performed based on the plastic strain curve and the elastic strain curve to obtain the elastic-plastic strain components.

[0027] Optionally, performing stress and deformation analysis of the workpiece during laser additive manufacturing based on the elastic-plastic strain component and the temperature field distribution data to obtain stress and deformation data includes:

[0028] Determine the inherent strain based on the elastic-plastic strain component, and determine the stress and deformation of the workpiece during laser additive manufacturing using a static analysis model based on the inherent strain;

[0029] Based on the three-dimensional model data, temperature field data, and stress and deformation data of the additive manufacturing sample, a classification regression tree is trained in combination with a depth-limited leaf-by-leaf growth algorithm to obtain a trained classification regression tree, which is used as a stress-deformation analysis model.

[0030] Based on the temperature field distribution data, the stress and deformation distribution of the workpiece during laser additive manufacturing is analyzed using a stress and deformation analysis model to obtain stress and deformation distribution data;

[0031] The stress and deformation data of the workpiece laser additive manufacturing are determined based on the stress and deformation distribution data, stress amount and deformation amount.

[0032] Optionally, optimizing preset processing parameters based on stress-strain data in combination with a multi-objective function to obtain optimized processing parameters includes:

[0033] Based on orthogonal process experiments, sample deposition efficiency analysis, sample tensile strength analysis and sample elongation analysis with different processing parameters were carried out to obtain sample deposition efficiency data, sample tensile strength data and sample elongation data;

[0034] Determine a deposition efficiency objective function based on the sample deposition efficiency data, determine a tensile strength objective function based on the sample tensile strength data, and determine an elongation objective function based on the sample elongation data;

[0035] Based on the stress-deformation data and the deposition efficiency objective function, the tensile strength objective function and the elongation objective function, the preset processing parameters are optimized to obtain the optimized processing parameters.

[0036] In addition, the present invention also provides an additive manufacturing process parameter optimization device based on deformation analysis, the device comprising:

[0037] Thermo-mechanical coupling model module: used to build a thermo-mechanical coupling model based on the heterogeneous material parameters, structural feature parameters and preset processing parameters of the target workpiece;

[0038] Structural mesh module: used to build a melt channel model based on preset processing parameters and structural feature parameters, and generate a structural mesh of the target workpiece based on the melt channel model;

[0039] Temperature field analysis module: used to perform temperature field distribution analysis based on the structural grid combined with the heat source model to obtain temperature field distribution data;

[0040] Strain analysis module: used to perform thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and to perform elastic-plastic strain analysis based on the thermodynamic simulation data and the thermo-solid coupling model to obtain the elastic-plastic strain components;

[0041] Stress and deformation analysis module: used to perform stress and deformation analysis of workpiece laser additive manufacturing based on elastic-plastic strain components and temperature field distribution data to obtain stress and deformation data;

[0042] Laser additive manufacturing module: used to optimize preset processing parameters based on stress and deformation data combined with multi-objective functions, obtain optimized processing parameters, and perform laser additive manufacturing of target workpieces based on the optimized processing parameters.

[0043] In addition, the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory is used to store instructions, and the processor is used to call the instructions in the memory so that the electronic device executes the above-mentioned additive manufacturing process parameter optimization method based on deformation analysis.

[0044] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned method for optimizing additive manufacturing process parameters based on deformation analysis.

[0045] In an embodiment of the present invention, a melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of the target workpiece is generated based on the melt channel model. This improves the accuracy of structural mesh generation, provides more accurate data for stress-deformation analysis, and avoids excessive time consumption in subsequent analysis processes. Temperature field distribution analysis is performed based on the structural mesh in combination with a heat source model, making the obtained temperature field distribution data more reliable. A thermodynamic simulation of the deposition process is performed based on the structural mesh to obtain thermodynamic simulation data. Using the more accurate thermodynamic simulation data, an elastic-plastic strain analysis is performed using a thermo-solid coupling model, making the obtained elastic-plastic strain data more accurate to the actual situation. Stress-deformation analysis of the workpiece during laser additive manufacturing is performed based on the elastic-plastic strain components and temperature field distribution data, effectively improving the accuracy of stress-deformation analysis and meeting the stress-deformation analysis requirements for more complex or larger workpieces. Preset processing parameters are optimized based on the stress-deformation data and combined with a multi-objective function to obtain optimized processing parameters. Laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters, improving the reliability of process parameter optimization and effectively improving the quality of laser additive manufacturing of the workpiece. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 1 is a flow chart of a method for optimizing additive manufacturing process parameters based on deformation analysis in an embodiment of the present invention;

[0048] Figure 2 is a schematic flow chart of a method for optimizing additive manufacturing process parameters based on deformation analysis in another embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the structure of an additive manufacturing process parameter optimization device based on deformation analysis in an embodiment of the present invention;

[0050] Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] Example 1

[0053] See also Figure 1 , Figure 1 : is a flow chart of a method for optimizing additive manufacturing process parameters based on deformation analysis in an embodiment of the present invention, the method comprising:

[0054] S11: Construct a thermo-mechanical coupling model based on the target workpiece’s heterogeneous material parameters, structural characteristic parameters, and preset machining process parameters;

[0055] In the specific implementation process of the present invention, the thermo-solid coupling model is constructed based on the heterogeneous material parameters, structural feature parameters and preset processing parameters of the target workpiece, including: constructing a geometric numerical model of the target workpiece based on the structural feature parameters, and setting the heterogeneous material parameters for the geometric numerical model to obtain a first numerical model; setting heating and cooling analysis steps and setting thermal and mechanical boundary conditions for the first numerical model based on the preset processing parameters to obtain a thermo-solid coupling model.

[0056] Specifically, the target workpiece can be a key transmission basic functional component such as gears and shafts of offshore wind power equipment in the new energy field, a heterogeneous material such as a combination of high-strength and tough medium-entropy alloy and wear-resistant and corrosion-resistant martensitic stainless steel powder, heterogeneous material parameters such as microstructure, composition parameters, thermal performance parameters and mechanical performance parameters, structural feature parameters such as the forming size and geometric structure of the target workpiece, and preset processing parameters such as laser power, scanning speed, scanning path, etc. A geometric numerical model of the target workpiece is constructed based on the structural feature parameters, and the forming size and geometric structure in the structural feature parameters are input into the numerical model software to obtain the geometric numerical model of the target workpiece. The heterogeneous material parameters are set for the geometric numerical model, that is, the thermal and mechanical material performance parameters are set for the geometric numerical model to obtain a first numerical model. Based on the preset processing parameters, the first numerical model is set up with heating and cooling analysis steps and thermal and mechanical boundary conditions. The analysis step is a step in the overall finite element simulation process, which includes the settings and output requirements of the model at a certain stage. The analysis step defines the loading method, boundary conditions, solution type, etc. of the model, and is an important part of the simulation process. The heating analysis step and the cooling analysis step are the heating simulation process and the cooling simulation process of the model during the laser additive manufacturing process. The thermal and mechanical boundary conditions are set according to the forming size and geometric structure of the target workpiece, that is, the limits of the heat treatment and mechanical treatment of the preset processing parameters in the laser additive manufacturing process are set to obtain the thermo-solid coupling model. The thermo-solid coupling model is a mathematical model used to describe the interaction between thermal and mechanical fields. The model is based on the basic principles of heat conduction and mechanics, and takes into account the influence of temperature changes on the mechanical properties of materials and the reaction of deformation to heat transfer. The thermo-solid coupling model can better analyze strain data.

[0057] S12: constructing a melt channel model based on preset processing parameters and structural feature parameters, and generating a structural mesh of the target workpiece based on the melt channel model;

[0058] In the specific implementation process of the present invention, the melt model is constructed based on the preset processing parameters and structural feature parameters, and the structural mesh of the target workpiece is generated based on the melt model, including: determining the melt data and deposition data based on the preset processing parameters, and generating a sliced ​​two-dimensional model of the target workpiece based on the structural feature parameters; constructing the melt model based on the melt data and deposition data combined with the sliced ​​two-dimensional model; constructing a boundary curve based on the structural feature parameters, dividing the melt model into a model mesh based on the boundary curve and the structural feature parameters to obtain a plurality of model meshes; and splicing the plurality of model meshes based on the melt data and deposition data to obtain the structural mesh of the target workpiece.

[0059] Specifically, melt path data and deposition data are determined based on preset processing parameters. The melt path is the cladding layer formed by a laser beam melting material on a substrate along a certain scanning path and filling a given two-dimensional contour. The melt path data and deposition data can be determined by preset processing parameters. The melt path data includes melt path thickness and melt path width, and the deposition data is the deposition path. A sliced ​​two-dimensional model of the target workpiece is generated based on the structural feature parameters. A three-dimensional model of the target workpiece is generated based on the structural feature parameters. The three-dimensional model is sliced ​​along the height according to the melt path thickness to obtain a corresponding slice model. The slice model is projected to obtain a sliced ​​two-dimensional model. A melt path model is constructed based on the melt path data and deposition data combined with the sliced ​​two-dimensional model. Based on the melt path width and deposition path, the sliced ​​two-dimensional model is determined to include a melt path model with the same structure, that is, a melt path model with a deposition path with the same path characteristics. A boundary curve is constructed based on the structural characteristic parameters, that is, the boundary curve of the grid division is determined according to the forming size and geometric structure in the structural characteristic parameters, and the melt channel model is grid-divided based on the boundary curve and the structural characteristic parameters. The melt channel model is divided into several sub-models of characteristic structures according to the boundary curve. Each sub-model contains only one characteristic structure, and the characteristic structures include straight line structure, arc structure and corner structure. If the characteristic structure is a straight line structure, the sub-model is grid-divided according to the starting point and end point of the sub-model corresponding to the straight line structure combined with the preset grid width. If the characteristic structure is an arc structure, the sub-model is grid-divided according to the preset expansion direction and preset angle with the center point of the sub-model as the center of the circle. If the characteristic structure is a corner structure, two sub-models of straight line structure are divided according to the corner point of the sub-model, and the divided sub-models are grid-divided according to the preset grid width to obtain several model grids. Based on the melt channel data and deposition data, several model grids are spliced ​​together. The deposition order of the melt channel is determined according to the melt channel data and deposition data. All model grids are spliced ​​together according to the deposition order of the melt channel and the position of each model grid to obtain a workpiece grid model containing structural features, that is, the structural mesh of the target workpiece is obtained.

[0060] S13: Perform temperature field distribution analysis based on the structural mesh combined with the heat source model to obtain temperature field distribution data;

[0061] In the specific implementation process of the present invention, the temperature field distribution analysis is performed based on the structural grid in combination with the heat source model to obtain temperature field distribution data, including: generating molten pool morphology data based on the structural grid in combination with preset processing parameters; matching corresponding heat source model parameters based on the molten pool morphology data, and constructing a heat source model based on the heat source model parameters; determining the time step of the heat source model, performing temperature field distribution analysis based on the time step of the heat source model, and obtaining temperature field distribution data.

[0062] Specifically, melt pool morphology data is generated based on the structural mesh combined with preset processing parameters. The width and depth of the melt pool are determined based on the structural mesh and the preset processing parameters. The melt pool morphology is determined based on the width and depth of the melt pool, thereby obtaining the melt pool morphology data. Corresponding heat source model parameters are matched based on the melt pool morphology data. Specifically, the radius of the heat source model is determined based on the melt pool width, and the height of the heat source model is determined based on the melt pool depth. A heat source model is constructed based on the heat source model parameters. The heat source model is constructed based on the radius and height of the heat source model combined with an energy loading distribution model. The energy loading distribution model reflects the heating time of the heat source and the heat flux density within the heat source's internal space. The time step of the heat source model is determined and set based on the mesh size of the structural mesh. A temperature field distribution analysis is performed based on the time step of the heat source model. The heat source model and its time step are input into finite element simulation software for temperature field distribution simulation analysis to obtain temperature field distribution data.

[0063] S14: Perform thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and perform elastic-plastic strain analysis based on the thermodynamic simulation data and the thermo-solid coupling model to obtain elastic-plastic strain components;

[0064] In the specific implementation process of the present invention, the thermodynamic simulation of the deposition process is performed based on the structural grid to obtain thermodynamic simulation data, and the elastic-plastic strain analysis is performed based on the thermodynamic simulation data and the thermo-solid coupling model to obtain the elastic-plastic strain component, including: determining the deposition simulation thermal conductivity based on the heterogeneous material parameters, and performing thermodynamic simulation of the deposition process based on the deposition simulation thermal conductivity and the structural grid combined with the life-death unit technology to obtain the thermodynamic simulation data; determining the plastic strain curve based on the thermodynamic simulation data, and performing elastic strain curve analysis based on the thermo-solid coupling model to obtain the elastic strain curve; performing elastic-plastic strain analysis based on the plastic strain curve and the elastic strain curve to obtain the elastic-plastic strain component.

[0065] Specifically, the deposition simulation thermal conductivity is determined based on the heterogeneous material parameters, that is, the deposition simulation thermal conductivity is determined according to the thermal performance parameters in the heterogeneous material parameters, and the thermal conductivity of the material is set to the required deposition simulation thermal conductivity under the condition that the temperature is higher than the melting point of the heterogeneous material, and the thermodynamic simulation of the deposition process is performed based on the deposition simulation thermal conductivity and the structural mesh combined with the birth and death unit technology. The birth and death unit technology is a powerful analysis tool in the fields of structural simulation, thermal analysis and fluid dynamics. It allows dynamic activation or deletion of units during the simulation process to simulate phenomena such as material addition and removal, heat transfer or fluid domain changes. For example, when adding deposition materials in thermo-mechanical simulation, the birth and death unit technology is used in the thermo-mechanical simulation software to perform thermo-mechanical simulation of the deposition process through the deposition simulation thermal conductivity and the structural mesh of the target workpiece to obtain thermodynamic simulation data. The plastic strain curve is determined based on thermodynamic simulation data. The plastic strain of the target point is determined based on the thermodynamic simulation data. The target point is a point on the cross section of the deposition path parallel to the scanning path. The plastic strain curve is generated by combining the plastic strain of each target point with the corresponding time point. The elastic strain curve is then analyzed based on the thermo-mechanical coupling model. The thermo-mechanical coupling model can extract the corresponding strain component variation curve from the nodes along the scanning direction on the scanning path. That is, the elastic strain component variation curve is extracted from the thermo-mechanical coupling model to obtain the elastic strain curve. The elastic-plastic strain analysis is performed based on the plastic strain curve and the elastic strain curve. The plastic strain components in the heating and cooling stages are extracted from the plastic strain curve, and the elastic strain components in the heating and cooling stages are extracted from the elastic strain curve. The elastic-plastic strain components are determined from the plastic strain components and the elastic strain components.

[0066] S15: Perform stress and deformation analysis of the workpiece during laser additive manufacturing based on elastic-plastic strain components and temperature field distribution data to obtain stress and deformation data;

[0067] In the specific implementation process of the present invention, the stress and deformation analysis of the workpiece laser additive manufacturing based on the elastic-plastic strain component and the temperature field distribution data is performed to obtain stress and deformation data, including: determining the inherent strain based on the elastic-plastic strain component, and determining the stress and deformation of the workpiece laser additive manufacturing based on the inherent strain using a static analysis model; training a classification regression tree based on the three-dimensional model data, temperature field data, and stress and deformation data of the additively manufactured sample in combination with a depth-limited leaf-by-leaf growth algorithm to obtain a trained classification regression tree, and using the trained classification regression tree as a stress and deformation analysis model; performing stress and deformation distribution analysis of the workpiece laser additive manufacturing based on the temperature field distribution data using the stress and deformation analysis model to obtain stress and deformation distribution data; and determining the stress and deformation data of the workpiece laser additive manufacturing based on the stress and deformation distribution data, the stress and deformation.

[0068] Specifically, the inherent strain is determined based on the elastic-plastic strain component, and the maximum plastic strain component of the heating process, the plastic strain component after cooling, and the elastic strain component are extracted from the elastic-plastic strain component. The inherent strain is determined based on the maximum plastic strain component of the heating process, the plastic strain component after cooling, and the elastic strain component. The inherent strain refers to the strain formed in the additive manufacturing process due to the internal stress generated by the heterogeneous material during the heat treatment and solidification process. The stress and deformation of the workpiece laser additive manufacturing are determined based on the inherent strain using a static analysis model. The heterogeneous material parameters, structural characteristic parameters, and preset processing parameters of the target workpiece are input into the finite element analysis software to construct a static analysis model. Static analysis refers to the analysis of the response of the workpiece additive manufacturing structure under static loads, mainly focusing on the displacement, stress distribution, and stability of the workpiece structure. The inherent strain is input into the static analysis model, the thermal expansion coefficient and unit temperature increment of the static analysis model are set, and the thermal strain of the static analysis model is obtained. The stress and deformation of the workpiece laser additive manufacturing are determined based on the thermal strain. The classification regression tree is trained based on the leaf-by-leaf growth algorithm of the three-dimensional model data, temperature field data, and stress and deformation data of the additive manufacturing sample combined with the depth restriction. The stress and deformation data are the stress data and deformation data obtained by finite element analysis based on the three-dimensional model data combined with different additive manufacturing paths. The temperature field data is the temperature field data of the sample in the finite element analysis of the additive manufacturing process. When training the classification regression tree, the depth of the classification regression tree is set, and the three-dimensional model data and stress and deformation data are used as data sets. The data of the data set is discretized into several discrete values, and a histogram of preset width is constructed. The leaves in the classification regression tree are The histogram is obtained by performing a difference operation between the histogram of the parent node and the histogram of the sibling node. At the same time, when training the classification and regression tree, the data set is divided into several attributes according to the category. Each time the classification and regression tree is trained, the order of the nodes with the largest splitting gain is searched from all attributes as the nodes to be split. The splitting gain can be based on the data volume, gain rate, and Gini coefficient. A depth-limited leaf-based growth algorithm is used. Each time, the leaf with the largest splitting gain is searched from all leaves for splitting. This can reduce errors and make the classification and regression tree more accurate. A trained classification and regression tree is obtained and used as a stress-deformation analysis model. Based on the temperature field distribution data, the stress-deformation analysis model is used to analyze the stress and deformation distribution of the workpiece in laser additive manufacturing. That is, the temperature field data is input into the stress-deformation analysis model to analyze the stress data and deformation data, and the corresponding stress data and deformation data are obtained, that is, the stress-deformation distribution data is obtained.The stress-deformation data of the workpiece laser additive manufacturing is determined based on the stress-deformation distribution data, stress amount and deformation amount. The stress data and deformation data in the stress-deformation distribution data are weighted averaged with the stress amount and deformation amount to obtain the final stress-deformation amount, which is the stress-deformation data of the workpiece laser additive manufacturing, so that the obtained stress-deformation data is more accurate.

[0069] S16: Optimizing preset processing parameters based on stress-deformation data in combination with a multi-objective function to obtain optimized processing parameters, and performing laser additive manufacturing of a target workpiece based on the optimized processing parameters.

[0070] In the specific implementation process of the present invention, the preset processing parameters are optimized based on the stress-deformation data in combination with a multi-objective function to obtain the optimized processing parameters, including: performing sample deposition efficiency analysis, sample tensile strength analysis and elongation analysis of different processing parameters based on orthogonal process experiments to obtain sample deposition efficiency data, sample tensile strength data and sample elongation data; determining a deposition efficiency objective function based on the sample deposition efficiency data, determining a tensile strength objective function based on the sample tensile strength data, and determining an elongation objective function based on the sample elongation data; optimizing the preset processing parameters based on the stress-deformation data in combination with the deposition efficiency objective function, the tensile strength objective function and the elongation objective function to obtain the optimized processing parameters.

[0071] Specifically, based on orthogonal process tests, sample deposition efficiency analysis, sample tensile strength analysis, and sample elongation analysis are conducted under different processing parameters. Orthogonal process tests of sample additive deposition are conducted using different processing parameters, and the deposition efficiency under different processing parameters is calculated, that is, sample deposition efficiency data is obtained. Mechanical properties tests of additive manufacturing are conducted on samples using different processing parameters, and the tensile strength and elongation of samples under different processing parameters can be measured using a tensile testing machine, that is, sample tensile strength data and sample elongation data are obtained. Based on the sample deposition efficiency data, a deposition efficiency objective function is determined, based on the sample tensile strength data, a tensile strength objective function is determined, and based on the sample elongation data, an elongation objective function is determined. Nonlinear fitting is used to establish a deposition efficiency objective function using the sample deposition efficiency data under different processing parameters, a tensile strength objective function is established using the sample tensile strength data under different processing parameters, and an elongation objective function is established using the sample elongation data under different processing parameters. The preset processing parameters are optimized based on stress-deformation data combined with a deposition efficiency objective function, a tensile strength objective function, and an elongation objective function. The adjustment range of the preset processing parameters is determined based on the stress-deformation data. Within this adjustment range, the target adjustment parameters of the preset processing parameters are determined using a particle swarm algorithm combined with the deposition efficiency objective function, the tensile strength objective function, and the elongation objective function. The fitness of the particle swarm algorithm is calculated based on the deposition efficiency objective function, the tensile strength objective function, and the elongation objective function. The preset processing parameters are optimized using the target adjustment parameters to obtain optimized processing parameters. Based on the optimized processing parameters, a laser additive manufacturing device is controlled to perform laser additive manufacturing of a target workpiece. While addressing the deformation problem of the workpiece during laser additive manufacturing, the deposition efficiency, tensile strength, and elongation are also taken into account, resulting in a target workpiece with improved performance.

[0072] In an embodiment of the present invention, a melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of the target workpiece is generated based on the melt channel model. This improves the accuracy of structural mesh generation, provides more accurate data for stress-deformation analysis, and avoids excessive time consumption in subsequent analysis processes. Temperature field distribution analysis is performed based on the structural mesh in combination with a heat source model, making the obtained temperature field distribution data more reliable. A thermodynamic simulation of the deposition process is performed based on the structural mesh to obtain thermodynamic simulation data. Using the more accurate thermodynamic simulation data, an elastic-plastic strain analysis is performed using a thermo-solid coupling model, making the obtained elastic-plastic strain data more accurate to the actual situation. Stress-deformation analysis of the workpiece during laser additive manufacturing is performed based on the elastic-plastic strain components and temperature field distribution data, effectively improving the accuracy of stress-deformation analysis and meeting the stress-deformation analysis requirements for more complex or larger workpieces. Preset processing parameters are optimized based on the stress-deformation data and combined with a multi-objective function to obtain optimized processing parameters. Laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters, improving the reliability of process parameter optimization and effectively improving the quality of laser additive manufacturing of the workpiece.

[0073] Example 2

[0074] See also Figure 2 , Figure 2 FIG. 1 is a flow chart of a method for optimizing additive manufacturing process parameters based on deformation analysis in another embodiment of the present invention, the method comprising:

[0075] S201: Constructing a thermo-mechanical coupling model based on heterogeneous material parameters, structural characteristic parameters, and preset processing parameters of the target workpiece;

[0076] S202: constructing a melt channel model based on preset processing parameters and structural feature parameters, and generating a structural mesh of a target workpiece based on the melt channel model;

[0077] S203: Perform temperature field distribution analysis based on the structural mesh and the heat source model to obtain temperature field distribution data;

[0078] S204: performing a thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and performing an elastic-plastic strain analysis based on the thermodynamic simulation data and a thermo-solid coupling model to obtain elastic-plastic strain components;

[0079] S205: determining an inherent strain based on the elastic-plastic strain component, and determining a stress and a deformation of the workpiece during laser additive manufacturing using a static analysis model based on the inherent strain;

[0080] S206: training a classification regression tree based on the three-dimensional model data, temperature field data, and stress and deformation data of the additively manufactured sample in combination with a depth-limited leaf-by-leaf growing algorithm to obtain a trained classification regression tree, and using the trained classification regression tree as a stress-deformation analysis model;

[0081] S207: performing stress and deformation distribution analysis of the workpiece during laser additive manufacturing using a stress and deformation analysis model based on the temperature field distribution data to obtain stress and deformation distribution data;

[0082] S208: Determining stress and deformation data of the workpiece during laser additive manufacturing based on the stress and deformation distribution data, the stress amount, and the deformation amount;

[0083] S209: Optimizing preset processing parameters based on the stress-deformation data in combination with a multi-objective function to obtain optimized processing parameters, and performing laser additive manufacturing of a target workpiece based on the optimized processing parameters.

[0084] In an embodiment of the present invention, a melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of the target workpiece is generated based on the melt channel model. This improves the accuracy of structural mesh generation, provides more accurate data for stress-deformation analysis, and avoids excessive time consumption in subsequent analysis processes. Temperature field distribution analysis is performed based on the structural mesh in combination with a heat source model, making the obtained temperature field distribution data more reliable. A thermodynamic simulation of the deposition process is performed based on the structural mesh to obtain thermodynamic simulation data. Using the more accurate thermodynamic simulation data, an elastic-plastic strain analysis is performed using a thermo-solid coupling model, making the obtained elastic-plastic strain data more accurate to the actual situation. Stress-deformation analysis of the workpiece during laser additive manufacturing is performed based on the elastic-plastic strain components and temperature field distribution data, effectively improving the accuracy of stress-deformation analysis and meeting the stress-deformation analysis requirements for more complex or larger workpieces. Preset processing parameters are optimized based on the stress-deformation data and combined with a multi-objective function to obtain optimized processing parameters. Laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters, improving the reliability of process parameter optimization and effectively improving the quality of laser additive manufacturing of the workpiece.

[0085] Example 3

[0086] See also Figure 3 , Figure 3 : is a schematic diagram of the structural composition of an additive manufacturing process parameter optimization device based on deformation analysis in an embodiment of the present invention, the device comprising:

[0087] Thermo-structural coupling model module 31: used to construct a thermo-structural coupling model based on heterogeneous material parameters, structural feature parameters and preset processing parameters of the target workpiece;

[0088] Structural mesh module 32: used to construct a melt channel model based on preset processing parameters and structural feature parameters, and generate a structural mesh of the target workpiece based on the melt channel model;

[0089] Temperature field analysis module 33: used to perform temperature field distribution analysis based on the structural grid combined with the heat source model to obtain temperature field distribution data;

[0090] Strain analysis module 34: used to perform thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and perform elastic-plastic strain analysis based on the thermodynamic simulation data and the thermo-solid coupling model to obtain elastic-plastic strain components;

[0091] Stress and deformation analysis module 35: used to perform stress and deformation analysis of the workpiece laser additive manufacturing based on elastic-plastic strain components and temperature field distribution data to obtain stress and deformation data;

[0092] Laser additive manufacturing module 36: used to optimize preset processing parameters based on stress and deformation data combined with multi-objective functions, obtain optimized processing parameters, and perform laser additive manufacturing of the target workpiece based on the optimized processing parameters.

[0093] In the specific implementation process of the present invention, the specific implementation method of the device item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0094] In an embodiment of the present invention, a melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of the target workpiece is generated based on the melt channel model. This improves the accuracy of structural mesh generation, provides more accurate data for stress-deformation analysis, and avoids excessive time consumption in subsequent analysis processes. Temperature field distribution analysis is performed based on the structural mesh in combination with a heat source model, making the obtained temperature field distribution data more reliable. A thermodynamic simulation of the deposition process is performed based on the structural mesh to obtain thermodynamic simulation data. Using the more accurate thermodynamic simulation data, an elastic-plastic strain analysis is performed using a thermo-solid coupling model, making the obtained elastic-plastic strain data more accurate to the actual situation. Stress-deformation analysis of the workpiece during laser additive manufacturing is performed based on the elastic-plastic strain components and temperature field distribution data, effectively improving the accuracy of stress-deformation analysis and meeting the stress-deformation analysis requirements for more complex or larger workpieces. Preset processing parameters are optimized based on the stress-deformation data and combined with a multi-objective function to obtain optimized processing parameters. Laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters, improving the reliability of process parameter optimization and effectively improving the quality of laser additive manufacturing of the workpiece.

[0095] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for optimizing additive manufacturing process parameters based on deformation analysis according to any of the above-described embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, a storage device includes any medium that stores or transmits information in a readable form by a device (e.g., a computer or mobile phone), and may be a read-only memory, a magnetic disk, or an optical disk.

[0096] Example 4

[0097] See also Figure 4 , Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention.

[0098] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. It will be understood by those skilled in the art that Figure 3The electronic devices shown do not constitute a limitation on all devices and may include more or fewer components than shown, or combinations of certain components. The memory 41 can be used to store the computer program 42 and various functional modules, and the processor 43 runs the computer program 42 stored in the memory 41, thereby executing various functional applications and data processing of the device. The memory can be internal memory or external memory, or include both internal memory and external memory. The internal memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. The external memory can include a hard disk, floppy disk, ZIP disk, USB flash drive, magnetic tape, etc. The processor 43 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, a single-chip microcomputer, or processor 43, or any conventional processor. The processor and memory disclosed in the present invention include but are not limited to these types of processors and memories. The processor and memory disclosed in the present invention are only examples and not limitations.

[0099] As an embodiment, the electronic device includes: one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and are configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the additive manufacturing process parameter optimization method based on deformation analysis in any of the above-mentioned embodiments. For the specific implementation process, please refer to the above-mentioned embodiments and will not be repeated here.

[0100] In an embodiment of the present invention, a melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of the target workpiece is generated based on the melt channel model. This improves the accuracy of structural mesh generation, provides more accurate data for stress-deformation analysis, and avoids excessive time consumption in subsequent analysis processes. Temperature field distribution analysis is performed based on the structural mesh in combination with a heat source model, making the obtained temperature field distribution data more reliable. A thermodynamic simulation of the deposition process is performed based on the structural mesh to obtain thermodynamic simulation data. Using the more accurate thermodynamic simulation data, an elastic-plastic strain analysis is performed using a thermo-solid coupling model, making the obtained elastic-plastic strain data more accurate to the actual situation. Stress-deformation analysis of the workpiece during laser additive manufacturing is performed based on the elastic-plastic strain components and temperature field distribution data, effectively improving the accuracy of stress-deformation analysis and meeting the stress-deformation analysis requirements for more complex or larger workpieces. Preset processing parameters are optimized based on the stress-deformation data and combined with a multi-objective function to obtain optimized processing parameters. Laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters, improving the reliability of process parameter optimization and effectively improving the quality of laser additive manufacturing of the workpiece.

[0101] In addition, the above is a detailed introduction to the additive manufacturing process parameter optimization method based on deformation analysis and related devices provided in the embodiments of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for optimizing additive manufacturing process parameters based on deformation analysis, characterized in that: The method comprises: Construct a thermo-mechanical coupling model based on the target workpiece's heterogeneous material parameters, structural feature parameters, and preset machining process parameters; A melt channel model is constructed based on preset processing parameters and structural feature parameters, and a structural mesh of a target workpiece is generated based on the melt channel model; Perform temperature field distribution analysis based on the structural mesh combined with the heat source model to obtain temperature field distribution data; Perform thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and perform elastic-plastic strain analysis based on the thermodynamic simulation data and the thermo-solid coupling model to obtain the elastic-plastic strain components; Based on the elastic-plastic strain components and temperature field distribution data, stress and deformation analysis of the workpiece laser additive manufacturing is performed to obtain stress and deformation data; Based on the stress-deformation data and the multi-objective function, the preset processing parameters are optimized to obtain the optimized processing parameters, and the laser additive manufacturing of the target workpiece is performed based on the optimized processing parameters; The method of performing temperature field distribution analysis based on the structural mesh in combination with the heat source model to obtain temperature field distribution data includes: generating molten pool morphology data based on the structural mesh in combination with preset processing parameters; matching corresponding heat source model parameters based on the molten pool morphology data, and constructing a heat source model based on the heat source model parameters; determining a time step of the heat source model, and performing temperature field distribution analysis based on the time step of the heat source model to obtain temperature field distribution data; The method comprises: performing a thermodynamic simulation of a deposition process based on a structural mesh to obtain thermodynamic simulation data, and performing an elastic-plastic strain analysis based on the thermodynamic simulation data and a thermo-solid coupling model to obtain an elastic-plastic strain component, comprising: determining a deposition simulation thermal conductivity based on heterogeneous material parameters, and performing a thermodynamic simulation of the deposition process based on the deposition simulation thermal conductivity and the structural mesh in combination with a birth-death unit technology to obtain thermodynamic simulation data; determining a plastic strain curve based on the thermodynamic simulation data, and performing an elastic strain curve analysis based on the thermo-solid coupling model to obtain an elastic strain curve; and performing an elastic-plastic strain analysis based on the plastic strain curve and the elastic strain curve to obtain an elastic-plastic strain component. The stress and deformation analysis of the workpiece laser additive manufacturing based on the elastic-plastic strain component and the temperature field distribution data to obtain stress and deformation data includes: determining the inherent strain based on the elastic-plastic strain component, and determining the stress and deformation of the workpiece laser additive manufacturing based on the inherent strain using a static analysis model; training a classification regression tree based on the three-dimensional model data, temperature field data, and stress and deformation data of the additively manufactured sample in combination with a depth-restricted leaf-by-leaf growth algorithm to obtain a trained classification regression tree, and using the trained classification regression tree as a stress and deformation analysis model; performing stress and deformation distribution analysis of the workpiece laser additive manufacturing based on the temperature field distribution data using the stress and deformation analysis model to obtain stress and deformation distribution data; and determining the stress and deformation data of the workpiece laser additive manufacturing based on the stress and deformation distribution data, the stress and deformation.

2. The method for optimizing additive manufacturing process parameters based on deformation analysis according to claim 1, characterized in that: The step of constructing a thermo-mechanical coupling model based on heterogeneous material parameters, structural characteristic parameters, and preset processing parameters of a target workpiece includes: Constructing a geometric numerical model of the target workpiece based on the structural characteristic parameters, and setting heterogeneous material parameters for the geometric numerical model to obtain a first numerical model; Based on the preset processing parameters, the heating and cooling analysis steps and the thermal and mechanical boundary conditions are set for the first numerical model to obtain a thermal-solid coupling model.

3. The method for optimizing additive manufacturing process parameters based on deformation analysis according to claim 1, characterized in that: The method of constructing a melt channel model based on preset processing parameters and structural feature parameters, and generating a structural mesh of a target workpiece based on the melt channel model, includes: Determine melt path data and deposition data based on preset machining process parameters, and generate a sliced ​​two-dimensional model of the target workpiece based on structural feature parameters; Construct a melt channel model based on melt channel data and sedimentation data combined with a slice 2D model; Constructing a boundary curve based on the structural characteristic parameters, and dividing the melt channel model into model grids based on the boundary curve and the structural characteristic parameters to obtain a plurality of model grids; Based on the melt data and deposition data, several model meshes are spliced ​​to obtain the structural mesh of the target workpiece.

4. The method for optimizing additive manufacturing process parameters based on deformation analysis according to claim 1, characterized in that: The method of optimizing preset processing parameters based on stress-deformation data in combination with a multi-objective function to obtain optimized processing parameters includes: Based on orthogonal process experiments, sample deposition efficiency analysis, sample tensile strength analysis and sample elongation analysis with different processing parameters were carried out to obtain sample deposition efficiency data, sample tensile strength data and sample elongation data; Determine a deposition efficiency objective function based on the sample deposition efficiency data, determine a tensile strength objective function based on the sample tensile strength data, and determine an elongation objective function based on the sample elongation data; Based on the stress-deformation data and the deposition efficiency objective function, the tensile strength objective function and the elongation objective function, the preset processing parameters are optimized to obtain the optimized processing parameters.

5. A device for optimizing process parameters of additive manufacturing based on deformation analysis, characterized in that: The device comprises: Thermo-mechanical coupling model module: used to build a thermo-mechanical coupling model based on the heterogeneous material parameters, structural feature parameters and preset processing parameters of the target workpiece; Structural mesh module: used to build a melt channel model based on preset processing parameters and structural feature parameters, and generate a structural mesh of the target workpiece based on the melt channel model; Temperature field analysis module: used to perform temperature field distribution analysis based on the structural grid combined with the heat source model to obtain temperature field distribution data; Strain analysis module: used to perform thermodynamic simulation of the deposition process based on the structural mesh to obtain thermodynamic simulation data, and to perform elastic-plastic strain analysis based on the thermodynamic simulation data and the thermo-solid coupling model to obtain the elastic-plastic strain components; Stress and deformation analysis module: used to perform stress and deformation analysis of workpiece laser additive manufacturing based on elastic-plastic strain components and temperature field distribution data to obtain stress and deformation data; Laser additive manufacturing module: used to optimize preset processing parameters based on stress and deformation data combined with multi-objective functions, obtain optimized processing parameters, and perform laser additive manufacturing of the target workpiece based on the optimized processing parameters; The method of performing temperature field distribution analysis based on the structural mesh in combination with the heat source model to obtain temperature field distribution data includes: generating molten pool morphology data based on the structural mesh in combination with preset processing parameters; matching corresponding heat source model parameters based on the molten pool morphology data, and constructing a heat source model based on the heat source model parameters; determining a time step of the heat source model, and performing temperature field distribution analysis based on the time step of the heat source model to obtain temperature field distribution data; The method comprises: performing a thermodynamic simulation of a deposition process based on a structural mesh to obtain thermodynamic simulation data, and performing an elastic-plastic strain analysis based on the thermodynamic simulation data and a thermo-solid coupling model to obtain an elastic-plastic strain component, comprising: determining a deposition simulation thermal conductivity based on heterogeneous material parameters, and performing a thermodynamic simulation of the deposition process based on the deposition simulation thermal conductivity and the structural mesh in combination with a birth-death unit technology to obtain thermodynamic simulation data; determining a plastic strain curve based on the thermodynamic simulation data, and performing an elastic strain curve analysis based on the thermo-solid coupling model to obtain an elastic strain curve; and performing an elastic-plastic strain analysis based on the plastic strain curve and the elastic strain curve to obtain an elastic-plastic strain component. The stress and deformation analysis of the workpiece laser additive manufacturing based on the elastic-plastic strain component and the temperature field distribution data to obtain stress and deformation data includes: determining the inherent strain based on the elastic-plastic strain component, and determining the stress and deformation of the workpiece laser additive manufacturing based on the inherent strain using a static analysis model; training a classification regression tree based on the three-dimensional model data, temperature field data, and stress and deformation data of the additively manufactured sample in combination with a depth-restricted leaf-by-leaf growth algorithm to obtain a trained classification regression tree, and using the trained classification regression tree as a stress and deformation analysis model; performing stress and deformation distribution analysis of the workpiece laser additive manufacturing based on the temperature field distribution data using the stress and deformation analysis model to obtain stress and deformation distribution data; and determining the stress and deformation data of the workpiece laser additive manufacturing based on the stress and deformation distribution data, the stress and deformation.

6. An electronic device comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the additive manufacturing process parameter optimization method based on deformation analysis according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the additive manufacturing process parameter optimization method based on deformation analysis according to any one of claims 1 to 4.

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