Adaptive Object Boundary Enhancement 3D Printing Technology Method and Integrated System

Abstract

The present invention relates to the field of 3D printing technology, and discloses an adaptive object boundary strengthening 3D printing technology method and an integrated system, including: determining high-stress regions and boundary regions of a 3D printed model; constructing a multi-scale network of the 3D printed model, defining multi-region printing materials of the 3D printed model, and determining a material transformation gradient of the multi-region printing materials; constructing a sensing network and a printing device of a target to be printed, and defining a multi-scale boundary strengthening path of the printing device; using the sensing network to collect printing data of the target to be printed, calculating an idle travel distance of the printing device, optimizing the multi-scale boundary strengthening path to obtain an optimized multi-scale path; analyzing a current printing quality of the target to be printed, defining optimized printing parameters of the target to be printed, and performing adaptive object boundary strengthening of the target to be printed based on the optimized printing parameters. The present invention can improve the boundary strengthening effect on the target to be printed.

Description

Technical Field

[0001] The present invention relates to an adaptive object boundary reinforcement 3D printing technology method and an integrated system, belonging to the technical field of 3D printing. Background Art

[0002] Adaptive object boundary reinforcement 3D printing technology refers to a printing technology that uses advanced software algorithms and 3D printing hardware to automatically adjust printing parameters and material properties according to the geometric features of the printed object and the expected usage conditions, so as to achieve enhanced physical properties (such as strength, toughness, durability, etc.) at the boundaries or high-stress regions of the object. The application of adaptive object boundary reinforcement 3D printing technology can greatly promote the development of additive manufacturing technology, enabling 3D printing to produce more complex and high-performance parts and products.

[0003] Traditional adaptive object boundary reinforcement 3D printing usually relies on preset printing parameters and single material properties, and enhances the boundary region by simple geometric modification or adding support structures. This method often lacks pertinence and flexibility, resulting in low printing efficiency, serious material waste, and difficulty in achieving precise reinforcement of complex models. Summary of the Invention

[0004] The present invention provides an adaptive object boundary reinforcement 3D printing technology method and an integrated system, and its main purpose is to improve the boundary reinforcement effect on the target to be printed.

[0005] To achieve the above object, an adaptive object boundary reinforcement 3D printing technology method provided by the present invention includes:

[0006] Obtain the 3D printing model of the target to be printed, analyze the geometric features of the 3D printing model, where the geometric features include curvature, thickness, and boundary, and determine the high-stress region and boundary region of the 3D printing model according to the geometric features;

[0007] According to the high-stress region and boundary region, construct a multi-scale network of the 3D printing model, based on the multi-scale network, define the multi-region printing materials of the 3D printing model, and determine the material transformation gradient of the multi-region printing materials;

[0008] Construct the sensing network and printing device of the target to be printed, and define the multi-scale boundary reinforcement path of the printing device according to the multi-scale network;

[0009] Based on the multi-scale boundary strengthening path, the material transformation gradient, the printing device, and the multi-region printing material, use the sensing network to collect the printing data of the target to be printed. According to the printing data, calculate the idle travel distance of the printing device. According to the idle travel distance, optimize the multi-scale boundary strengthening path to obtain an optimized multi-scale path;

[0010] Based on the optimized multi-scale boundary strengthening path, analyze the current printing quality of the target to be printed. According to the current printing quality, define the optimized printing parameters of the target to be printed. Based on the optimized printing parameters, perform adaptive object boundary strengthening of the target to be printed.

[0011] Optionally, the analysis of the geometric features of the printed 3D model includes:

[0012] Perform smoothing processing on the printed 3D model to obtain a smoothed 3D model;

[0013] Perform surface subdivision on the smoothed 3D model to obtain a surface-subdivided 3D model;

[0014] Calculate the curvature of the surface-subdivided 3D model;

[0015] Analyze the closed coefficient of the printed 3D model;

[0016] When the closed coefficient meets the preset closed threshold, perform offset on the printed 3D model to obtain an offset 3D model;

[0017] Calculate the surface volume between the offset 3D model and the printed 3D model;

[0018] Based on the surface volume, determine the thickness of the printed 3D model;

[0019] Mark the boundary of the printed 3D model;

[0020] According to the curvature, the thickness, and the boundary, determine the geometric features of the printed 3D model.

[0021] Optionally, the calculation of the curvature of the surface-subdivided 3D model includes:

[0022] Determine the model vertices of the surface-subdivided 3D model;

[0023] Calculate the normal vectors of the model vertices;

[0024] Calculate the normal vector gradient of the normal vectors;

[0025] Based on the model vertices, the normal vectors, and the normal vector gradient, use the following formula to calculate the curvature of the surface-subdivided 3D model:

[0026]

[0027] wherein, represents the curvature of the tessellated 3D model, represents the normal vector of the th model vertex of the tessellated 3D model, represents the normal vector gradient of the normal vector of the th model vertex of the tessellated 3D model.

[0028] Optionally, determining the high-stress region and the boundary region of the printed 3D model according to the geometric features includes:

[0029] Mapping the curvature corresponding to the geometric features to the model surface of the printed 3D model to obtain a color-coded curvature map;

[0030] Identifying the high-curvature regions in the color-coded curvature map;

[0031] Mapping the thickness corresponding to the geometric features to the model surface of the printed 3D model to obtain a thickness distribution;

[0032] Combining the high-curvature regions and the thickness distribution to determine the high-stress region of the printed 3D model;

[0033] Based on the boundary corresponding to the geometric features, determining the boundary region of the printed 3D model.

[0034] Optionally, constructing a multi-scale network of the printed 3D model according to the high-stress region and the boundary region includes:

[0035] Defining the regional network nodes of the high-stress region and the boundary region;

[0036] Constructing a coarse network of the regional network nodes;

[0037] According to the coarse network, constructing a mesoscopic network of the high-stress region and the boundary region;

[0038] Determining the key regions of the high-stress region and the boundary region;

[0039] Constructing a microscopic network of the key regions;

[0040] Establishing network bridges for the coarse network, the mesoscopic network, and the microscopic network;

[0041] Based on the network bridges, the coarse network, the mesoscopic network, and the microscopic network, constructing a multi-scale network of the printed 3D model.

[0042] Optionally, constructing the micro-network of the key area includes:

[0043] Defining the node distance of the key area;

[0044] According to the node distance, calculating the total energy of the key area using the following formula:

[0045]

[0046] where, represents the total energy of the key area, represents the summation function, represents the spring constant, represents node and node the node distance between them, represents node and node the equilibrium distance;

[0047] Defining the micro-network constraint conditions of the key area;

[0048] Based on the micro-network constraint conditions and the total energy, constructing the micro-network of the key area.

[0049] Optionally, determining the material transformation gradient of the multi-region printing material includes:

[0050] Determining the printing function requirements of the multi-region printing material corresponding to the printed 3D model;

[0051] According to the printing function requirements, defining the gradient type of the multi-region printing material;

[0052] Identifying the material properties of the multi-region printing material, where the material properties include physical properties and chemical properties;

[0053] Analyzing the material transformation gradient of the multi-region printing material through the gradient type and the material properties.

[0054] Optionally, defining the multi-scale boundary strengthening path of the printing device according to the multi-scale network includes:

[0055] Analyzing the device performance of the printing device;

[0056] According to the multi-scale network, analyzing the boundary characteristics of the printing device corresponding to the printed 3D model;

[0057] Through the boundary characteristics, establishing the boundary model of the printed 3D model in the multi-scale network;

[0058] Based on the device performance and the boundary characteristics, establish a multi-scale boundary reinforcement path of the printing device in the boundary model.

[0059] Optionally, according to the printing data, calculate the idle travel distance of the printing device. According to the idle travel distance, it includes:

[0060] Preprocess the printing data to obtain processed printing data;

[0061] According to the printing data, construct a visualization path of the printing device;

[0062] Mark the non-working movement segments of the visualization path;

[0063] Calculate the movement segment idle travel distance of the non-working movement segments in the visualization path;

[0064] Sum up the movement segment idle travel distances to obtain the idle travel distance of the printing device.

[0065] To solve the above problems, the present invention also provides an adaptive object boundary reinforcement 3D printing technology system. The system includes:

[0066] A model area analysis module, used to obtain a 3D printing model of the target to be printed, analyze the geometric features of the 3D printing model. Among them, the geometric features include curvature, thickness, and boundaries. According to the geometric features, determine the high-stress area and boundary area of the 3D printing model;

[0067] A multi-scale network construction module, used to construct a multi-scale network of the 3D printing model according to the high-stress area and boundary area. Based on the multi-scale network, define multi-region printing materials of the 3D printing model, and determine the material transformation gradient of the multi-region printing materials;

[0068] A reinforcement path construction module, used to construct a sensing network and a printing device of the target to be printed. According to the multi-scale network, define a multi-scale boundary reinforcement path of the printing device;

[0069] A reinforcement path optimization module, used to utilize the sensing network to collect printing data of the target to be printed based on the multi-scale boundary reinforcement path, the material transformation gradient, the printing device, and the multi-region printing materials. According to the printing data, calculate the idle travel distance of the printing device. According to the idle travel distance, optimize the multi-scale boundary reinforcement path to obtain an optimized multi-scale path;

[0070] An object boundary strengthening module, configured to analyze the current printing quality of the target to be printed based on the optimized multi-scale boundary strengthening path, define optimized printing parameters for the target to be printed according to the current printing quality, and perform adaptive object boundary strengthening of the target to be printed based on the optimized printing parameters.

[0071] Compared with the problems in the background art, first, by comprehensively analyzing the geometric features of the 3D printed model, a multi-scale network is constructed, and multi-region printing materials and their transformation gradients are defined, realizing the accurate identification and strengthening of high-stress regions and boundary regions of the target to be printed. Next, by constructing an integrated system of a sensing network and a printing device and defining a multi-scale boundary strengthening path, the efficiency and finished product quality of 3D printing are significantly improved. The optimized multi-scale path reduces the empty driving distance of the printing device, enhances the continuity of the printing process and the material utilization rate. Finally, based on the optimized multi-scale boundary strengthening path, this solution can analyze the printing quality in real time and adaptively adjust the printing parameters, ensuring the mechanical properties and reliability of the printing target. Therefore, the present invention can improve the boundary strengthening effect on the target to be printed. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic flowchart of an adaptive object boundary strengthening 3D printing technology method provided by an embodiment of the present invention;

[0073] Figure 2 It is a schematic diagram of a module for implementing the adaptive object boundary strengthening 3D printing technology method provided by an embodiment of the present invention.

[0074] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0076] An embodiment of the present application provides an adaptive object boundary strengthening 3D printing technology method. The execution subject of the adaptive object boundary strengthening 3D printing technology method includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the adaptive object boundary strengthening 3D printing technology 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, etc.

[0077] Embodiment 1:

[0078] Refer to Figure 1As shown in the figure, it is a schematic flowchart of the adaptive object boundary strengthening 3D printing technology method provided by an embodiment of the present invention. In this embodiment, the adaptive object boundary strengthening 3D printing technology method includes:

[0079] S1. Obtain the 3D printing model of the target to be printed, analyze the geometric features of the 3D printing model, where the geometric features include curvature, thickness, and boundary, and determine the high-stress area and boundary area of the 3D printing model according to the geometric features.

[0080] It should be explained that the target to be printed refers to the specific object or component to be manufactured by 3D printing technology, and the 3D printing model refers to the digital representation of the target to be printed, which is created by 3D modeling software.

[0081] The present invention analyzes the geometric features of the 3D printing model, where the geometric features including curvature, thickness, and boundary can be used as the basis for later 3D model area analysis.

[0082] Specifically, the analysis of the geometric features of the 3D printing model includes:

[0083] Perform smoothing processing on the 3D printing model to obtain a smoothed 3D model;

[0084] Perform surface subdivision on the smoothed 3D model to obtain a surface-subdivided 3D model;

[0085] Calculate the curvature of the surface-subdivided 3D model;

[0086] Analyze the closed coefficient of the 3D printing model;

[0087] When the closed coefficient meets the preset closed threshold, perform offset on the 3D printing model to obtain an offset 3D model;

[0088] Calculate the surface volume between the offset 3D model and the 3D printing model;

[0089] Determine the thickness of the 3D printing model based on the surface volume;

[0090] Mark the boundary of the 3D printing model;

[0091] Determine the geometric features of the 3D printing model according to the curvature, the thickness, and the boundary.

[0092] Among them, the smoothed 3D model refers to optimizing the surface of the original 3D model through mathematical algorithms to reduce surface noise and unnecessary details, thereby obtaining a smoother and more continuous model. The tessellated 3D model refers to a model that further subdivides the patches of the original 3D model into smaller patches. The curvature refers to a measure that describes the degree of bending of the surface of the 3D model. The enclosure coefficient refers to a parameter used to describe the enclosure degree of the 3D model. The enclosure threshold refers to a criterion used to determine whether the enclosure degree of the model is sufficient for the next offset operation. The offset 3D model refers to a model obtained by performing an isometric offset operation on the surface of the original 3D model. The surface volume refers to the volume difference between the offset 3D model and the original 3D model. The thickness refers to the dimension of the 3D model in the direction perpendicular to its surface. The boundary refers to the dividing line between different surfaces or regions on the 3D model. The geometric features include the curvature, thickness, and boundary of the printed 3D model.

[0093] Further, calculating the curvature of the tessellated 3D model includes:

[0094] Determining the model vertices of the tessellated 3D model;

[0095] Calculating the normal vectors of the model vertices;

[0096] Calculating the normal vector gradient of the normal vectors;

[0097] Based on the model vertices, the normal vectors, and the normal vector gradient, using the following formula to calculate the curvature of the tessellated 3D model:

[0098]

[0099] Among them, represents the curvature of the tessellated 3D model, represents the normal vector of the th model vertex of the tessellated 3D model, represents the normal vector gradient of the normal vector of the th model vertex of the tessellated 3D model.

[0100] Among them, the model vertex refers to one of the basic elements that make up the 3D model, that is, the point on the surface of the model. The normal vector refers to a vector perpendicular to a certain point on the surface of the 3D model. The normal vector gradient refers to the rate of change of the normal vector in space.

[0101] Based on the geometric features, the present invention determines that the high-stress region and the boundary region of the printed 3D model can be used as the data basis for the later analysis of the adaptive object boundary strengthening path.

[0102] In detail, determining the high stress area and boundary area of the printed 3D model according to the geometric features includes:

[0103] Mapping the curvature corresponding to the geometric feature to the model surface of the printed 3D model to obtain a color-coded curvature map;

[0104] identifying regions of high curvature in the color-coded curvature map;

[0105] Mapping the thickness corresponding to the geometric feature to the model surface of the printed 3D model to obtain a thickness distribution;

[0106] Determining a high stress area of the printed 3D model in combination with the high curvature area and the thickness distribution;

[0107] Based on the corresponding boundary of the geometric feature, a boundary area of the printed 3D model is determined.

[0108] Among them, the color-coded curvature map refers to a visual representation method of encoding the curvature value of the 3D model surface by color, the high curvature area is an area represented by a specific color (usually warm colors, such as red or yellow) in the color-coded curvature map, the thickness distribution refers to the distribution of thickness values at each point on the 3D model surface, the high stress area refers to an area that may be subjected to higher mechanical stress during printing or use, and the boundary area refers to the edge or interface area in the 3D model.

[0109] Optionally, mapping the curvature corresponding to the geometric feature to the model surface of the printed 3D model to obtain a color-coded curvature map can be achieved by writing a script using MATLAB, Python (particularly using matplotlib and numpy libraries) or other programming languages to map the curvature value to a color space.

[0110] S2. Construct a multi-scale network of the printed 3D model according to the high stress area and the boundary area, define multi-region printing materials of the printed 3D model based on the multi-scale network, and determine the material transformation gradient of the multi-region printing materials.

[0111] The present invention constructs a multi-scale network of the printed 3D model based on the high stress areas and boundary areas, which can accurately simulate and optimize the performance of the high stress areas and boundary areas, thereby improving the overall strength and reliability of the printed structure.

[0112] In detail, constructing the multi-scale network of the printed 3D model according to the high stress area and the boundary area includes:

[0113] defining regional network nodes of the high stress area and the boundary area;

[0114] Construct a rough network of the regional network nodes;

[0115] Construct a mesoscopic network of the high-stress region and the boundary region according to the rough network;

[0116] Determine the key regions of the high-stress region and the boundary region;

[0117] Construct a microscopic network of the key regions;

[0118] Establish network bridges for the rough network, the mesoscopic network, and the microscopic network;

[0119] Construct a multi-scale network for printing the 3D model based on the network bridges, the rough network, the mesoscopic network, and the microscopic network.

[0120] Wherein, the regional network nodes refer to representative points defined within the high-stress region and the boundary region of the 3D model. The rough network refers to a preliminary network structure that connects the regional network nodes to form a large-scale network covering the entire model. The mesoscopic network refers to a network further refined based on the rough network, which more detailedly describes the local characteristics of the high-stress region and the boundary region, including more complex connections and interactions. The key regions refer to particularly important regions identified in the mesoscopic network. The microscopic network refers to a finer network constructed within the key regions, which takes into account the influence of the material microstructure, such as grain structure, defects, etc., for simulating and analyzing behaviors at the microscopic scale. The network bridges refer to the intermediate layer or interface used to connect the rough network, the mesoscopic network, and the microscopic network. The multi-scale network refers to a comprehensive network integrating the rough network, the mesoscopic network, and the microscopic network, which can describe and analyze the geometric and physical characteristics of the 3D model at different scales, thereby providing a more comprehensive understanding and prediction of the model's behavior.

[0121] Further, the construction of the microscopic network of the key regions includes:

[0122] Define the node distances of the key regions;

[0123] Calculate the total energy of the key regions according to the node distances using the following formula:

[0124]

[0125] Wherein, represents the total energy of the key regions, represents the summation function, represents the spring constant, represents node and node The node distance between represents the node and the node 's equilibrium distance;

[0126] Define the micro-network constraint conditions of the key area;

[0127] Based on the micro-network constraint conditions and the total energy, construct the micro-network of the key area.

[0128] Wherein, the node distance refers to the Euclidean distance between two adjacent nodes in the micro-network, the total energy refers to the sum of the energies of the interactions between all nodes, the summation function refers to the function used to accumulate the interactions between all node pairs in the network, the spring constant refers to the intensity of the interaction between nodes in the network, the equilibrium distance refers to the ideal distance maintained between two nodes in the network without external forces, and the micro-network constraint conditions refer to a set of conditions that must be satisfied when constructing the micro-network, such as restrictions on node positions, network connectivity, maximum distance between nodes, etc.

[0129] It should be explained that the multi-region printing material refers to a printing object composed of two or more different materials during the 3D printing process.

[0130] The present invention determines the material transformation gradient of the multi-region printing material to make the boundary strengthening of the printing more refined.

[0131] Specifically, determining the material transformation gradient of the multi-region printing material includes:

[0132] Determine the printing function requirements of the multi-region printing material corresponding to the printed 3D model;

[0133] According to the printing function requirements, define the gradient type of the multi-region printing material;

[0134] Identify the material properties of the multi-region printing material, wherein the material properties include physical properties and chemical properties;

[0135] Analyze the material transformation gradient of the multi-region printing material through the gradient type and the material properties.

[0136] Among them, the printing function requirements refer to the specific performance and functional requirements that the 3D model needs to meet after printing, such as mechanical properties, thermal properties, electrical properties, etc. The gradient type refers to the change pattern of material properties in the 3D model. The physical property refers to the property that the material exhibits without changing its chemical composition. The chemical property refers to the property that the material exhibits in a chemical reaction, such as reactivity, corrosion resistance, etc. The material transformation gradient refers to the degree to which material properties gradually or suddenly change according to a predetermined gradient type in different regions of the 3D model.

[0137] Optionally, analyzing the material transformation gradient of the multi-region printing material through the gradient type and the material property can be simulated and analyzed by material property modeling software (such as COMSOL Multiphysics, ANSYS, etc.).

[0138] S3. Construct the sensing network and printing device of the target to be printed, and define the multi-scale boundary strengthening path of the printing device according to the multi-scale network.

[0139] It should be explained that the sensing network refers to a system network composed of a series of sensor nodes, and the printing device refers to a machine used to perform 3D printing tasks.

[0140] According to the multi-scale network of the present invention, defining the multi-scale boundary strengthening path of the printing device can be used as the basis for later boundary strengthening.

[0141] Specifically, defining the multi-scale boundary strengthening path of the printing device according to the multi-scale network includes:

[0142] Analyze the device performance of the printing device;

[0143] According to the multi-scale network, analyze the boundary characteristics of the 3D model corresponding to the printing device;

[0144] Through the boundary characteristics, establish the boundary model of the printed 3D model in the multi-scale network;

[0145] Based on the device performance and the boundary characteristics, establish the multi-scale boundary strengthening path of the printing device in the boundary model.

[0146] Among them, the device performance refers to the performance and ability of the printing device when performing its designed functions. The boundary characteristics refer to the specific properties of the 3D model at the boundary region. The boundary model refers to a mathematical or computational model established on the multi-scale network for simulating and analyzing the boundary region of the printed 3D model. The multi-scale boundary strengthening path refers to a set of strengthening paths designed to improve the performance of the printing device in the boundary region.

[0147] Optionally, the boundary model of the printed 3D model established in the multi-scale network by the boundary feature can be simulated by using FEA software (such as ANSYS, Abaqus, COMSOL Multiphysics, etc.).

[0148] S4. Based on the multi-scale boundary reinforcement path, the material transformation gradient, the printing device, and the multi-region printing material, use the sensing network to collect the printing data of the target to be printed. According to the printing data, calculate the idle travel distance of the printing device. According to the idle travel distance, optimize the multi-scale boundary reinforcement path to obtain an optimized multi-scale path.

[0149] It should be explained that the printing data refers to various information related to the target to be printed collected by the sensing network, including structure data, material data, printing process data, and other data.

[0150] In the present invention, calculating the idle travel distance of the printing device according to the printing data can be used as the basis for subsequent path optimization.

[0151] Specifically, calculating the idle travel distance of the printing device according to the printing data and according to the idle travel distance includes:

[0152] Preprocess the printing data to obtain processed printing data;

[0153] Construct a visualization path of the printing device according to the printing data;

[0154] Mark the non-working moving segments of the visualization path;

[0155] Calculate the moving segment idle travel distance of the non-working moving segments in the visualization path;

[0156] Sum up the moving segment idle travel distances to obtain the idle travel distance of the printing device.

[0157] Among them, the processed printing data refers to the data obtained by performing a series of cleaning, conversion, and formatting operations on the original printing data. The visualization path refers to the graphical representation of the moving path of the printing device. The non-working moving segment refers to the moving segment where the print head does not deposit material. The moving segment idle travel distance refers to the specific distance that the print head moves in the non-working moving segment. The idle travel distance refers to the sum of the idle travel distances of all non-working moving segments of the print head during the entire printing process.

[0158] Optionally, constructing the visualization path of the printing device according to the printing data can be implemented by 3Dmax software.

[0159] Based on the non-printing travel distance, the multi-scale boundary strengthening path is optimized to obtain an optimized multi-scale path, which can improve the printing strengthening efficiency. Among them, the optimized multi-scale path refers to the path after optimizing the non-printing travel distance of the multi-scale boundary strengthening path. Specifically, the path optimization of the multi-scale boundary strengthening path can be achieved through algorithms in graph theory (such as the shortest path algorithm, minimum spanning tree algorithm, etc.) to optimize the path.

[0160] S5. Based on the optimized multi-scale boundary strengthening path, analyze the current printing quality of the object to be printed. According to the current printing quality, define the optimized printing parameters of the object to be printed, and perform adaptive object boundary strengthening of the object to be printed based on the optimized printing parameters.

[0161] It should be explained that the current printing quality refers to the printing effect and performance indicators achieved by the object to be printed during the actual printing process. The optimized printing parameters refer to a series of printing settings adjusted according to the printing target and the characteristics of the printer to improve the printing quality, such as parameters like printing speed, printing temperature, layer thickness, etc.

[0162] Finally, the present invention performs adaptive object boundary strengthening of the object to be printed based on the optimized printing parameters to achieve precise boundary strengthening of the object to be printed.

[0163] Compared with the problems in the background art, firstly, by comprehensively analyzing the geometric features of the 3D printing model, a multi-scale network is constructed, and multi-region printing materials and their transformation gradients are defined, realizing precise identification and strengthening of the high-stress regions and boundary regions of the object to be printed. Then, by constructing an integrated system of a sensing network and a printing device, and defining a multi-scale boundary strengthening path, the efficiency and finished product quality of 3D printing are significantly improved. The optimized multi-scale path reduces the non-printing travel distance of the printing device, enhances the continuity of the printing process and the material utilization rate. Finally, based on the optimized multi-scale boundary strengthening path, this solution can analyze the printing quality in real time and adaptively adjust the printing parameters, ensuring the mechanical properties and reliability of the printing target. Therefore, the present invention can improve the boundary strengthening effect on the object to be printed.

[0164] Embodiment 2:

[0165] As Figure 2 shown, it is a functional module diagram of a 3D printing technology system for adaptive object boundary strengthening of the present invention.

[0166] The adaptive object boundary strengthening 3D printing technology system 200 described in the present invention can be installed in an electronic device. According to the implemented functions, the adaptive object boundary strengthening 3D printing technology system may include an application scenario analysis module 201, a multi-scale network construction module 202, a strengthening path construction module 203, a strengthening path optimization module 204, and an object boundary strengthening module 205. The modules described in the present invention may also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

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

[0168] The model area analysis module 201 is used to obtain the 3D printing model of the target to be printed, analyze the geometric features of the 3D printing model, where the geometric features include curvature, thickness, and boundaries, and determine the high-stress area and boundary area of the 3D printing model according to the geometric features;

[0169] The multi-scale network construction module 202 is used to construct a multi-scale network of the 3D printing model according to the high-stress area and boundary area, define multi-region printing materials for the 3D printing model based on the multi-scale network, and determine the material transformation gradient of the multi-region printing materials;

[0170] The strengthening path construction module 203 is used to construct a sensing network and a printing device for the target to be printed, and define a multi-scale boundary strengthening path for the printing device according to the multi-scale network;

[0171] The strengthening path optimization module 204 is used to collect printing data of the target to be printed by using the sensing network based on the multi-scale boundary strengthening path, the material transformation gradient, the printing device, and the multi-region printing materials, calculate the idle travel distance of the printing device according to the printing data, and optimize the multi-scale boundary strengthening path according to the idle travel distance to obtain an optimized multi-scale path;

[0172] The object boundary strengthening module 205 is used to analyze the current printing quality of the target to be printed based on the optimized multi-scale boundary strengthening path, define optimized printing parameters for the target to be printed according to the current printing quality, and perform adaptive object boundary strengthening of the target to be printed based on the optimized printing parameters.

[0173] Specifically, each module in the adaptive object boundary strengthening 3D printing technology system 200 in the embodiments of the present invention is used in the same way as the above Figure 1It uses the same technical means as the adaptive object boundary enhancement 3D printing technology method described in [reference], and can produce the same technical effects, which will not be elaborated here.

[0174] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic 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 not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An adaptive object boundary enhancement 3D printing technology method, characterized in that The method includes: Obtain the printed 3D model of the target to be printed, analyze the geometric features of the printed 3D model, where the geometric features include curvature, thickness, and boundary, and determine the high-stress region and boundary region of the printed 3D model according to the geometric features; Construct a multi-scale network of the printed 3D model according to the high-stress region and boundary region, define multi-region printing materials for the printed 3D model based on the multi-scale network, and determine the material transformation gradient of the multi-region printing materials. Wherein, constructing the multi-scale network of the printed 3D model according to the high-stress region and boundary region includes: defining regional network nodes for the high-stress region and boundary region, constructing a rough network of the regional network nodes, constructing a mesoscopic network of the high-stress region and boundary region according to the rough network, determining the key regions of the high-stress region and boundary region, constructing a microscopic network of the key regions, establishing network bridges for the rough network, the mesoscopic network, and the microscopic network, and constructing a multi-scale network of the printed 3D model based on the network bridges, the rough network, the mesoscopic network, and the microscopic network. Wherein, constructing the microscopic network of the key regions includes: defining the node distance of the key regions, and calculating the total energy of the key regions using the following formula according to the node distance: Among them, represents the total energy of the key area, represents the summation function, represents the spring constant, represents the node and the node the node distance between them, represents the node and the node the equilibrium distance of, defining the microscopic network constraint conditions of the key area, and constructing the microscopic network of the key area based on the microscopic network constraint conditions and the total energy. Among them, the determination of the material transformation gradient of the multi-region printing material includes: determining the printing function requirements of the multi-region printing material corresponding to the printed 3D model, defining the gradient type of the multi-region printing material according to the printing function requirements, and identifying the material properties of the multi-region printing material, where the material properties include physical properties and chemical properties, and analyzing the material transformation gradient of the multi-region printing material through the gradient type and the material properties; Construct a sensing network and a printing device for the target to be printed, and define a multi-scale boundary strengthening path for the printing device according to the multi-scale network. Wherein, defining the multi-scale boundary strengthening path for the printing device according to the multi-scale network includes: analyzing the device performance of the printing device, analyzing the boundary characteristics of the printed 3D model corresponding to the printing device according to the multi-scale network, establishing a boundary model of the printed 3D model in the multi-scale network through the boundary characteristics, and establishing a multi-scale boundary strengthening path of the printing device in the boundary model based on the device performance and the boundary characteristics; Based on the multi-scale boundary strengthening path, the material transformation gradient, the printing device, and the multi-region printing materials, use the sensing network to collect printing data of the target to be printed, calculate the idle travel distance of the printing device according to the printing data, optimize the path of the multi-scale boundary strengthening path according to the idle travel distance to obtain an optimized multi-scale boundary strengthening path. Wherein, calculating the idle travel distance of the printing device according to the printing data includes: preprocessing the printing data to obtain processed printing data, constructing a visualization path of the printing device according to the printing data, marking the non-working moving segments of the visualization path, calculating the moving segment idle travel distance of the non-working moving segments in the visualization path, and summing the moving segment idle travel distances to obtain the idle travel distance of the printing device; Based on the optimized multi-scale boundary enhancement path, analyze the current printing quality of the target to be printed, define the optimized printing parameters of the target to be printed according to the current printing quality, and perform adaptive object boundary enhancement of the target to be printed based on the optimized printing parameters.

2. The adaptive object boundary enhancement 3D printing technology method according to claim 1, wherein The analysis of the geometric features of the printed 3D model includes: Perform smoothing processing on the printed 3D model to obtain a smoothed 3D model; Perform surface subdivision on the smoothed 3D model to obtain a surface-subdivided 3D model; Calculate the curvature of the surface-subdivided 3D model; Analyze the closure coefficient of the printed 3D model; When the closure coefficient meets the preset closure threshold, perform offset on the printed 3D model to obtain an offset 3D model; Calculate the surface volume between the offset 3D model and the printed 3D model; Determine the thickness of the printed 3D model based on the surface volume; Mark the boundary of the printed 3D model; Determine the geometric features of the printed 3D model according to the curvature, the thickness, and the boundary.

3. The adaptive object boundary enhancement 3D printing technology method according to claim 2, characterized in that The calculation of the curvature of the surface-subdivided 3D model includes: Determine the model vertices of the surface-subdivided 3D model; Calculate the normal vectors of the model vertices; Calculate the normal vector gradient of the normal vectors; Based on the model vertices, the normal vectors, and the normal vector gradient, calculate the curvature of the surface-subdivided 3D model using the following formula: Among them, represents the curvature of the tessellated 3D model, represents the normal vector of the th model vertex of the tessellated 3D model, represents the normal vector gradient of the normal vector of the th model vertex of the tessellated 3D model.

4. The adaptive object boundary enhancement 3D printing technology method according to claim 3, characterized in that The determination of the high-stress area and the boundary area of the printed 3D model according to the geometric features includes: Map the curvature corresponding to the geometric features to the model surface of the printed 3D model to obtain a color-coded curvature map; Identify the high-curvature areas in the color-coded curvature map; Map the thickness corresponding to the geometric features to the model surface of the printed 3D model to obtain a thickness distribution; Combine the high-curvature areas and the thickness distribution to determine the high-stress area of the printed 3D model; Determine the boundary area of the printed 3D model based on the boundary corresponding to the geometric features.

5. An adaptive object boundary enhancement 3D printing technology system, characterized in that, The system includes: A model area analysis module for obtaining the printed 3D model of the target to be printed, analyzing the geometric features of the printed 3D model, where the geometric features include curvature, thickness, and boundary, and determining the high-stress area and the boundary area of the printed 3D model according to the geometric features; A multi-scale network construction module is used to construct a multi-scale network of the printed 3D model according to the high-stress region and the boundary region. Based on the multi-scale network, multi-region printing materials of the printed 3D model are defined, and a material transformation gradient of the multi-region printing materials is determined. Among them, constructing the multi-scale network of the printed 3D model according to the high-stress region and the boundary region includes: defining regional network nodes of the high-stress region and the boundary region, constructing a rough network of the regional network nodes, constructing a mesoscopic network of the high-stress region and the boundary region according to the rough network, determining key regions of the high-stress region and the boundary region, constructing a microscopic network of the key regions, establishing network bridges between the rough network, the mesoscopic network and the microscopic network, and constructing the multi-scale network of the printed 3D model based on the network bridges, the rough network, the mesoscopic network and the microscopic network. Among them, constructing the microscopic network of the key regions includes: defining a node distance of the key region, and calculating the total energy of the key region according to the node distance by using the following formula: Among them, represents the total energy of the key area, represents the summation function, represents the spring constant, represents the node and the node the node distance between them, represents the node and the node the equilibrium distance of, define the microscopic network constraint conditions of the key area, and construct the microscopic network of the key area based on the microscopic network constraint conditions and the total energy. Among them, the determining the material transformation gradient of the multi-region printing material includes: determining the printing function requirements of the multi-region printing material corresponding to the printed 3D model, defining the gradient type of the multi-region printing material according to the printing function requirements, and identifying the material properties of the multi-region printing material, where the material properties include physical properties and chemical properties, and analyzing the material transformation gradient of the multi-region printing material through the gradient type and the material properties; A reinforcement path construction module is used to construct a sensing network and a printing device of the target to be printed, and define a multi-scale boundary reinforcement path of the printing device according to the multi-scale network. Among them, defining the multi-scale boundary reinforcement path of the printing device according to the multi-scale network includes: analyzing the device performance of the printing device, analyzing the boundary characteristics of the printed 3D model corresponding to the printing device according to the multi-scale network, establishing a boundary model of the printed 3D model in the multi-scale network through the boundary characteristics, and establishing the multi-scale boundary reinforcement path of the printing device in the boundary model based on the device performance and the boundary characteristics; A reinforcement path optimization module is used to collect printing data of the target to be printed by using the sensing network based on the multi-scale boundary reinforcement path, the material transformation gradient, the printing device and the multi-region printing materials, calculate the idle running distance of the printing device according to the printing data, and perform path optimization on the multi-scale boundary reinforcement path according to the idle running distance to obtain an optimized multi-scale boundary reinforcement path. Among them, calculating the idle running distance of the printing device according to the printing data includes: preprocessing the printing data to obtain processed printing data, constructing a visual path of the printing device according to the printing data, marking non-working moving segments of the visual path, calculating the moving segment idle running distance of the non-working moving segments in the visual path, and summing up the moving segment idle running distance to obtain the idle running distance of the printing device; An object boundary strengthening module is used to analyze the current printing quality of the target to be printed based on the optimized multi-scale boundary reinforcement path, define optimized printing parameters of the target to be printed according to the current printing quality, and perform adaptive object boundary strengthening of the target to be printed based on the optimized printing parameters.

Images