Laser Additive Manufacturing Method and Apparatus for Components Based on Heterogeneous Material Evolution Analysis

Abstract

The application discloses a component laser additive manufacturing method and device based on heterogeneous material evolution analysis, and relates to the technical field of additive manufacturing. The method comprises the following steps: analyzing target micro-size data of heterogeneous materials required by a target component; performing geometric shape analysis on the heterogeneous materials based on pixel error probability compensation; performing spatial distribution analysis on the heterogeneous materials; performing initial process parameter analysis of laser additive manufacturing based on the target micro-size data, geometric shape data and spatial distribution data in combination with heterogeneous material structure evolution influence analysis; performing component material performance analysis based on simulation results of the laser additive manufacturing; adjusting the initial process parameters based on component material performance analysis data in combination with energy consumption analysis to obtain target process parameters; and performing laser additive manufacturing processing on the target component based on the target process parameters. The application can accurately control process parameters, and the performance and morphological structure of the manufactured component can achieve more ideal effects.

Description

Technical Field

[0001] This invention relates to the field of additive manufacturing technology, and in particular to a laser additive manufacturing method and apparatus for components based on heterogeneous material evolution analysis. Background Technology

[0002] Laser additive manufacturing is a component manufacturing method that uses a high-energy laser beam as an energy source to deposit materials layer by layer to form components. Due to its significant advantages over traditional material forming methods in manufacturing complex components, laser additive manufacturing is increasingly being applied in industrial manufacturing. Heterogeneous materials are structures composed of materials with different physical and chemical properties. Because of their more diverse physical and chemical properties, they are gradually being used in additive manufacturing. Currently, methods for laser additive manufacturing using heterogeneous materials typically determine process parameters only by analyzing the geometric morphology of the heterogeneous material, without considering the influence of its microscopic size and spatial distribution of components, or the effects of structural evolution. This leads to insufficient reliability of the analyzed process parameters, resulting in significant deviations between the performance and morphological structure of the manufactured components and the expected results. After analyzing the process parameters, further adjustments and optimizations are usually required to ensure the reliability of laser additive manufacturing. Currently, the process parameters are mainly determined by relevant personnel through data statistics. However, this method relies too heavily on the professional expertise of the relevant personnel, making it difficult to guarantee the accuracy of the process parameter adjustments. Furthermore, its processing efficiency is low, affecting the efficiency and precision of laser additive manufacturing of components. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art. This invention provides a laser additive manufacturing method and apparatus for components based on heterogeneous material evolution analysis. By precisely controlling the parameters, the performance and morphological structure of the manufactured components can achieve more ideal results.

[0004] To address the aforementioned technical problems, this invention provides a laser additive manufacturing method for components based on heterogeneous material evolution analysis, the method comprising:

[0005] Analyze the target micro-dimensional data of the heterogeneous material required for the target component;

[0006] Geometric shape analysis of heterogeneous materials is performed based on pixel error probability compensation to obtain geometric shape data;

[0007] Spatial distribution analysis of heterogeneous materials is performed to obtain spatial distribution data;

[0008] Based on the target's microscopic size data, geometric shape data, and spatial distribution data, combined with the analysis of the influence of heterogeneous material structure evolution, the initial process parameters for laser additive manufacturing of the target component are analyzed to obtain the initial process parameters.

[0009] Laser additive manufacturing simulation is performed based on initial process parameters to obtain simulation results. Then, component material performance analysis is performed based on the simulation results to obtain component material performance analysis data.

[0010] The initial process parameters are adjusted based on the component material performance analysis data and energy consumption analysis to obtain the target process parameters;

[0011] Laser additive manufacturing of target components is performed based on target process parameters.

[0012] Optionally, the target micro-dimensional data of the heterogeneous material required for analyzing the target component includes:

[0013] The optical 3D surface profilometer uses white light interferometry to measure the micro-size of heterogeneous materials and obtain the first micro-size data.

[0014] Using confocal microscopy, the microscopic dimensions of heterogeneous materials are measured using confocal technology to obtain second microscopic dimension data, and target microscopic dimension data is generated based on the first and second microscopic dimension data.

[0015] Optionally, the step of performing geometric shape analysis on heterogeneous materials based on pixel error probability compensation to obtain geometric shape data includes:

[0016] The target image of the heterogeneous material is subjected to saliency processing and binarization to obtain a binarized target saliency map;

[0017] The foreground pixel probability and background pixel probability of the binarized target saliency map are determined based on the pixel probability model, and the foreground pixel probability and background pixel probability are compensated for by error probability to obtain the target foreground pixel probability and target background pixel probability.

[0018] Determine the region contour of heterogeneous material in the target image based on the target foreground pixel probability and the target background pixel probability;

[0019] Geometric shape analysis is performed on the regional contour of heterogeneous materials to obtain geometric shape data.

[0020] Optionally, the step of performing error probability compensation on the foreground pixel probability and background pixel probability to obtain the target foreground pixel probability and target background pixel probability includes:

[0021] Classification error analysis is performed on the pixel probability model to obtain classification error data;

[0022] Based on the classification error data, compensation values ​​for the foreground pixel probability and background pixel probability are determined, and error probability compensation is performed on the foreground pixel probability and background pixel probability based on the compensation values ​​to obtain the target foreground pixel probability and target background pixel probability.

[0023] Optionally, the step of performing spatial distribution analysis on heterogeneous materials to obtain spatial distribution data includes:

[0024] Construct a representative volume element of the heterogeneous material, and mesh the representative volume element to obtain several meshes corresponding to the representative volume element;

[0025] A structural simulation model of heterogeneous materials is constructed, and the component location coordinates and material properties of each grid are analyzed based on the structural simulation model to obtain the component location coordinates and material properties.

[0026] Spatial distribution data is determined based on the component location coordinates and material properties of each grid.

[0027] Optionally, the initial process parameter analysis for laser additive manufacturing of the target component based on target micro-size data, geometric shape data, and spatial distribution data combined with the influence analysis of heterogeneous material structure evolution, to obtain initial process parameters, includes:

[0028] Based on different process parameters of laser additive manufacturing, combined with target micro-size data, geometric data and spatial distribution data, a physical field coupling model is constructed using finite element simulation technology;

[0029] A microstructure evolution model is constructed based on the physical field coupling model, and the influence of the microstructure evolution model on the evolution of heterogeneous material structure is analyzed to obtain data on the influence of heterogeneous material structure evolution.

[0030] Initial process parameters are obtained by combining data on the influence of heterogeneous material structure evolution, target micro-size data, geometric shape data, and spatial distribution data with process correlation models.

[0031] Optionally, the step of performing laser additive manufacturing simulation based on initial process parameters to obtain laser additive manufacturing simulation results, and performing component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data, includes:

[0032] Laser additive manufacturing simulation is performed based on initial process parameters, target micro-size data, geometric data, and spatial distribution data to obtain laser additive manufacturing simulation results;

[0033] Based on the simulation results of laser additive manufacturing, the impact resistance, wear resistance and corrosion resistance of the components are analyzed using a simulation comparison model to obtain material performance analysis data.

[0034] Optionally, the adjustment of initial process parameters based on component material performance analysis data combined with energy consumption analysis to obtain target process parameters includes:

[0035] The adjustment range of the initial process parameters is determined based on the component material performance analysis data;

[0036] Set up time and energy consumption models for the laser additive manufacturing process, and construct a target optimization model based on the time and energy consumption models;

[0037] Based on the adjustment range of the initial process parameters, energy consumption analysis of the laser additive manufacturing process is carried out by combining time model and energy consumption model with orthogonal experiment to obtain energy consumption analysis data.

[0038] Based on energy consumption analysis data and target optimization model, adjustment parameters are determined, and the initial process parameters are adjusted based on the adjustment parameters to obtain the target process parameters.

[0039] Optionally, the laser additive manufacturing process of the target component based on the target process parameters includes:

[0040] Path planning for the laser additive manufacturing process is performed based on geometric shape data and spatial distribution data combined with component forming requirements to obtain path data;

[0041] Based on path data and target process parameters, processing instructions are generated, and the laser additive manufacturing equipment performs laser additive manufacturing of the target component based on the processing instructions.

[0042] In addition, the present invention also provides a laser additive manufacturing apparatus for components based on heterogeneous material evolution analysis, the apparatus comprising:

[0043] Microscopic size analysis module: used to analyze the target microscopic size data of the heterogeneous material required for the target component;

[0044] Geometric shape analysis module: used to perform geometric shape analysis on heterogeneous materials based on pixel error probability compensation to obtain geometric shape data;

[0045] Spatial distribution analysis module: used to perform spatial distribution analysis on heterogeneous materials and obtain spatial distribution data;

[0046] Parameter Analysis Module: Used to analyze the initial process parameters for laser additive manufacturing of target components based on target micro-size data, geometric data, and spatial distribution data, combined with the influence analysis of heterogeneous material structure evolution, and obtain the initial process parameters;

[0047] Material performance analysis module: used to perform laser additive manufacturing simulation based on initial process parameters, obtain laser additive manufacturing simulation results, and perform component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data;

[0048] Parameter adjustment module: used to adjust the initial process parameters based on component material performance analysis data and energy consumption analysis to obtain the target process parameters;

[0049] Laser Additive Manufacturing Module: Used for laser additive manufacturing of target parts based on target process parameters.

[0050] In this embodiment of the invention, geometric shape analysis of heterogeneous materials based on pixel error probability compensation can improve the accuracy of the geometric shape analysis. Initial process parameter analysis for laser additive manufacturing of the target component is performed based on target micro-size data, geometric shape data, and spatial distribution data combined with the influence analysis of heterogeneous material structural evolution. This clarifies the influence data on heterogeneous material structural evolution, providing more comprehensive and accurate data support for the initial process parameter analysis, making the obtained initial process parameters more reliable. Laser additive manufacturing simulation is performed based on the initial process parameters to obtain simulation results. Component material performance analysis is then conducted based on these results. Adjustments to the initial process parameters are made based on the component material performance analysis data combined with energy consumption analysis, ensuring the accuracy of the initial process parameter adjustment and improving the efficiency of parameter adjustment analysis. Laser additive manufacturing of the target component is then performed based on the target process parameters. Through precise parameter control, the performance and morphological structure of the manufactured component achieve more ideal results. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 This is a schematic flowchart of a laser additive manufacturing method for components based on heterogeneous material evolution analysis in an embodiment of the present invention.

[0053] Figure 2 This is a schematic flowchart of a laser additive manufacturing method for components based on heterogeneous material evolution analysis, according to another embodiment of the present invention.

[0054] Figure 3 This is a schematic diagram of the structural composition of a component laser additive manufacturing device based on heterogeneous material evolution analysis in an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1

[0057] Please see Figure 1 , Figure 1 This is a schematic flowchart of a laser additive manufacturing method for components based on heterogeneous material evolution analysis, as described in an embodiment of the present invention. The method includes:

[0058] S11: Target micro-dimensional data of the heterogeneous material required for analyzing the target component;

[0059] In the specific implementation of this invention, the target micro-size data of the heterogeneous material required for analyzing the target component includes: measuring the micro-size of the heterogeneous material using white light interferometry based on an optical 3D surface profilometer to obtain first micro-size data; measuring the micro-size of the heterogeneous material using confocal microscopy to obtain second micro-size data; and generating target micro-size data based on the first and second micro-size data.

[0060] Specifically, the target components can be key transmission components such as gears and shafts in offshore wind power equipment in the new energy field, or heterogeneous materials such as high-strength and tough medium-entropy alloys combined with wear-resistant and corrosion-resistant martensitic stainless steel powder. Based on an optical three-dimensional (3D) surface profilometer, white light interferometry is used to measure the micro-size of heterogeneous materials. The optical 3D surface profilometer is a non-contact metrological instrument with white light interferometry as its core principle. This instrument has nanometer-level resolution and sub-nanometer-level repeatability, suitable for the measurement and analysis of micro-sizes. White light interferometry is a technique that uses the principle of interference to measure the difference in optical path length to determine relevant physical quantities. The light emitted from the light source is expanded and collimated, then split into two beams by a beam splitter. One beam is reflected back from the surface being measured, and the other beam is reflected by a reference mirror. The two reflected beams eventually converge and interfere. Any change in the optical path difference between the two coherent beams will sensitively cause the interference fringes to shift. The change in the optical path length of a certain coherent beam is caused by the geometric path it has traversed or the change in the refractive index of the medium. By measuring the interference fringes... By measuring changes in the surface, relevant physical quantities of the tested surface can be determined. Sub-nanometer measurements of the heterogeneous material are performed using a white light interferometer with an optical 3D surface profilometer to obtain the first microscopic size data. Microscopic measurements of the heterogeneous material are then performed using a confocal microscope, a high-resolution and highly photosensitive metrology instrument. The confocal microscope utilizes a laser scanning beam to form a point light source through a grating pinhole, scanning point by point on the focal plane of the sample. The light signal from the collected point reaches a photomultiplier tube through a probe pinhole, and after signal processing, is analyzed on the system to obtain micro-nanoscale measurement data of the heterogeneous material, i.e., the second microscopic size data. Target microscopic size data is then generated based on the first and second microscopic size data; that is, the target microscopic size data is composed of the first and second microscopic size data.

[0061] S12: Perform geometric shape analysis on heterogeneous materials based on pixel error probability compensation to obtain geometric shape data;

[0062] In a specific implementation of this invention, the step of performing geometric shape analysis on heterogeneous materials based on pixel error probability compensation to obtain geometric shape data includes: performing saliency processing and binarization processing on the target image of the heterogeneous material to obtain a binarized target saliency map; determining the foreground pixel probability and background pixel probability of the binarized target saliency map based on a pixel probability model, and performing error probability compensation on the foreground pixel probability and background pixel probability to obtain the target foreground pixel probability and target background pixel probability; determining the region contour of the heterogeneous material in the target image based on the target foreground pixel probability and target background pixel probability; and performing geometric shape analysis based on the region contour of the heterogeneous material to obtain geometric shape data.

[0063] Furthermore, the step of performing error probability compensation on the foreground pixel probability and background pixel probability to obtain the target foreground pixel probability and target background pixel probability includes: performing classification error analysis on the pixel probability model to obtain classification error data; determining compensation values ​​for the foreground pixel probability and background pixel probability based on the classification error data; and performing error probability compensation on the foreground pixel probability and background pixel probability based on the compensation values ​​to obtain the target foreground pixel probability and target background pixel probability.

[0064] Specifically, a surface image of the heterogeneous material is acquired using a high-resolution image acquisition device; this image, containing both the heterogeneous material and its background, is the target image. The target image undergoes saliency processing and binarization, followed by noise reduction. The frequency domain features of the denoised target image are extracted, and a spectrogram is generated based on these features. This spectrogram is then filtered to obtain a filtered spectrogram. The filtered spectrogram is converted to a time domain image using inverse Fourier transform and inverse wavelet transform. This time domain image is then input into a visual saliency model for saliency processing, resulting in a target saliency map. Image saliency processing determines the importance of each pixel relative to the background. Finally, the target saliency map is binarized using the Otsu algorithm, which calculates a binarization threshold to obtain a binarized target saliency map. The foreground and background pixel probabilities of the binarized target saliency map are determined based on a pixel probability model. Sample images and sample background images are acquired. The sample image includes the sample heterogeneous material and its surrounding background, while the sample background image represents the background region of the sample image. The distance between each pixel in the sample image and its corresponding pixel in the sample background image is calculated. When this distance is greater than a first preset distance threshold, the corresponding pixel is designated as a seed foreground pixel; when the distance is less than a second preset distance threshold, the corresponding pixel is designated as a seed background pixel. The first preset distance threshold is greater than the second preset distance threshold. The sample image is labeled using seed foreground and seed background pixels. A deep convolutional neural network is trained based on the labeled sample images. The trained deep convolutional neural network is used as a pixel probability model. The binarized target saliency map is input into the pixel probability model for pixel probability analysis, yielding the corresponding foreground and background pixel probabilities, i.e., the probability that a pixel in the image belongs to the foreground pixel and the probability that it belongs to the background pixel. A classification error analysis is performed on the pixel probability model. The pixel probability model may contain errors in its pixel probability analysis; for example, a pixel belonging to the background might have a higher probability of being classified as a foreground pixel. Therefore, a classification error analysis of the pixel probability model is necessary. An error compensator is constructed using test data from simulation applications of pixel probability calculation. This error compensator is then used to analyze the output errors of the pixel probability model regarding background and foreground pixel probabilities, thus obtaining classification error data. Based on the classification error data, compensation values ​​for the foreground and background pixel probabilities are determined. This involves matching the compensation values ​​to the foreground and background pixel probabilities using the classification error data, and then performing error probability compensation on the foreground and background pixel probabilities based on these compensation values. In other words, the foreground and background pixel probabilities are calculated using their corresponding compensation values ​​to obtain the target foreground and target background pixel probabilities.The contour of the heterogeneous material region in the target image is determined based on the target foreground pixel probability and the target background pixel probability. These probabilities are then input into a segmentation model to obtain the contour of the heterogeneous material region in the target image. Geometric shape analysis is performed based on the contour of the heterogeneous material region to determine the contour point coordinates. The geometric shape of the heterogeneous material is then identified using the region contour and contour point coordinates based on the recognition model, and geometric shape data is generated from the geometric shape and contour point coordinates.

[0065] S13: Perform spatial distribution analysis on heterogeneous materials to obtain spatial distribution data;

[0066] In the specific implementation of this invention, the step of performing spatial distribution analysis on heterogeneous materials to obtain spatial distribution data includes: constructing representative volume units of the heterogeneous materials and dividing the representative volume units into meshes to obtain several meshes corresponding to the representative volume units; constructing a structural simulation model of the heterogeneous materials and performing component position coordinate analysis and material property analysis on each mesh based on the structural simulation model to obtain component position coordinates and material properties; and determining spatial distribution data based on the component position coordinates and material properties of each mesh.

[0067] Specifically, representative volumetric elements of the heterogeneous material are constructed. Finite element analysis software is used to construct corresponding representative volumetric elements based on microscopic size information, geometric shape information, and the structure of the heterogeneous material. These representative volumetric elements contain sufficient geometric and distribution information of the microstructure components. The representative volumetric elements are then meshed according to preset horizontal and vertical spacing, resulting in several meshes corresponding to each representative volumetric element. A structural simulation model of the heterogeneous material is then constructed. Phase separation data of the heterogeneous material is used as training data to train a machine learning model, resulting in the structural simulation model. Phase separation refers to the process by which an alloy mixture splits into two or more phases with different physical and chemical properties. The structural simulation model can describe the interface evolution of phase separation in heterogeneous materials. Based on the structural simulation model, component position coordinate analysis and material property analysis are performed on each mesh to obtain the component material data of the heterogeneous material. For example, if the heterogeneous material uses a high-strength, high-toughness, medium-entropy alloy and wear-resistant, corrosion-resistant martensitic stainless steel powder, the proportions of these two components are determined. The component material data is input into the structural simulation model to analyze the material properties of each mesh and the position coordinates of all component phases within each mesh, thus obtaining the component position coordinates and material properties. Spatial distribution data is determined based on the component location coordinates and material properties of each grid. The spatial distribution of components in a heterogeneous material structure can be described by the component location coordinates and material properties of each grid.

[0068] S14: Based on the target's microscopic size data, geometric shape data, and spatial distribution data, combined with the analysis of the influence of heterogeneous material structure evolution, the initial process parameters for laser additive manufacturing of the target component are analyzed to obtain the initial process parameters;

[0069] In the specific implementation of this invention, the initial process parameter analysis for laser additive manufacturing of the target component based on target microscopic size data, geometric shape data, and spatial distribution data combined with the influence analysis of heterogeneous material structure evolution, to obtain initial process parameters, includes: constructing a physical field coupling model using finite element simulation technology based on different process parameters of laser additive manufacturing combined with target microscopic size data, geometric shape data, and spatial distribution data; constructing a microstructure evolution model based on the physical field coupling model, and performing heterogeneous material structure evolution influence analysis based on the microstructure evolution model to obtain heterogeneous material structure evolution influence data; and performing initial process parameter analysis based on heterogeneous material structure evolution influence data, target microscopic size data, geometric shape data, and spatial distribution data combined with a process correlation model to obtain initial process parameters.

[0070] Specifically, based on different process parameters of laser additive manufacturing, combined with target microscopic size data, geometric shape data, and spatial distribution data, a physical field coupling model is constructed using finite element simulation technology. Process parameters include laser power, scanning speed, and overlapping parameters. Different process parameters, target microscopic size data, geometric shape data, and spatial distribution data are input into the finite element simulation model to construct electromagnetic field mathematical models, thermal field mathematical models, and flow field mathematical models of the laser additive manufacturing process. The electromagnetic field mathematical model can adopt Maxwell's equations, the thermal field mathematical model can adopt the heat conduction equation, and the flow field mathematical model can adopt the Navier-Stokes equations. Through a preset coupling strategy, the physical field coupling model is constructed using the electromagnetic field mathematical model, the thermal field mathematical model, and the flow field mathematical model. The preset coupling strategy is that the electromagnetic field and the thermal field are coupled through the Joule heat source term, the electromagnetic field and the flow field are coupled through the Lorentz force, and the thermal field and the flow field are coupled through temperature distribution, latent heat of phase change, and surface tension. A microstructure evolution model is constructed based on the aforementioned physical field coupling model. The corresponding grain growth equation, phase field equation, and diffusion equation are determined by the microscopic size, geometry, and spatial distribution of the heterostructure. The grain growth equation describes the growth process after nucleation, the phase field equation describes the phase transition and motion process of the heterostructure, and can describe the evolution of the solid-liquid interface. The diffusion equation describes the solute redistribution process. The microstructure evolution model, composed of the physical field coupling model, grain growth equation, phase field equation, and diffusion equation, enables the analysis of the dynamic evolution of the microstructure of heterostructures during laser additive manufacturing. The microstructure evolution model is used to analyze the influence of heterogeneous material structure evolution. This analysis combines the microstructure evolution model with target micro-size data, geometric shape data, and spatial distribution data to analyze the microstructure evolution information of heterogeneous materials under different process parameters. The impact of this microstructure evolution information on the performance of the formed part is analyzed; for example, finer grains in the microstructure result in better mechanical properties, and more grain interfaces improve the material's toughness and strength. This analysis of the influence of heterogeneous material microstructure evolution on the performance of the formed part under different process parameters yields data on the influence of heterogeneous material structure evolution. Based on the heterogeneous material structure evolution data, target micro-size data, geometric shape data, and spatial distribution data, and combined with a process correlation model, initial process parameters are analyzed. This process correlation model uses a deep learning model to match the corresponding process parameters based on the heterogeneous material structure evolution data, target micro-size data, geometric shape data, spatial distribution data, and the forming requirements of the target part, thus obtaining initial process parameters that align with the expected performance and shape of the part.

[0071] S15: Perform laser additive manufacturing simulation based on initial process parameters, obtain laser additive manufacturing simulation results, and perform component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data;

[0072] In the specific implementation of this invention, the process of performing laser additive manufacturing simulation based on initial process parameters to obtain laser additive manufacturing simulation results, and performing component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data, includes: performing laser additive manufacturing simulation based on initial process parameters, target microscopic size data, geometric shape data, and spatial distribution data to obtain laser additive manufacturing simulation results; and using a simulation comparison model to analyze impact resistance, wear resistance, and corrosion resistance based on the laser additive manufacturing simulation results to obtain component material performance analysis data.

[0073] Specifically, laser additive manufacturing simulation is performed based on initial process parameters, target micro-size data, geometric data, and spatial distribution data. The initial process parameters, target micro-size data, geometric data, and spatial distribution data are input into the simulation software to perform laser additive manufacturing simulation of the target component, thereby obtaining the simulation manufacturing model of the target component, which is the result of laser additive manufacturing simulation. Based on the simulation results of laser additive manufacturing, impact resistance, wear resistance, and corrosion resistance are analyzed using simulation comparison models. These models include impact, wear, and corrosion comparison models, which characterize the performance of these properties. Several nodes are set in the simulation manufacturing model of the target component, with impact, wear, and corrosion parameters configured. Impact parameters include impact velocity, wear parameters include friction force, and corrosion parameters include corrosive medium concentration. Impact, wear, and corrosion simulation tests are conducted on these nodes according to the set parameters. The results are then compared with the corresponding simulation comparison models to obtain the impact resistance, wear resistance, and corrosion resistance data of the target component obtained through laser additive manufacturing according to the initial process parameters. Finally, component material performance analysis data is generated from these data.

[0074] S16: Adjust the initial process parameters based on component material performance analysis data and energy consumption analysis to obtain the target process parameters;

[0075] In the specific implementation of this invention, the step of adjusting the initial process parameters based on component material performance analysis data combined with energy consumption analysis to obtain target process parameters includes: determining the adjustment range of the initial process parameters based on component material performance analysis data; setting a time model and an energy consumption model for the laser additive manufacturing process, and constructing a target optimization model based on the time model and energy consumption model; conducting energy consumption analysis of the laser additive manufacturing process using the time model and energy consumption model combined with orthogonal experiments based on the adjustment range of the initial process parameters to obtain energy consumption analysis data; determining adjustment parameters based on the energy consumption analysis data and the target optimization model, and adjusting the initial process parameters based on the adjustment parameters to obtain the target process parameters.

[0076] Specifically, the adjustment range of initial process parameters is determined based on component material performance analysis data. The component material performance analysis data is compared with the expected performance data, and the adjustment range of the initial process parameters is matched based on the comparison results. A time model and an energy consumption model for the laser additive manufacturing process are set up. An overall time model is constructed based on the time models of each step in the laser additive manufacturing process, and an energy consumption model is constructed based on the running time and equipment output power of each step. A target optimization model is then constructed based on the time model and the energy consumption model, with the minimization of time and energy consumption in the laser additive manufacturing process and the maximum utilization rate of heterogeneous materials as optimization objectives. The target optimization model is then constructed by combining the time model and the energy consumption model. Based on the adjustment range of initial process parameters, energy consumption analysis of the laser additive manufacturing process is conducted using orthogonal experiments combined with time and energy consumption models. One adjustment parameter is selected within the adjustment range of the initial process parameters. The initial process parameters are adjusted according to this parameter, and orthogonal experiments are performed using the adjusted initial process parameters. The time required for laser additive manufacturing under these adjusted parameters is analyzed using a time model. Energy consumption data is analyzed based on the required time and an energy consumption model. All adjustment parameters within the adjustment range are tested, and the resulting energy consumption and time data constitute the energy consumption analysis data. Based on the energy consumption analysis data and a target optimization model, adjustment parameters are determined. The material utilization rate of each adjustment parameter is determined according to the adjustment range of the initial process parameters. The adjustment parameters are analyzed using the energy consumption analysis data and material utilization rate combined with the target optimization model to obtain the final adjustment parameters. Based on these adjustment parameters, the initial process parameters are adjusted to obtain the target process parameters, which reduce the energy consumption of laser additive manufacturing and improve material utilization.

[0077] S17: Perform laser additive manufacturing of the target component based on the target process parameters.

[0078] In the specific implementation of this invention, the laser additive manufacturing process of the target component based on the target process parameters includes: planning the path of the laser additive manufacturing process based on geometric shape data and spatial distribution data combined with the component forming requirements to obtain path data; generating processing instructions based on the path data and the target process parameters; and the laser additive manufacturing equipment performing laser additive manufacturing of the target component based on the processing instructions.

[0079] Specifically, the laser additive manufacturing process involves path planning based on geometric shape data, spatial distribution data, and component forming requirements. The component shape is determined according to these requirements, and the operating path of the laser additive manufacturing equipment is determined using the geometric shape data and spatial distribution data of the heterogeneous material, thus obtaining path data. Based on the path data and target process parameters, processing instructions are generated and transmitted to the laser additive manufacturing equipment. The equipment then performs laser additive manufacturing on the target component according to these instructions, employing laser selection and melting, powder spreading, and laser melting of the corresponding heterogeneous material until the entire target component is constructed. This process improves the impact resistance, wear resistance, and corrosion resistance of the target component, significantly enhancing the service performance and lifespan of key components.

[0080] In this embodiment of the invention, geometric shape analysis of heterogeneous materials based on pixel error probability compensation can improve the accuracy of the geometric shape analysis. Initial process parameter analysis for laser additive manufacturing of the target component is performed based on target micro-size data, geometric shape data, and spatial distribution data combined with the influence analysis of heterogeneous material structural evolution. This clarifies the influence data on heterogeneous material structural evolution, providing more comprehensive and accurate data support for the initial process parameter analysis, making the obtained initial process parameters more reliable. Laser additive manufacturing simulation is performed based on the initial process parameters to obtain simulation results. Component material performance analysis is then conducted based on these results. Adjustments to the initial process parameters are made based on the component material performance analysis data combined with energy consumption analysis, ensuring the accuracy of the initial process parameter adjustment and improving the efficiency of parameter adjustment analysis. Laser additive manufacturing of the target component is then performed based on the target process parameters. Through precise parameter control, the performance and morphological structure of the manufactured component achieve more ideal results.

[0081] Example 2

[0082] Please see Figure 2 , Figure 2 This is a flowchart illustrating a laser additive manufacturing method for components based on heterogeneous material evolution analysis, according to another embodiment of the present invention. The method includes:

[0083] S201: Target micro-dimensional data of the heterogeneous material required for analyzing the target component;

[0084] S202: Perform geometric shape analysis on heterogeneous materials based on pixel error probability compensation to obtain geometric shape data;

[0085] S203: Perform spatial distribution analysis on heterogeneous materials to obtain spatial distribution data;

[0086] S204: Based on different process parameters of laser additive manufacturing, combined with target micro-size data, geometric data and spatial distribution data, a physical field coupling model is constructed using finite element simulation technology;

[0087] S205: Construct a microstructure evolution model based on the physical field coupling model, and conduct an influence analysis on the evolution of heterogeneous material structure based on the microstructure evolution model to obtain data on the influence of heterogeneous material structure evolution.

[0088] S206: Initial process parameters are analyzed based on the data on the influence of heterogeneous material structure evolution, target micro-size data, geometric shape data, and spatial distribution data, combined with the process correlation model, to obtain the initial process parameters.

[0089] S207: Perform laser additive manufacturing simulation based on initial process parameters, obtain laser additive manufacturing simulation results, and perform component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data;

[0090] S208: Adjust the initial process parameters based on component material performance analysis data and energy consumption analysis to obtain the target process parameters;

[0091] S209: Laser additive manufacturing of target components based on target process parameters.

[0092] In this embodiment of the invention, geometric shape analysis of heterogeneous materials based on pixel error probability compensation can improve the accuracy of the geometric shape analysis. Initial process parameter analysis for laser additive manufacturing of the target component is performed based on target micro-size data, geometric shape data, and spatial distribution data combined with the influence analysis of heterogeneous material structural evolution. This clarifies the influence data on heterogeneous material structural evolution, providing more comprehensive and accurate data support for the initial process parameter analysis, making the obtained initial process parameters more reliable. Laser additive manufacturing simulation is performed based on the initial process parameters to obtain simulation results. Component material performance analysis is then conducted based on these results. Adjustments to the initial process parameters are made based on the component material performance analysis data combined with energy consumption analysis, ensuring the accuracy of the initial process parameter adjustment and improving the efficiency of parameter adjustment analysis. Laser additive manufacturing of the target component is then performed based on the target process parameters. Through precise parameter control, the performance and morphological structure of the manufactured component achieve more ideal results.

[0093] Example 3

[0094] Please see Figure 3 , Figure 3 This is a schematic diagram of the structural composition of a component laser additive manufacturing device based on heterogeneous material evolution analysis in an embodiment of the present invention. The device includes:

[0095] Microscopic size analysis module 31: Used to analyze the target microscopic size data of the heterogeneous material required for the target component;

[0096] Geometric shape analysis module 32: used to perform geometric shape analysis on heterogeneous materials based on pixel error probability compensation to obtain geometric shape data;

[0097] Spatial distribution analysis module 33: used to perform spatial distribution analysis on heterogeneous materials and obtain spatial distribution data;

[0098] Parameter Analysis Module 34: Used to analyze the initial process parameters for laser additive manufacturing of target components based on target micro-size data, geometric shape data and spatial distribution data combined with the influence analysis of heterogeneous material structure evolution, and obtain the initial process parameters;

[0099] Material performance analysis module 35: Used to perform laser additive manufacturing simulation based on initial process parameters, obtain laser additive manufacturing simulation results, and perform component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data;

[0100] Parameter adjustment module 36: used to adjust the initial process parameters based on component material performance analysis data and energy consumption analysis to obtain the target process parameters;

[0101] Laser Additive Manufacturing Module 37: Used for laser additive manufacturing of target parts based on target process parameters.

[0102] In the specific implementation of this invention, the specific implementation of the device item can be referred to the implementation of the method item above, and will not be repeated here.

[0103] In this embodiment of the invention, geometric shape analysis of heterogeneous materials based on pixel error probability compensation can improve the accuracy of the geometric shape analysis. Initial process parameter analysis for laser additive manufacturing of the target component is performed based on target micro-size data, geometric shape data, and spatial distribution data combined with the influence analysis of heterogeneous material structural evolution. This clarifies the influence data on heterogeneous material structural evolution, providing more comprehensive and accurate data support for the initial process parameter analysis, making the obtained initial process parameters more reliable. Laser additive manufacturing simulation is performed based on the initial process parameters to obtain simulation results. Component material performance analysis is then conducted based on these results. Adjustments to the initial process parameters are made based on the component material performance analysis data combined with energy consumption analysis, ensuring the accuracy of the initial process parameter adjustment and improving the efficiency of parameter adjustment analysis. Laser additive manufacturing of the target component is then performed based on the target process parameters. Through precise parameter control, the performance and morphological structure of the manufactured component achieve more ideal results.

[0104] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0105] Furthermore, the above provides a detailed description of the laser additive manufacturing method and apparatus for components based on heterogeneous material evolution analysis provided by the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method of component laser additive manufacturing based on analysis of heterogeneous material evolution, characterized in that, The method comprises: analyzing target micro-size data of the heterogeneous material required by the target component; performing geometric shape analysis on the heterogeneous material based on pixel error probability compensation to obtain geometric shape data; performing spatial distribution analysis on the heterogeneous material to obtain spatial distribution data; performing initial process parameter analysis on the target component laser additive manufacturing based on the target micro-size data, the geometric shape data and the spatial distribution data combined with the influence analysis of the structure evolution of the heterogeneous material to obtain the initial process parameters; performing laser additive manufacturing simulation based on the initial process parameters to obtain laser additive manufacturing simulation results, and performing component material performance analysis based on the laser additive manufacturing simulation results to obtain component material performance analysis data; adjusting the initial process parameters based on the component material performance analysis data combined with energy consumption analysis to obtain target process parameters; performing laser additive manufacturing processing on the target component based on the target process parameters. The method comprises:

2. The component laser additive manufacturing method based on hetero-material evolution analysis of claim 1, wherein, performing micro-size measurement on the heterogeneous material based on an optical 3D surface profiler using white light interference technology to obtain first micro-size data; performing micro-size measurement on the heterogeneous material based on a confocal microscope using confocal technology to obtain second micro-size data, and generating target micro-size data based on the first micro-size data and the second micro-size data. The method comprises:

3. The component laser additive manufacturing method based on heterogenous material evolution analysis of claim 1, wherein, performing classification error analysis on the pixel probability model to obtain classification error data; determining compensation values of the foreground pixel probability and the background pixel probability based on the classification error data, and performing error probability compensation on the foreground pixel probability and the background pixel probability based on the compensation values to obtain target foreground pixel probability and target background pixel probability. The method comprises:

4. The component laser additive manufacturing method based on heterogenous material evolution analysis of claim 1, wherein, constructing a representative volume unit of the heterogeneous material, and performing grid division on the representative volume unit to obtain a plurality of grids corresponding to the representative volume unit; constructing a structure simulation model of the heterogeneous material, and performing component position coordinate analysis and material attribute analysis on each grid based on the structure simulation model to obtain component position coordinates and material attributes; determining the spatial distribution data based on the component position coordinates and the material attributes of each grid. ​ 5. The component laser additive manufacturing method based on heterogenous material evolution analysis of claim 1, wherein, The initial process parameter analysis of the target component laser additive manufacturing based on the target micro-size data, the geometric shape data and the spatial distribution data combined with the heterogeneous material structure evolution influence analysis obtains the initial process parameter, including: The physical field coupling model is constructed based on the different process parameters of the laser additive manufacturing combined with the target micro-size data, the geometric shape data and the spatial distribution data by using the finite element simulation technology; The microstructure evolution model is constructed based on the physical field coupling model, and the heterogeneous material structure evolution influence analysis is performed based on the microstructure evolution model to obtain the heterogeneous material structure evolution influence data; The initial process parameter analysis is performed based on the heterogeneous material structure evolution influence data, the target micro-size data, the geometric shape data and the spatial distribution data combined with the process process correlation model to obtain the initial process parameter.

6. The component laser additive manufacturing method based on heterogenous material evolution analysis of claim 1, wherein, The laser additive manufacturing simulation is performed based on the initial process parameter to obtain the laser additive manufacturing simulation result, and the component material performance analysis is performed based on the laser additive manufacturing simulation result to obtain the component material performance analysis data, including: The laser additive manufacturing simulation is performed based on the initial process parameter, the target micro-size data, the geometric shape data and the spatial distribution data to obtain the laser additive manufacturing simulation result; The impact resistance, wear resistance and corrosion resistance performance analysis is performed based on the laser additive manufacturing simulation result by using the simulation comparison model to obtain the component material performance analysis data.

7. The component laser additive manufacturing method based on heterogenous material evolution analysis of claim 1, wherein, The initial process parameter is adjusted based on the component material performance analysis data combined with the energy consumption analysis to obtain the target process parameter, including: The adjustment range of the initial process parameter is determined based on the component material performance analysis data; The time model and the energy consumption model of the laser additive manufacturing process are set, and the target optimization model is constructed based on the time model and the energy consumption model; The energy consumption analysis of the laser additive manufacturing process is performed based on the adjustment range of the initial process parameter by using the time model and the energy consumption model combined with the orthogonal test to obtain the energy consumption analysis data; The adjustment parameter is determined based on the energy consumption analysis data and the target optimization model, and the initial process parameter is adjusted based on the adjustment parameter to obtain the target process parameter.

8. The component laser additive manufacturing method based on heterogenous material evolution analysis of claim 1, wherein, The laser additive manufacturing process of the target component is performed based on the target process parameter, including: The path planning of the laser additive manufacturing process is performed based on the geometric shape data and the spatial distribution data combined with the component forming requirement to obtain the path data; The machining instruction is generated based on the path data and the target process parameter, and the laser additive manufacturing equipment performs the laser additive manufacturing process of the target component based on the machining instruction.

9. A component laser additive manufacturing apparatus based on heterogeneous material evolution analysis, characterized by, The device includes: The micro-size analysis module is used for analyzing the target micro-size data of the heterogeneous material required by the target component; The geometric shape analysis module is used for performing geometric shape analysis on the heterogeneous material based on the pixel error probability compensation to obtain the geometric shape data; The spatial distribution analysis module is used for performing spatial distribution analysis on the heterogeneous material to obtain the spatial distribution data; The parameter analysis module is used for performing initial process parameter analysis of the target component laser additive manufacturing based on the target micro-size data, the geometric shape data and the spatial distribution data combined with the heterogeneous material structure evolution influence analysis to obtain the initial process parameter; The material performance analysis module is configured to perform a laser additive manufacturing simulation based on the initial process parameters, obtain a laser additive manufacturing simulation result, and perform a component material performance analysis based on the laser additive manufacturing simulation result to obtain component material performance analysis data. The parameter adjustment module is configured to adjust the initial process parameters based on the component material performance analysis data in combination with the energy consumption analysis to obtain target process parameters. The laser additive manufacturing module is configured to perform a laser additive manufacturing process on the target component based on the target process parameters. The method comprises: performing saliency processing and binarization processing on the target image of the heterogeneous material to obtain a binarized target saliency map; determining foreground pixel probability and background pixel probability of the binarized target saliency map based on a pixel probability model, and performing error probability compensation on the foreground pixel probability and the background pixel probability to obtain target foreground pixel probability and target background pixel probability; determining the region contour of the heterogeneous material in the target image based on the target foreground pixel probability and the target background pixel probability; and performing geometric shape analysis based on the region contour of the heterogeneous material to obtain geometric shape data.

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