High performance polygonal component manufacturing 3d printing technology method and system

Abstract

The present application relates to the technical field of 3D printing, and discloses a high-efficiency polygon component manufacturing 3D printing technology method and system, which comprises the following steps: identifying the internal cavity structure and the external contour complexity of a polygon component, dividing the dynamic support area and the core bearing area of the polygon component; designing a multi-density honeycomb structure corresponding to the core bearing area, generating a multi-density filling strategy of the multi-density honeycomb structure; compensating for the thermal expansion deviation value corresponding to the polygon component, setting the printing control parameters of a printing head; analyzing the coupling relationship between the material extrusion dynamics and the interlayer bonding efficiency of a 3D printer, setting the multi-axis printing path planning of 3D printing; controlling the 3D printer to print the polygon component, calculating the component surface roughness and the component internal porosity in the printing process, and performing real-time optimization processing on the printing control parameters and the multi-axis printing path planning, so as to finally print the best polygon component. The present application can improve the 3D printing efficiency of the polygon component.

Description

Technical Field

[0001] This invention relates to a high-efficiency 3D printing technology method and system for manufacturing polygonal components, belonging to the field of 3D printing technology. Background Technology

[0002] With the modern manufacturing industry continuously moving towards higher precision, higher efficiency, and higher performance, 3D printing technology, as a key means of advanced manufacturing, is playing an increasingly important role. The high-efficiency polygon component manufacturing 3D printing technology aims to achieve a fast, accurate, and high-quality manufacturing process for polygon components through innovative technical methods and system architecture. This technology integrates advanced digital model processing, intelligent printing parameter control, and optimized material distribution strategies, and is committed to improving the performance and production efficiency of polygon components in various industry applications.

[0003] Polygonal components are widely used in aerospace, automotive manufacturing, mechanical engineering and many other fields. Their complex geometry and strict performance requirements pose a severe challenge to manufacturing technology. Traditional manufacturing processes, such as casting and forging, are not only cumbersome and costly in terms of molds when dealing with complex polygonal components, but also make it difficult to achieve precise control over the internal structure and material distribution of the components. This results in long production cycles, low material utilization, and the manufactured components are unable to meet the requirements of high-end applications in terms of precision and mechanical properties.

[0004] Within the realm of 3D printing technology, conventional printing methods also have many limitations when dealing with polygonal components. In most cases, using uniform printing parameters and material infill patterns cannot fully consider the functional differences and stress distribution characteristics of different parts of the polygonal component. For example, for the critical load-bearing areas of the polygonal component, it is impossible to specifically enhance the material strength and optimize the structure; while in some non-critical parts, material may be overused, resulting in waste. This not only leads to poor overall performance of the printed polygonal component, making it prone to failure during use, but also results in low printing efficiency, which cannot meet the pace of large-scale production. Summary of the Invention

[0005] This invention provides a high-efficiency 3D printing technology method and system for manufacturing polygonal components, the main purpose of which is to improve the 3D printing efficiency of polygonal components.

[0006] To achieve the above objectives, the present invention provides a high-efficiency 3D printing technology method for manufacturing polygonal components, comprising:

[0007] Acquire the 3D topology data of the polygonal component to be printed and the 3D printer, identify the internal cavity structure and external contour complexity of the polygonal component, and combine the internal cavity structure and the external contour complexity to divide the dynamic support area and core bearing area of ​​the polygonal component.

[0008] Determine the printing efficiency constraints of the polygonal component, and based on the printing efficiency constraints, design the multi-density honeycomb structure corresponding to the core bearing area, and generate the multi-density filling strategy of the multi-density honeycomb structure;

[0009] The temperature field distribution data of the printing nozzle of the 3D printer and the vibration spectrum data of the forming platform are collected in real time. Based on the temperature field distribution data, the thermal expansion deviation value corresponding to the polygonal component is compensated, the geometric constraint conditions of the dynamic support area are analyzed, and the printing control parameters of the printing nozzle are set in combination with the geometric constraint conditions and the vibration spectrum data.

[0010] Based on the multi-density filling strategy and the thermal expansion deviation value, the coupling relationship between the material extrusion dynamics and interlayer bonding efficiency of the 3D printer is analyzed, and based on the coupling relationship, the multi-axis printing path planning of the 3D printer is set.

[0011] By combining the printing control parameters and the multi-axis printing path planning, the 3D printer is manipulated to print polygonal components, and the surface roughness and internal porosity of the components are calculated during the printing process. Based on the surface roughness and internal porosity of the components, the printing control parameters and the multi-axis printing path planning are optimized in real time, and finally the best polygonal components are printed.

[0012] Optionally, identifying the internal cavity structure and external contour complexity of the polygonal component includes:

[0013] The three-dimensional topology data corresponding to the polygonal component is detected, and the topology structure of the three-dimensional topology data is parsed to obtain three-dimensional mesh data;

[0014] Calculate the discrete Gaussian curvature corresponding to each grid vertex in the three-dimensional mesh data;

[0015] Based on the discrete Gaussian curvature, analyze the Gaussian curvature distribution heatmap corresponding to the three-dimensional mesh data;

[0016] Based on the Gaussian curvature distribution heatmap, high curvature regions in the three-dimensional mesh data are identified;

[0017] The three-dimensional mesh data is voxelized to obtain a binary voxel space;

[0018] Cavity detection is performed on the binary voxel space to obtain a set of cavity parameters;

[0019] Identify the region contour of the high curvature region and calculate the fractal dimension matrix corresponding to the region contour;

[0020] By combining the high curvature region and the fractal dimension matrix, the internal cavity structure and external contour complexity of the polygonal component are identified.

[0021] Optionally, calculating the discrete Gaussian curvature corresponding to each grid vertex in the three-dimensional mesh data includes:

[0022] Collect the adjacent triangle facets corresponding to each grid vertex in the three-dimensional mesh data;

[0023] Calculate the interior angle of each mesh vertex in the three-dimensional mesh data within the adjacent triangular facet;

[0024] Based on the adjacent triangle facets, the vertex-associated area corresponding to each grid vertex in the three-dimensional mesh data is calculated;

[0025] Combining the interior angles of the facets and the associated area of ​​the vertices, the discrete Gaussian curvature corresponding to each grid vertex in the 3D mesh data is calculated using the following formula:

[0026]

[0027] in, This represents the discrete Gaussian curvature corresponding to each grid vertex in the 3D mesh data. This represents the interior angle of the i-th adjacent triangle face of the a-th mesh vertex in the 3D mesh data. This represents the vertex-associated area of ​​the a-th vertex in the 3D mesh data, where a represents the sequence number of the mesh vertex, q represents the number of mesh vertices, and i represents the sequence number of the adjacent triangle facets.

[0028] Optionally, designing the multi-density honeycomb structure corresponding to the core bearing area based on the printing efficiency constraint includes:

[0029] Obtain the target material corresponding to the core bearing area, and query the material safety factor corresponding to the target material.

[0030] Based on the material safety factor, calculate the material strength threshold corresponding to the target material in the region;

[0031] Stress finite element analysis was performed on the core load-bearing area to obtain a stress distribution cloud map;

[0032] Based on the stress distribution cloud map and the material strength threshold, calculate the adjustment density value corresponding to the core bearing area;

[0033] Based on the adjusted density value and the stress distribution cloud map, a multi-density honeycomb structure corresponding to the core bearing area is designed.

[0034] Optionally, calculating the adjustment density value corresponding to the core load-bearing region by combining the stress distribution cloud map and the material strength threshold includes:

[0035] The test stress value corresponding to the core bearing area is extracted from the stress distribution cloud map;

[0036] Based on the test stress value and the material strength threshold, the adjusted density value corresponding to the core load-bearing area is calculated using the following formula:

[0037]

[0038] in, This indicates the adjusted density value corresponding to the core bearing area. Indicates the minimum density of the material. Indicates the test stress value. Indicates the material strength threshold. This indicates the maximum density of the material.

[0039] Optionally, compensating for the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data includes:

[0040] The temperature field distribution data is subjected to noise reduction processing to obtain filtered temperature data;

[0041] Based on the filtered temperature data, construct the temperature change matrix corresponding to the polygonal component;

[0042] The thermal expansion amount is calculated from the temperature change matrix to obtain the thermal expansion deviation matrix;

[0043] The thermal expansion deviation matrix is ​​geometrically mapped and corrected to obtain a compensated printing path;

[0044] The deformation of the compensated printing path is verified to obtain the thermal expansion deviation value corresponding to the polygonal component.

[0045] Optionally, setting the printing control parameters of the print head by combining the geometric constraints and the vibration spectrum data includes:

[0046] The vibration spectrum data is subjected to time-frequency conversion processing to obtain the vibration spectrum frequency domain signal;

[0047] Extract the signal's dominant frequency and amplitude characteristics from the vibration spectrum frequency domain signal;

[0048] Based on the signal's dominant frequency and amplitude characteristics, analyze the vibration risk level of the vibration spectrum data;

[0049] The geometric constraints are quantized to obtain the geometric constraint threshold.

[0050] Based on the vibration risk level and the geometric constraint threshold, the printing control parameters of the print head are set.

[0051] Optionally, the step of analyzing the coupling relationship between the material extrusion dynamics and interlayer bonding performance of the 3D printer based on the multi-density filling strategy and the thermal expansion deviation value includes:

[0052] Extract the filling strategy feature information from the multi-density filling strategy;

[0053] Based on the filling strategy feature information, analyze the evolution of the filling structure corresponding to the thermal expansion deviation value;

[0054] Query the key dynamic parameters of the material extrusion dynamics of the 3D printer;

[0055] Based on the evolution of the filling architecture and the dynamic key parameters, simulated interlayer bonding performance corresponding to the material extrusion dynamics is simulated.

[0056] Measure the actual interlayer bonding performance of the printed component corresponding to the multi-density filling strategy and the thermal expansion deviation value;

[0057] By combining the simulated interlayer bonding performance and the actual interlayer bonding performance, the coupling relationship between the material extrusion dynamics and interlayer bonding performance of the 3D printer is analyzed.

[0058] Optionally, the calculation of the component surface roughness and internal porosity during the printing process includes:

[0059] Acquire optical image data and X-ray scan data of components during the printing process;

[0060] Based on the optical image data of the component, the surface texture features of the component during the printing process are extracted;

[0061] Based on the surface texture features of the component, the surface roughness of the component during the printing process is calculated;

[0062] Based on the component's X-ray scanning data, the component's pore areas are determined during the printing process;

[0063] Based on the pore region of the component, the internal porosity of the component during the printing process is calculated.

[0064] To address the above problems, the present invention also provides a high-efficiency 3D printing technology system for manufacturing polygonal components, the system comprising:

[0065] The region division module is used to acquire the three-dimensional topology data of the polygonal component to be printed and the 3D printer, identify the internal cavity structure and external contour complexity of the polygonal component, and divide the dynamic support region and core bearing region of the polygonal component by combining the internal cavity structure and the external contour complexity.

[0066] The filling strategy generation module is used to determine the printing efficiency constraints of the polygonal component, design the multi-density honeycomb structure corresponding to the core bearing area based on the printing efficiency constraints, and generate the multi-density filling strategy of the multi-density honeycomb structure.

[0067] The printing control parameter setting module is used to collect the temperature field distribution data of the printing nozzle of the 3D printer and the vibration spectrum data of the forming platform in real time. Based on the temperature field distribution data, it compensates for the thermal expansion deviation value corresponding to the polygonal component, analyzes the geometric constraint conditions of the dynamic support area, and sets the printing control parameters of the printing nozzle in combination with the geometric constraint conditions and the vibration spectrum data.

[0068] The path planning module is used to analyze the coupling relationship between the material extrusion dynamics and interlayer bonding efficiency of the 3D printer based on the multi-density filling strategy and the thermal expansion deviation value, and to set the multi-axis printing path planning of the 3D printer based on the coupling relationship.

[0069] The printing optimization module is used to combine the printing control parameters and the multi-axis printing path planning to control the 3D printer to print polygonal components, and to calculate the surface roughness and internal porosity of the components during the printing process. Based on the surface roughness and internal porosity of the components, the module performs real-time optimization processing on the printing control parameters and the multi-axis printing path planning to finally print the best polygonal components.

[0070] Compared to the problems described in the background art, this invention, by identifying the internal cavity structure and external contour complexity of the polygonal component, can understand the support requirements and material filling focus of the polygonal component during printing, as well as determine the difficulty of printing path planning and parameter settings. This lays an important foundation for the subsequent division of the dynamic support area and core load-bearing area of ​​the polygonal component. Furthermore, by designing a multi-density honeycomb structure corresponding to the core load-bearing area based on the printing efficiency constraints, this invention can rationally allocate materials, reduce material waste, and significantly improve printing efficiency. Finally, by compensating for the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data, this invention can effectively improve the forming efficiency of the polygonal component. This invention improves precision, reduces dimensional errors and shape deformations caused by thermal expansion, and enhances component quality and performance. Furthermore, based on the multi-density infill strategy and thermal expansion deviation values, the invention analyzes the coupling relationship between the material extrusion dynamics and interlayer bonding efficiency of the 3D printer. This allows for understanding the impact of thermal expansion and infill density differences on interlayer bonding during material extrusion, thereby optimizing printing process parameters. This provides crucial information for setting the multi-axis printing path in subsequent 3D printing. Moreover, by calculating the surface roughness and internal porosity of the component during printing, the invention understands the immediate impact of printing process parameters on finished product quality, enabling rapid parameter adjustments and effectively avoiding defects caused by improper parameters. Therefore, the efficient polygonal component manufacturing 3D printing technology method and system provided by this invention can improve the 3D printing efficiency of polygonal components. Attached Figure Description

[0071] Figure 1 A flowchart illustrating a method for manufacturing high-efficiency polygonal components using 3D printing technology according to an embodiment of the present invention;

[0072] Figure 2 An example diagram of the density honeycomb structure in the efficient polygonal component manufacturing 3D printing technology method provided by the present invention;

[0073] Figure 3 This is a schematic diagram of a module for implementing the efficient polygonal component manufacturing 3D printing technology method according to an embodiment of the present invention.

[0074] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0075] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0076] This application provides a method for efficient 3D printing of polygonal components. The execution subject of this method includes, but is not limited to, at least one electronic device configured to execute the method provided in this application, such as a server or a terminal. In other words, the method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0077] Example 1:

[0078] Reference Figure 1 The diagram shown is a flowchart illustrating a method for manufacturing high-efficiency polygonal components using 3D printing technology according to an embodiment of the present invention. In this embodiment, the method includes:

[0079] S1. Obtain the 3D topology data of the polygonal component to be printed and the 3D printer, identify the internal cavity structure and external contour complexity of the polygonal component, and combine the internal cavity structure and the external contour complexity to divide the dynamic support area and core bearing area of ​​the polygonal component.

[0080] This invention, by identifying the internal cavity structure and external contour complexity of the polygonal component, can understand the support requirements and material filling focus during printing, as well as the difficulty of printing path planning and parameter settings. This lays an important foundation for the subsequent division of the dynamic support area and core load-bearing area of ​​the polygonal component. It should be noted that the 3D printer is the key execution device for printing polygonal components from virtual models to physical products. The internal cavity structure refers to the hollow areas within the polygonal component; its shape, size, location, and distribution affect the component's mechanical properties and the support design during printing, such as the support structure of a certain aero-engine. As a key load-bearing component, the frame has the following structural characteristics: Shape: Irregular polygonal frame structure, including complex curved surfaces and hollow areas; Dimensions: Length 280mm × Width 150mm × Height 200mm; Material requirements: Titanium alloy Ti-6Al-3V, which must meet aerospace-grade mechanical performance standards; Performance challenges: It needs to withstand alternating loads in a high-temperature environment of 300℃, and traditional casting processes are difficult to achieve a balance between lightweight and strength; The complexity of the external contour refers to the complexity of the external surface shape of the polygonal component, covering changes in surface curvature, concave and convex features, edges, etc., which determines the difficulty of printing path planning and the required printing parameters and post-processing level.

[0081] Specifically, identifying the internal cavity structure and external contour complexity of the polygonal component includes:

[0082] The three-dimensional topology data corresponding to the polygonal component is detected, and the topology structure of the three-dimensional topology data is parsed to obtain three-dimensional mesh data;

[0083] Calculate the discrete Gaussian curvature corresponding to each grid vertex in the three-dimensional mesh data;

[0084] Based on the discrete Gaussian curvature, analyze the Gaussian curvature distribution heatmap corresponding to the three-dimensional mesh data;

[0085] Based on the Gaussian curvature distribution heatmap, high curvature regions in the three-dimensional mesh data are identified;

[0086] The three-dimensional mesh data is voxelized to obtain a binary voxel space;

[0087] Cavity detection is performed on the binary voxel space to obtain a set of cavity parameters;

[0088] Identify the region contour of the high curvature region and calculate the fractal dimension matrix corresponding to the region contour;

[0089] By combining the high curvature region and the fractal dimension matrix, the internal cavity structure and external contour complexity of the polygonal component are identified.

[0090] It should be explained that the three-dimensional topological data is a comprehensive digital representation of the polygonal component, accurately recording its spatial shape, internal structure, and the relationships between its parts. For example, the number of vertices is 12,583, the number of faces is 23,962, the maximum size is 250mm x 180mm x 120mm, it contains three main irregular cavity structures internally, and the external contour has multiple high-curvature transition areas (such as the edges of connecting flanges and the intersections of reinforcing ribs). The three-dimensional mesh data is a specific representation of the three-dimensional topological data, approximating the geometry and spatial structure of the polygonal component through a series of interconnected vertices, edges, and faces. The discrete Gaussian curvature represents a quantitative index of the local curvature degree corresponding to each mesh vertex in the three-dimensional mesh data, reflecting the curvature characteristics of the surface near that vertex. The Gaussian curvature distribution heatmap is... The visualization method corresponding to the three-dimensional mesh data uses different colors to intuitively display the distribution of Gaussian curvature in the entire three-dimensional mesh data, making it easy to quickly identify high curvature and low curvature regions. The high curvature region is the part of the surface in the three-dimensional mesh data with a large degree of local curvature. These regions are geometrically characterized by sharp corners, protruding parts, or abrupt curvature changes. The binary voxel space is a representation of the three-dimensional mesh data after processing and transformation. It divides the three-dimensional space into voxel units and uses binary values ​​(usually 0 and 1) to indicate whether the voxel is occupied by the component. The cavity parameter set is a set of relevant parameters about the internal cavity in the binary voxel space, including information such as the location, size, shape, and volume of the cavity, used to describe and analyze the hollow structural characteristics inside the polygonal component.

[0091] Furthermore, the 3D topological data corresponding to the polygonal components can be detected using 3D modeling software or professional scanning equipment; the 3D topological data can be analyzed using a mesh generation algorithm to obtain 3D mesh data; based on the discrete Gaussian curvature, the Gaussian curvature distribution heatmap corresponding to the 3D mesh data can be analyzed using visualization programming tools or mathematical calculation software; based on the Gaussian curvature distribution heatmap, high curvature regions in the 3D mesh data can be identified using a threshold segmentation algorithm; the 3D mesh data can be voxelized using a voxelization algorithm to obtain a binary voxel space; and the binary voxel space can be analyzed using a voxel-based cavity detection algorithm. Cavity detection is performed in voxel space to obtain a set of cavity parameters; the region contour of the high curvature area can be identified by an edge detection algorithm, and the fractal dimension matrix corresponding to the region contour can be calculated by a fractal dimension calculation method; combining the high curvature area and the fractal dimension matrix, the internal cavity structure and external contour complexity of the polygonal component are identified. For example, the distribution of high curvature areas and the size of the fractal dimension are compared with a predetermined standard to determine the complexity of the internal cavity structure and the complexity level of the external contour. For example, if the high curvature areas are concentrated inside and the fractal dimension is large, the internal cavity structure is complex; if the external contour has few high curvature areas and a small fractal dimension, the external contour complexity is determined to be low.

[0092] Furthermore, as an optional embodiment of the present invention, calculating the discrete Gaussian curvature corresponding to each grid vertex in the three-dimensional mesh data includes:

[0093] Collect the adjacent triangle facets corresponding to each grid vertex in the three-dimensional mesh data;

[0094] Calculate the interior angle of each mesh vertex in the three-dimensional mesh data within the adjacent triangular facet;

[0095] Based on the adjacent triangle facets, the vertex-associated area corresponding to each grid vertex in the three-dimensional mesh data is calculated;

[0096] Combining the interior angles of the facets and the associated area of ​​the vertices, the discrete Gaussian curvature corresponding to each grid vertex in the 3D mesh data is calculated using the following formula:

[0097]

[0098] in, This represents the discrete Gaussian curvature corresponding to each grid vertex in the 3D mesh data. This represents the interior angle of the i-th adjacent triangle face of the a-th mesh vertex in the 3D mesh data. This represents the vertex-associated area of ​​the a-th vertex in the 3D mesh data, where a represents the sequence number of the mesh vertex, q represents the number of mesh vertices, and i represents the sequence number of the adjacent triangle facets.

[0099] It should be explained that the adjacent triangular facets are the triangular facets in the 3D mesh data that are directly connected to each mesh vertex and together form a 3D shape, forming a local geometric structure around that vertex; the facet interior angles are the interior angles of each mesh vertex in the 3D mesh data within the adjacent triangular facets directly connected to it, with that vertex as the common vertex, and their size and distribution reflect the characteristics of the local geometric shape; the vertex associated area is the sum of the areas of the adjacent triangular facets associated with each mesh vertex in the 3D mesh data, which reflects the size of the spatial range affected or covered by that vertex in the 3D structure and the characteristics of the local geometric structure.

[0100] Furthermore, the adjacent triangular faces corresponding to each grid vertex in the 3D mesh data can be collected through a traversal algorithm; the interior angles of each grid vertex in the adjacent triangular faces can be calculated through geometric operations related to trigonometric functions, such as obtaining the edge vectors of the adjacent triangular faces based on vector operations, and then using the cosine theorem, combining the magnitude of these edge vectors with the vector dot product, to calculate the size of each interior angle with the grid vertex as the common vertex; based on the adjacent triangular faces, the vertex-related area corresponding to each grid vertex in the 3D mesh data can be calculated by summing the areas of each adjacent triangular face.

[0101] This invention, by combining the internal cavity structure and the complexity of the external contour, divides the polygonal component into a dynamic support area and a core load-bearing area. This ensures precise support placement for complex structural areas during 3D printing, preventing printing collapse. Simultaneously, it prioritizes ensuring reasonable material distribution in the core load-bearing area, optimizing the overall mechanical properties of the component, and improving printing success rate and product quality. It should be explained that the dynamic support area is a specific region of the polygonal component that requires additional support during 3D printing due to its internal cavity structure being prone to collapse or its complex external contour, ensuring smooth printing. The core load-bearing area is a critical region of the polygonal component that bears the main mechanical load, determines the overall strength and stability of the component, and requires careful attention to material distribution and structural integrity. Furthermore, by combining the internal cavity structure and the complexity of the external contour, the dynamic support area and core load-bearing area of ​​the polygonal component are divided. For example, for areas with internal cavities and large changes in external contour curvature, their periphery and the suspended portion of the cavity are designated as dynamic support areas to ensure structural stability during printing. The area bearing the main external forces, with a relatively regular shape, and where materials need to be tightly arranged to ensure strength is defined as the core load-bearing area.

[0102] S2. Determine the printing efficiency constraints of the polygonal component, and based on the printing efficiency constraints, design the multi-density honeycomb structure corresponding to the core bearing area, and generate the multi-density filling strategy of the multi-density honeycomb structure.

[0103] This invention designs a multi-density honeycomb structure corresponding to the core load-bearing area based on the aforementioned printing efficiency constraints. This allows for the rational allocation of materials, reduces material waste, and significantly improves printing efficiency. It should be explained that the printing efficiency constraints are determined during the 3D printing process of the polygonal component, taking into account factors such as printing equipment performance, material properties, and process requirements. These constraints limit printing time, printing speed, and the number of printing layers to ensure efficient printing. The multi-density honeycomb structure, corresponding to the core load-bearing area, employs honeycomb structures of different densities based on the stress conditions and material strength requirements of different parts of this area. This design optimizes material distribution, improves mechanical properties, and balances printing efficiency. For specific examples of the multi-density honeycomb structure, please refer to the following diagram. Figure 2 The image shown is an example diagram of the density honeycomb structure in the efficient polygonal component manufacturing 3D printing technology method provided by this invention. It should be noted that in this invention... Figure 2 The example images presented are only for illustrative purposes of the efficient polygon component manufacturing 3D printing technology method, and are not limited to example images of the efficient polygon component manufacturing 3D printing technology method in different actual application scenarios. Furthermore, the printing efficiency constraints of the polygon component can be determined based on the equipment performance or the maximum printing time required by the user.

[0104] In detail, the design of the multi-density honeycomb structure corresponding to the core bearing area based on the printing efficiency constraint includes:

[0105] Obtain the target material corresponding to the core bearing area, and query the material safety factor corresponding to the target material.

[0106] Based on the material safety factor, calculate the material strength threshold corresponding to the target material in the region;

[0107] Stress finite element analysis was performed on the core load-bearing area to obtain a stress distribution cloud map;

[0108] Based on the stress distribution cloud map and the material strength threshold, calculate the adjustment density value corresponding to the core bearing area;

[0109] Based on the adjusted density value and the stress distribution cloud map, a multi-density honeycomb structure corresponding to the core bearing area is designed.

[0110] It should be explained that the target material for the region is the material selected to meet the printing process conditions corresponding to the core bearing area; the material safety factor is a coefficient that reduces the strength of the target material for the region; the material strength threshold is the maximum stress value that the target material for the region can withstand; the stress distribution cloud map is a visual representation of the stress magnitude and distribution in the core bearing area after stress finite element analysis, using different colors or gray levels to represent different stress values; the adjusted density value is a suitable density value obtained after optimizing and adjusting the density of the multi-density honeycomb structure in the core bearing area based on factors such as the stress distribution cloud map, material strength threshold, and material safety factor, so as to achieve reasonable material distribution and optimized component performance.

[0111] Furthermore, the target material for the core load-bearing region can be obtained by analyzing the usage scenarios, mechanical requirements, and 3D printing process compatibility of the polygonal component. The material safety factor corresponding to the target material can be queried using material industry standards. Based on the material safety factor, the material strength threshold corresponding to the target material is calculated. The standard strength value of the material is divided by the material safety factor to obtain the strength limit that the target material in that region can withstand under safety considerations. Stress finite element analysis can be performed on the core load-bearing region using finite element analysis software with input parameters such as the geometric model, material properties, load conditions, and constraint settings of the core load-bearing region, resulting in a stress distribution cloud map. Based on the adjusted density value and the stress distribution cloud map, a multi-density honeycomb structure corresponding to the core load-bearing region is designed. For example, the honeycomb structure density is increased in high-stress concentration areas to enhance load-bearing capacity, while the honeycomb structure density is decreased in low-stress areas. Simultaneously, the density of the honeycomb structure in different regions is precisely controlled according to the adjusted density value, ensuring that the multi-density honeycomb structure meets the mechanical performance requirements of the core load-bearing region.

[0112] Furthermore, as an optional embodiment of the present invention, the step of calculating the adjustment density value corresponding to the core bearing area by combining the stress distribution cloud map and the material strength threshold includes:

[0113] The test stress value corresponding to the core bearing area is extracted from the stress distribution cloud map;

[0114] Based on the test stress value and the material strength threshold, the adjusted density value corresponding to the core load-bearing area is calculated using the following formula:

[0115]

[0116] in, This indicates the adjusted density value corresponding to the core bearing area. Indicates the minimum density of the material. Indicates the test stress value. Indicates the material strength threshold. This indicates the maximum density of the material.

[0117] The formula works by calculating the adjusted density value for the core load-bearing area based on the test stress value and the material strength threshold, thus rationally allocating the material density. The underlying principle is that the ratio of the test stress value to the material strength threshold reflects the degree of stress relative to the threshold. This ratio is then multiplied by the difference between the maximum and minimum material density to obtain the density adjustment range required due to stress changes. Finally, the minimum density is added to obtain the adjusted density value adapted to the stress conditions of the core load-bearing area.

[0118] It should be explained that the test stress value is a specific numerical value in the stress distribution cloud map corresponding to the core bearing area, measuring the stress magnitude at different locations in that area, reflecting the stress condition borne by each part of the core bearing area; the minimum material density is the lowest permissible density value of the target material in the area, provided that the basic function of the component and certain safety requirements are met; the maximum material density is the highest density value that the target material in the area can achieve after considering factors such as printing process limitations, mechanical performance requirements, and overall structural rationality. Furthermore, the test stress value corresponding to the core bearing area can be extracted from the stress distribution cloud map using a data extraction tool compiled by a programming language; the minimum and maximum material densities can be obtained from material handbooks.

[0119] This invention, through the generation of a multi-density honeycomb structure with a multi-density filling strategy, can control the material distribution in different regions, significantly reducing material usage and manufacturing costs while meeting the mechanical performance requirements of the component. It should be explained that the multi-density filling strategy is a systematic plan for determining the honeycomb structure density and specific filling method in different parts of the multi-density honeycomb structure. Furthermore, based on the stress distribution cloud map, the density value can be adjusted according to different regions to plan the distribution and filling method of the honeycomb structure, thereby generating the multi-density filling strategy for the multi-density honeycomb structure.

[0120] S3. Real-time acquisition of temperature field distribution data of the 3D printer nozzle and vibration spectrum data of the forming platform; compensation of thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data; analysis of geometric constraints of the dynamic support area; and setting printing control parameters of the nozzle in combination with the geometric constraints and the vibration spectrum data.

[0121] This invention, by compensating for the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data, can effectively improve the forming accuracy of the polygonal component, reduce dimensional errors and shape deformation caused by thermal expansion, and improve the quality and performance of the component. It should be explained that the printing nozzle is the key execution component of the 3D printer used to extrude printing material and build it up layer by layer according to a preset path; the temperature field distribution data is the distribution and change data of the temperature at different parts of the printing nozzle during operation, reflecting the thermal state; the forming platform is the basic component of the 3D printer that carries the printed component and provides a stable support plane; the vibration spectrum data is a data set of information such as the vibration amplitude of the forming platform at different frequencies during the printing process, reflecting its vibration characteristics. Furthermore, the real-time acquisition of the temperature field distribution data of the printing nozzle and the vibration spectrum data of the forming platform can be achieved through high-precision temperature sensors and vibration acceleration sensors.

[0122] Specifically, the compensation for the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data includes:

[0123] The temperature field distribution data is subjected to noise reduction processing to obtain filtered temperature data;

[0124] Based on the filtered temperature data, construct the temperature change matrix corresponding to the polygonal component;

[0125] The thermal expansion amount is calculated from the temperature change matrix to obtain the thermal expansion deviation matrix;

[0126] The thermal expansion deviation matrix is ​​geometrically mapped and corrected to obtain a compensated printing path;

[0127] The deformation of the compensated printing path is verified to obtain the thermal expansion deviation value corresponding to the polygonal component.

[0128] It should be explained that the filtered temperature data is the temperature field distribution data after noise reduction processing; the temperature change matrix is ​​a matrix constructed based on the filtered temperature data corresponding to the polygonal component, describing the temperature change characteristics of the polygonal component at different positions and times; the thermal expansion deviation matrix is ​​a matrix calculated from the temperature change matrix using the amount of thermal expansion, reflecting the deviation of thermal expansion of various parts of the polygonal component due to temperature changes; and the compensated printing path is the path obtained after adjusting the printing path of the 3D printer using the thermal expansion deviation matrix to compensate for the impact of thermal expansion deviation on printing accuracy.

[0129] Furthermore, the temperature field distribution data can be denoised using a Fourier transform algorithm to obtain filtered temperature data. Based on the filtered temperature data, a temperature change matrix corresponding to the polygonal component is constructed according to the spatial coordinates and time dimension relationship of the polygonal component. The thermal expansion amount can be calculated on the temperature change matrix using the material's thermal expansion coefficient combined with the relevant formula for temperature change, resulting in a thermal expansion deviation matrix. For example, for each temperature change data point in the matrix, multiplying by the material's thermal expansion coefficient and considering the initial size of the component yields the thermal expansion amount at the corresponding location. The thermal expansion amounts at all locations are then integrated to form the thermal expansion deviation matrix. The deviation values ​​in the thermal expansion deviation matrix can be mapped to the printing path coordinate system to calculate the thermal expansion deviation. The difference matrix is ​​used for geometric mapping correction to obtain the compensated printing path. For example, based on the correspondence between the positions represented by each element in the thermal expansion deviation matrix and the coordinates of the printing path, the deviation value is converted into a coordinate offset, and the original printing path coordinates are adjusted accordingly to obtain the compensated printing path. The deformation verification of the compensated printing path can be performed by simulating the printing process and comparing it with the ideal model to obtain the thermal expansion deviation value corresponding to the polygonal component. For example, professional 3D printing simulation software can be used to simulate the forming process of the polygonal component based on the compensated printing path. The simulated formed component is compared with the designed ideal model point by point in terms of size, shape, etc., and error analysis is performed. The thermal expansion deviation value corresponding to the polygonal component is calculated by comprehensively considering the comparison results.

[0130] This invention analyzes the geometric constraints of the dynamic support region to grasp its structural characteristics, providing crucial spatial constraints for the printing process. By combining these geometric constraints with vibration spectrum data, the printing control parameters of the print head are set, thereby optimizing the printing path and ensuring accurate deposition of printing material in the dynamic support region. This improves the forming quality and precision of polygonal components, reduces the negative impact of vibration interference on the printing effect, and further allows for precise modeling of the dynamic support region using 3D modeling software. Computer-aided analysis techniques are then employed, combining the mechanical principles of the components with actual usage scenarios, to analyze the geometric constraints of the dynamic support region from multiple aspects, including boundary conditions, spatial constraints, and assembly relationships.

[0131] Specifically, setting the printing control parameters of the print head by combining the geometric constraints and the vibration spectrum data includes:

[0132] The vibration spectrum data is subjected to time-frequency conversion processing to obtain the vibration spectrum frequency domain signal;

[0133] Extract the signal's dominant frequency and amplitude characteristics from the vibration spectrum frequency domain signal;

[0134] Based on the signal's dominant frequency and amplitude characteristics, analyze the vibration risk level of the vibration spectrum data;

[0135] The geometric constraints are quantized to obtain the geometric constraint threshold.

[0136] Based on the vibration risk level and the geometric constraint threshold, the printing control parameters of the print head are set.

[0137] It should be explained that the vibration spectrum frequency domain signal is the frequency domain representation of the vibration spectrum data after Fourier transform. The signal main frequency and signal amplitude characteristics are characteristic parameters in the vibration spectrum frequency domain signal that can reflect the main frequency components of the signal and the vibration intensity at the corresponding frequency. The vibration risk level represents a quantitative index of the potential vibration hazard level obtained by comprehensively evaluating the vibration spectrum data based on its frequency, amplitude and other characteristics. The geometric constraint threshold is a critical value used in the geometric constraint conditions to define the acceptable range of dynamic support area in terms of geometric parameters.

[0138] Furthermore, the vibration spectrum data can be processed by time-frequency conversion using Fast Fourier Transform to obtain the vibration spectrum frequency domain signal; the signal dominant frequency and signal amplitude characteristics in the vibration spectrum frequency domain signal can be extracted using a spectrum peak detection algorithm; based on the signal dominant frequency and signal amplitude characteristics, the vibration risk level of the vibration spectrum data can be analyzed with reference to preset vibration risk assessment rules, such as substituting the signal dominant frequency and amplitude characteristics into the mathematical model of the risk assessment rules, and determining the specific level of low, medium, or high vibration risk based on the risk interval of the model output result; the geometric constraint conditions can be quantified using statistical analysis methods to obtain geometric constraint thresholds, such as collecting a large amount of geometric data about the dynamic support area under different working conditions, using statistical quantities such as mean and standard deviation to analyze the data distribution characteristics, and determining the maximum and minimum boundary values ​​that meet specific reliability requirements as geometric constraint thresholds based on the analysis results; combined with the vibration risk level and the geometric constraint thresholds, the printing control parameters of the print head can be set, such as improving the stability of the printing speed and adjusting the accuracy of the extrusion volume based on high vibration risk and strict geometric constraints.

[0139] S3. Based on the multi-density filling strategy and the thermal expansion deviation value, analyze the coupling relationship between the material extrusion dynamics and interlayer bonding efficiency of the 3D printer, and set the multi-axis printing path planning of the 3D printer based on the coupling relationship.

[0140] Based on the multi-density filling strategy and the thermal expansion deviation value, this invention analyzes the coupling relationship between the material extrusion dynamics and interlayer bonding performance of the 3D printer. It can grasp the influence of thermal expansion and filling density differences on interlayer bonding during the material extrusion process, thereby optimizing the printing process parameters and providing an important basis for the subsequent multi-axis printing path planning of the 3D printer. It should be explained that the coupling relationship is the inherent interactive connection between the material extrusion dynamics and interlayer bonding performance of the 3D printer.

[0141] In detail, the analysis of the coupling relationship between the material extrusion dynamics and interlayer bonding performance of the 3D printer based on the multi-density filling strategy and the thermal expansion deviation value includes:

[0142] Extract the filling strategy feature information from the multi-density filling strategy;

[0143] Based on the filling strategy feature information, analyze the evolution of the filling structure corresponding to the thermal expansion deviation value;

[0144] Query the key dynamic parameters of the material extrusion dynamics of the 3D printer;

[0145] Based on the evolution of the filling architecture and the dynamic key parameters, simulated interlayer bonding performance corresponding to the material extrusion dynamics is simulated.

[0146] Measure the actual interlayer bonding performance of the printed component corresponding to the multi-density filling strategy and the thermal expansion deviation value;

[0147] By combining the simulated interlayer bonding performance and the actual interlayer bonding performance, the coupling relationship between the material extrusion dynamics and interlayer bonding performance of the 3D printer is analyzed.

[0148] It should be explained that the infill strategy feature information refers to the set of specific attributes and parameters in the multi-density infill strategy that reflect the infill method, density distribution law, and related to the printed structure; the infill architecture evolution refers to the changes in the shape, size, and spatial distribution of the infill structure caused by the thermal expansion deviation value over time or during the printing process; the dynamic key parameters are key variables in the material extrusion dynamics of the 3D printer that can significantly affect the material extrusion process and quality, such as extrusion speed, temperature, and flow rate; the simulated interlayer bonding performance is a quantitative indicator or evaluation result reflecting the interlayer bonding effect under specific infill architecture and key parameters during the material extrusion process, obtained by modeling and simulation calculation based on the infill architecture evolution and the dynamic key parameters corresponding to the material extrusion dynamics; the actual interlayer bonding performance is the actual measurement result obtained by actual printing and experimental testing of the printed component corresponding to the multi-density infill strategy and the thermal expansion deviation value, truly reflecting the bonding strength and performance between layers in the printed component.

[0149] Furthermore, the infill strategy feature information in the multi-density infill strategy can be extracted by parsing the 3D printing model file and related parameter setting documents; based on the infill strategy feature information, the evolution of the infill structure corresponding to the thermal expansion deviation value can be analyzed using material thermal expansion theory and finite element simulation software; the dynamic key parameters of the 3D printer's material extrusion dynamics can be monitored and queried in real time by reading printer control system data and connecting sensors; based on the infill structure evolution and the dynamic key parameters, the simulated interlayer bonding efficiency corresponding to the material extrusion dynamics can be simulated by constructing a multiphysics numerical model including material rheology, heat transfer, and contact mechanics; the actual interlayer bonding efficiency of the printed component corresponding to the multi-density infill strategy and the thermal expansion deviation value can be measured by tensile, shear, and peel mechanical property test experiments; combining the simulated interlayer bonding efficiency and the actual interlayer bonding efficiency, the coupling relationship between the 3D printer's material extrusion dynamics and interlayer bonding efficiency can be analyzed, such as comparing simulation and actual results to clarify the specific influence of dynamic parameters such as extrusion speed and temperature on performance indicators such as interlayer bonding strength and uniformity under different infill structures, as well as the feedback requirements of interlayer bonding efficiency on material extrusion dynamic parameters.

[0150] This invention, based on the aforementioned coupling relationship, sets up a multi-axis printing path for 3D printing. By adjusting the nozzle's movement trajectory along multiple axes according to the interaction between material extrusion dynamics and interlayer bonding efficiency, it ensures a high degree of fit between material extrusion volume and interlayer bonding requirements at each printing position, effectively improving the interlayer bonding strength of the printed parts. It should be explained that the multi-axis printing path planning involves designing and planning the nozzle's movement trajectory along multiple coordinate axes in 3D printing. Furthermore, based on the aforementioned coupling relationship, the multi-axis printing path planning allows for the following: in areas with high interlayer bonding requirements, the nozzle is planned to move at a slower speed and smaller intervals while appropriately increasing the extrusion volume to ensure full material fusion; conversely, in areas with relatively low bonding requirements, the nozzle's movement speed is increased to accelerate the printing process, achieving a balance between printing efficiency and quality.

[0151] S5. Combining the printing control parameters and the multi-axis printing path planning, the 3D printer is controlled to print polygonal components, and the surface roughness and internal porosity of the components are calculated during the printing process. Based on the surface roughness and internal porosity of the components, the printing control parameters and the multi-axis printing path planning are optimized in real time to finally print the best polygonal components.

[0152] This invention calculates the surface roughness and internal porosity of components during the printing process to understand the immediate impact of printing process parameters on the quality of the finished product. This allows for rapid parameter adjustment and effectively avoids defective products caused by improper parameters. It should be noted that the surface roughness of the component represents the degree of irregularity of the micro-geometry of the component surface during the printing process, reflecting the smoothness or roughness of the printed surface. The internal porosity of the component represents the proportion of the internal pore volume of the component to the total volume during the printing process, reflecting the density and pore distribution inside the printed structure.

[0153] Specifically, the calculation of the component surface roughness and internal porosity during the printing process includes:

[0154] Acquire optical image data and X-ray scan data of components during the printing process;

[0155] Based on the optical image data of the component, the surface texture features of the component during the printing process are extracted;

[0156] Based on the surface texture features of the component, the surface roughness of the component during the printing process is calculated;

[0157] Based on the component's X-ray scanning data, the component's pore areas are determined during the printing process;

[0158] Based on the pore region of the component, the internal porosity of the component during the printing process is calculated.

[0159] It should be explained that the optical image data and the ray scan data of the component are information carriers reflecting the surface and internal structure of the component, obtained by optical imaging equipment and ray scanning equipment respectively during the printing process; the surface texture features of the component are specific information determined by the micro-geometry and structure of the component surface during the printing process, which can be extracted by optical image data and used to describe surface properties; the pore area of ​​the component is an area that appears in the ray scan data during the printing process, which has different ray absorption and scattering characteristics from the solid part due to the existence of voids or holes inside the component.

[0160] Furthermore, high-resolution optical cameras and compatible X-ray scanning devices can be strategically placed next to the 3D printer to acquire optical image data and X-ray scan data of the component during the printing process. Based on the optical image data, image recognition algorithms can be used to process the images through grayscale conversion, filtering, and edge detection to extract the surface texture features of the component during printing. Based on these surface texture features, mathematical algorithms such as power spectral density analysis, combined with a roughness evaluation model, can be used to calculate the surface roughness of the component during printing. Based on the X-ray scan data, image processing techniques such as threshold segmentation and morphological operations can be applied to determine the pore regions of the component during printing. Based on these pore regions, the internal porosity of the component during printing can be calculated; the porosity is calculated as the ratio of pore volume to the total volume of the component. Accurate porosity values ​​can be obtained by performing 3D reconstruction and volume calculation of the pore regions, as well as measuring the volume of the entire component.

[0161] This invention optimizes the printing control parameters and multi-axis printing path planning in real time based on the surface roughness and internal porosity of the component, ultimately printing the optimal polygonal component and improving the 3D printing efficiency of polygonal components. Specifically, the comparison data of surface roughness / porosity before and after optimization can be referred to in the table below. It should be noted that in this invention, the comparison data table presented below is only for data reference of the efficient polygonal component manufacturing 3D printing technology method, and is not limited to data reference of the efficient polygonal component manufacturing 3D printing technology method in different actual application scenarios.

[0162]

[0163] Compared to the problems described in the background art, this invention, by identifying the internal cavity structure and external contour complexity of the polygonal component, can understand the support requirements and material filling focus of the polygonal component during printing, as well as determine the difficulty of printing path planning and parameter settings. This lays an important foundation for the subsequent division of the dynamic support area and core load-bearing area of ​​the polygonal component. Furthermore, by designing a multi-density honeycomb structure corresponding to the core load-bearing area based on the printing efficiency constraints, this invention can rationally allocate materials, reduce material waste, and significantly improve printing efficiency. Finally, by compensating for the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data, this invention can effectively improve the forming efficiency of the polygonal component. This invention improves precision, reduces dimensional errors and shape deformations caused by thermal expansion, and enhances component quality and performance. Furthermore, based on the multi-density infill strategy and thermal expansion deviation values, the invention analyzes the coupling relationship between the material extrusion dynamics and interlayer bonding efficiency of the 3D printer. This allows for understanding the impact of thermal expansion and infill density differences on interlayer bonding during material extrusion, thereby optimizing printing process parameters. This provides crucial information for setting the multi-axis printing path in subsequent 3D printing. Moreover, by calculating the surface roughness and internal porosity of the component during printing, the invention understands the immediate impact of printing process parameters on finished product quality, enabling rapid parameter adjustments and effectively avoiding defects caused by improper parameters. Therefore, the efficient polygonal component manufacturing 3D printing technology method and system provided by this invention can improve the 3D printing efficiency of polygonal components.

[0164] Example 2:

[0165] like Figure 3 The diagram shown is a functional block diagram of a high-efficiency polygon component manufacturing 3D printing technology system according to the present invention.

[0166] The efficient polygon component manufacturing 3D printing technology system 300 described in this invention can be installed in an electronic device. Depending on the functions implemented, the efficient polygon component manufacturing 3D printing technology system may include a region division module 301, an infill strategy generation module 302, a printing control parameter setting module 303, a path planning module 303, and a printing optimization module 305. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0167] In this embodiment of the invention, the functions of each module / unit are as follows:

[0168] The region division module 301 is used to acquire the three-dimensional topology data of the polygonal component to be printed and the 3D printer, identify the internal cavity structure and external contour complexity of the polygonal component, and divide the dynamic support region and core bearing region of the polygonal component by combining the internal cavity structure and the external contour complexity.

[0169] The filling strategy generation module 302 is used to determine the printing efficiency constraints of the polygonal component, design the multi-density honeycomb structure corresponding to the core bearing area based on the printing efficiency constraints, and generate the multi-density filling strategy of the multi-density honeycomb structure.

[0170] The printing control parameter setting module 303 is used to collect the temperature field distribution data of the printing nozzle of the 3D printer and the vibration spectrum data of the forming platform in real time. Based on the temperature field distribution data, it compensates for the thermal expansion deviation value corresponding to the polygonal component, analyzes the geometric constraint conditions of the dynamic support area, and sets the printing control parameters of the printing nozzle in combination with the geometric constraint conditions and the vibration spectrum data.

[0171] The path planning module 303 is used to analyze the coupling relationship between the material extrusion dynamics and interlayer bonding efficiency of the 3D printer based on the multi-density filling strategy and the thermal expansion deviation value, and to set the multi-axis printing path planning of the 3D printer based on the coupling relationship.

[0172] The printing optimization module 305 is used to combine the printing control parameters and the multi-axis printing path planning to control the 3D printer to print polygonal components, and to calculate the surface roughness and internal porosity of the components during the printing process. Based on the surface roughness and internal porosity of the components, the printing control parameters and the multi-axis printing path planning are optimized in real time to finally print the best polygonal components.

[0173] In detail, the modules in the high-efficiency polygonal component manufacturing 3D printing technology system 300 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method used is the same as the efficient polygonal component manufacturing 3D printing technology described above, and it can produce the same technical effect, so it will not be repeated here.

[0174] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A high efficiency polygonal member manufacturing 3D printing technique method characterized by, The method comprises: acquiring three-dimensional topological data of a polygonal component to be printed and a 3D printer, identifying internal cavity structure and external contour complexity of the polygonal component, combining the internal cavity structure and the external contour complexity, and dividing a dynamic support area and a core bearing area of the polygonal component; determining a printing efficiency constraint condition of the polygonal component, designing a multi-density honeycomb structure corresponding to the core bearing area based on the printing efficiency constraint condition, and generating a multi-density filling strategy of the multi-density honeycomb structure; real-time acquisition of temperature field distribution data of a printing nozzle of the 3D printer and vibration spectrum data of a forming platform, compensation of a thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data, analysis of geometric constraint conditions of the dynamic support area, and setting of printing control parameters of the printing nozzle in combination with the geometric constraint conditions and the vibration spectrum data; wherein the compensation of the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data comprises: noise reduction processing of the temperature field distribution data to obtain filtered temperature data; construction of a temperature change matrix corresponding to the polygonal component based on the filtered temperature data; thermal expansion amount calculation of the temperature change matrix to obtain a thermal expansion deviation matrix; geometric mapping correction of the thermal expansion deviation matrix to obtain a compensated printing path; deformation verification of the compensated printing path to obtain the thermal expansion deviation value corresponding to the polygonal component; analysis of a coupling correlation between material extrusion dynamics and interlayer bonding efficiency of the 3D printer according to the multi-density filling strategy and the thermal expansion deviation value, and setting of a multi-axis printing path planning of the 3D printing based on the coupling correlation; combination of the printing control parameters and the multi-axis printing path planning, control of the 3D printer to print the polygonal component, and calculation of component surface roughness and component internal porosity in the printing process, real-time optimization processing of the printing control parameters and the multi-axis printing path planning based on the component surface roughness and the component internal porosity, and finally printing of the best polygonal component.

2. The high performance polygonal member manufacturing 3D printing technique method as claimed in claim 1, wherein, The identification of the internal cavity structure and the external contour complexity of the polygonal component comprises: detection of three-dimensional topological data corresponding to the polygonal component, topological structure analysis of the three-dimensional topological data to obtain three-dimensional grid data; calculation of a discrete Gaussian curvature corresponding to each grid vertex in the three-dimensional grid data; analysis of a Gaussian curvature distribution thermodynamic map corresponding to the three-dimensional grid data based on the discrete Gaussian curvature; identification of a high-curvature area in the three-dimensional grid data based on the Gaussian curvature distribution thermodynamic map; voxelization processing of the three-dimensional grid data to obtain a binary voxel space; cavity detection of the binary voxel space to obtain a cavity parameter set; identification of a region contour of the high-curvature area, calculation of a fractal dimension matrix corresponding to the region contour; combination of the high-curvature area and the fractal dimension matrix to identify the internal cavity structure and the external contour complexity of the polygonal component.

3. The high performance polygonal member manufacturing 3D printing technique method as claimed in claim 2, wherein, The calculating the discrete Gaussian curvature corresponding to each mesh vertex in the three-dimensional mesh data comprises: Collecting the adjacent triangular patches corresponding to each mesh vertex in the three-dimensional mesh data; Calculating the in-patch angle of each mesh vertex in the three-dimensional mesh data in the adjacent triangular patches; Based on the adjacent triangular patches, the vertex correlation area corresponding to each mesh vertex in the three-dimensional mesh data is calculated; Combining the in-patch angle and the vertex correlation area, the discrete Gaussian curvature corresponding to each mesh vertex in the three-dimensional mesh data is calculated by the following formula: wherein, represents the discrete Gaussian curvature corresponding to each mesh vertex in the three-dimensional mesh data, represents the in-patch angle of the a-th mesh vertex in the i-th adjacent triangular patch in the three-dimensional mesh data, represents the vertex-associated area of the a-th mesh vertex in the three-dimensional mesh data, a represents the serial number corresponding to the mesh vertex, q represents the quantity corresponding to the mesh vertex, and i represents the serial number of the adjacent triangular patch.

4. The high performance polygonal member manufacturing 3D printing technique method as claimed in claim 1, wherein, The design of the multi-density honeycomb structure corresponding to the core bearing area based on the printing efficiency constraint condition comprises: Obtaining the region target material corresponding to the core bearing area, and querying the material safety factor corresponding to the region target material; Based on the material safety factor, the material strength threshold value corresponding to the region target material is calculated; Performing stress finite element analysis on the core bearing area to obtain a stress distribution cloud map; Combining the stress distribution cloud map and the material strength threshold value, the adjustment density value corresponding to the core bearing area is calculated; Based on the adjustment density value and the stress distribution cloud map, the multi-density honeycomb structure corresponding to the core bearing area is designed.

5. The high performance polygonal member manufacturing 3D printing technique method as claimed in claim 4, wherein, The combination of the stress distribution cloud map and the material strength threshold value to calculate the adjustment density value corresponding to the core bearing area comprises: Extracting the test stress value corresponding to the core bearing area from the stress distribution cloud map; Based on the test stress value and the material strength threshold value, the adjustment density value corresponding to the core bearing area is calculated by the following formula: wherein, represents the adjusted density value corresponding to the core bearing area, represents the minimum density of the material, represents the test stress value, represents the material strength threshold value, represents the maximum density of the material.

6. The efficient polygonal member manufacturing 3D printing technique method as claimed in claim 1, wherein, The combination of the geometric constraint condition and the vibration spectrum data to set the printing control parameters of the printing nozzle comprises: Performing time-frequency conversion processing on the vibration spectrum data to obtain a vibration spectrum frequency domain signal; Extracting the signal main frequency and signal amplitude characteristics in the vibration spectrum frequency domain signal; Based on the signal main frequency and the signal amplitude characteristics, the vibration risk level of the vibration spectrum data is analyzed; Quantitative processing of the geometric constraint condition to obtain a geometric constraint threshold value; Combining the vibration risk level and the geometric constraint threshold value, the printing control parameters of the printing nozzle are set.

7. The efficient polygonal member manufacturing 3D printing technique method as claimed in claim 1, wherein, The analysis of the coupling correlation between the material extrusion dynamics and the interlayer bonding efficiency of the 3D printer according to the multi-density filling strategy and the thermal expansion deviation value comprises: Extracting the filling strategy feature information in the multi-density filling strategy; Based on the filling strategy feature information, the filling architecture evolution corresponding to the thermal expansion deviation value is analyzed; Querying the dynamic key parameters of the material extrusion dynamics of the 3D printer; Based on the filling architecture evolution and the dynamic key parameters, the simulated interlayer bonding efficiency corresponding to the material extrusion dynamics is simulated; Measuring the actual interlayer bonding efficiency of the printed component corresponding to the multi-density filling strategy and the thermal expansion deviation value; Combining the simulated interlayer bonding efficiency and the actual interlayer bonding efficiency, the coupling correlation between the material extrusion dynamics and the interlayer bonding efficiency of the 3D printer is analyzed.

8. The high performance polygonal member manufacturing 3D printing technique method as claimed in claim 1, wherein, The method comprises the following steps: Collecting optical image data and ray scanning data of the component during printing; Extracting surface texture features of the component during printing based on the optical image data; Calculating the surface roughness of the component during printing based on the surface texture features; Determining the pore region of the component during printing based on the ray scanning data; Calculating the internal porosity of the component during printing based on the pore region.

9. A high performance polygonal member manufacturing 3D printing technology system, characterized by, The system comprises: A region division module for obtaining three-dimensional topological data of a polygonal component to be printed and a 3D printer, identifying the internal cavity structure and the external contour complexity of the polygonal component, and dividing the dynamic support region and the core bearing region of the polygonal component based on the internal cavity structure and the external contour complexity; A filling strategy generation module for determining the printing efficiency constraint condition of the polygonal component, designing a multi-density honeycomb structure corresponding to the core bearing region based on the printing efficiency constraint condition, and generating a multi-density filling strategy of the multi-density honeycomb structure; A printing control parameter setting module for real-time acquisition of temperature field distribution data of a printing nozzle of the 3D printer and vibration spectrum data of a forming platform, compensation of the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data, analysis of the geometric constraint condition of the dynamic support region, and setting of the printing control parameters of the printing nozzle based on the geometric constraint condition and the vibration spectrum data; The compensation of the thermal expansion deviation value corresponding to the polygonal component based on the temperature field distribution data comprises: Noise reduction processing of the temperature field distribution data to obtain filtered temperature data; Construction of a temperature change matrix corresponding to the polygonal component based on the filtered temperature data; Thermal expansion amount calculation of the temperature change matrix to obtain a thermal expansion deviation matrix; Geometric mapping correction of the thermal expansion deviation matrix to obtain a compensated printing path; Deformation verification of the compensated printing path to obtain the thermal expansion deviation value corresponding to the polygonal component; A path planning module for analyzing the coupling relationship between material extrusion dynamics and interlayer adhesion efficiency of the 3D printer based on the multi-density filling strategy and the thermal expansion deviation value, and setting the multi-axis printing path planning of the 3D printing based on the coupling relationship; A printing optimization module for controlling the 3D printer to print the polygonal component in combination with the printing control parameters and the multi-axis printing path planning, calculating the surface roughness and internal porosity of the component during printing, and performing real-time optimization processing on the printing control parameters and the multi-axis printing path planning based on the surface roughness and the internal porosity of the component, and finally printing the best polygonal component.

Images