3D printed product lightweight design method and system and storage medium

Abstract

The invention relates to the technical field of 3D printing, in particular to a lightweight design method and system for a 3D printed product and a storage medium. The method comprises the following steps that geometric data information of a 3D printing model is obtained; performing model internal space volume distribution feature extraction and model wall thickness uniformity distribution evaluation on the geometric data information, and predicting a model structure load bearing capacity change trend to obtain structure load prediction data; detecting the optimized space attenuation trend of the material distribution density according to the structural load prediction data; performing dynamic interference removal intensity prediction on redundant materials in the model according to the optimization space attenuation trend to obtain redundant removal intensity data; and determining a coupling balance trend of model lightweight and strength maintenance based on the redundancy removal strength data and the optimized spatial attenuation trend. According to the method, accurate matching of the load demand and material distribution is realized by establishing the correlation model of the wall thickness uniformity and the load bearing capacity.

Description

Technical Field

[0001] The present invention relates to the field of 3D printing technology, and in particular to a lightweight design method, system and storage medium for 3D printed products. Background Technique

[0002] 3D printing technology is an additive manufacturing technology based on a three-dimensional digital model, which manufactures solid parts by adding materials layer by layer. Its principle is to perform digital slicing processing on the designed 3D model in a computer-aided design (CAD) environment, and then further divide these slices into voxels or track elements according to the type of printer, and stack materials layer by layer to form a three-dimensional object. Currently, common types of 3D printing technologies include fused deposition modeling (FDM), stereolithography (SLA), digital light processing (DLP), inkjet 3D printing (PolyJet / 3DP), selective laser sintering (SLS), and electron beam melting (EBM), etc. However, when facing complex geometric models, traditional 3D printing pre-processing software lacks the ability to deeply analyze the characteristics of the internal space volume distribution of the model. Previous methods mainly relied on simple unified parameter settings, and could not identify the differences in the structural importance of different regions of the model, which made the optimization design lack pertinence. There is also a lack of accurate prediction of the change trend of the structural load-bearing capacity. This results in the fact that in actual use, some regions may have structural weaknesses due to insufficient materials, while other regions have material redundancy due to over-design. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a lightweight design method, system and medium for 3D printed products to solve at least one of the above technical problems.

[0004] To achieve the above object, a lightweight design method for 3D printed products includes the following steps: Step S1: Obtain the geometric data information of the 3D printing model; extract the characteristics of the internal space volume distribution of the model and evaluate the uniformity distribution of the model wall thickness for the geometric data information, and predict the change trend of the model structural load-bearing capacity to obtain structural load prediction data; Step S2: Detect the optimization space attenuation trend of the material distribution density according to the structural load prediction data; predict the dynamic interference intensity of removing redundant materials inside the model according to the optimization space attenuation trend to obtain redundant removal intensity data; determine the coupling balance trend of model lightweight and strength retention based on the redundant removal intensity data and the optimization space attenuation trend; Step S3: Use the coupling balance trend to predict the deviation condition of the support structure layout requirements; predict the synchronous deterioration trend of the coordinated bearing of multiple support points based on the deviation condition of the support structure layout requirements; evaluate the efficiency difference degree between the material consumption and bearing of the support structure according to the synchronous deterioration trend; Step S4: Evaluate the matching risk of printing process parameters based on the efficiency difference degree; trace the abnormal slicing layer - to - layer bonding strength based on the matching risk of printing process parameters, and optimize the printing filling parameters to obtain optimized printing filling parameter data; Step S5: Construct a three - dimensional space material distribution model according to the optimized printing filling parameter data, and generate a lightweight printing path planning scheme; output a lightweight digital control model of the 3D printed product according to the printing path planning scheme.

[0005] The present invention also provides a lightweight design system for 3D printed products, which is used to execute the above - mentioned lightweight design method for 3D printed products. The lightweight design system for 3D printed products includes: A geometric feature analysis module, which is used to obtain the geometric data information of the 3D printing model; extract the internal space volume distribution characteristics of the model and evaluate the wall thickness uniformity distribution of the model for the geometric data information, and predict the change trend of the model structure's load - bearing capacity to obtain structure load prediction data; A material optimization analysis module, which is used to detect the optimization space attenuation trend of the material distribution density according to the structure load prediction data; predict the dynamic interference strength of removing redundant materials inside the model according to the optimization space attenuation trend to obtain redundant removal strength data; determine the coupling balance trend between model lightweight and strength retention based on the redundant removal strength data and the optimization space attenuation trend; A support structure optimization module, which is used to predict the deviation of the support structure layout requirements by using the coupling balance trend; predict the deterioration trend of the synchronization of multi - support point coordinated bearing based on the deviation of the support structure layout requirements; evaluate the efficiency difference degree between the material consumption and bearing of the support structure according to the synchronization deterioration trend; A process parameter optimization module, which is used to evaluate the matching risk of printing process parameters based on the efficiency difference degree; trace the abnormal slicing layer - to - layer bonding strength based on the matching risk of printing process parameters, and optimize the printing filling parameters to obtain optimized printing filling parameter data; A path planning generation module, which is used to construct a three - dimensional space material distribution model according to the optimized printing filling parameter data, and generate a lightweight printing path planning scheme; output a lightweight digital control model of the 3D printed product according to the printing path planning scheme.

[0006] The present invention also provides a computer storage medium, storing a computer program, and when the computer program is executed, it implements the above - mentioned lightweight design method for 3D printed products.

[0007] The present invention can effectively solve the technical bottlenecks existing in the process of achieving the weight reduction goal of existing 3D printed products, such as low material utilization rate, difficult to balance structural strength, and unreasonable printing paths. In traditional 3D printing lightweight design, a unified filling density and fixed path strategy is often adopted, ignoring the stress distribution differences borne by the product structure in different regions, resulting in material redundancy in some regions and fragile structures in other regions, with unstable overall structural performance. At the same time, improper path design during the printing process is likely to cause material accumulation, energy consumption waste, and a decrease in processing efficiency. This method starts from the design source by introducing multi-level structural analysis, material efficiency evaluation, and path optimization mechanisms, integrating multi-dimensional parameters such as strength requirements, support performance, printing process, and equipment control, and constructs an intelligent lightweight design process that takes into account performance, materials, and processes. After constructing the initial structure of the model, this method first carefully evaluates the load-bearing capacity of the support structure and its material usage, and combines the synchronism deterioration trend index to quantify the load-bearing efficiency corresponding to the unit material of different support structures. In this process, through the systematic calculation of the material consumption and load-bearing capacity of the support structure, the basic data for performance-oriented material configuration is obtained, which not only improves the accuracy of the analysis but also provides solid data support for the subsequent evaluation of the adaptability of the printing process. By comparing and analyzing the unit material load-bearing efficiency of different support regions and calculating the coefficient of variation, the balance degree of the material usage distribution in the structure is further revealed, which helps to identify problems such as unreasonable material distribution and unbalanced structural configuration in the design, laying a foundation for optimizing the lightweight and load-bearing coordination within the structure. Then, in the stage of evaluating the adaptability of process parameters, this method quickly locates the possible matching risk points in the printing settings by analyzing the response relationship between the efficiency difference of the support structure and the current printing process parameters (such as temperature, speed, layer thickness, etc.). Combining the actual deviation evaluation of the interlayer bonding strength and the process combination traceability technology, it can identify the bonding abnormality problems caused by improper parameter combination between different sliced layers, avoiding structural fragility caused by unqualified local forming quality. In addition, the system also supports automatically adjusting parameters such as filling density, filling pattern, and printing path after identifying the abnormal region, forming a local optimization strategy with higher mechanical stability and material usage efficiency. Through a series of parameter verification and index comparison (including weight reduction rate, load-bearing strength retention rate, interlayer bonding strength deviation, etc.), the comprehensive coordination of the lightweight goal and strength standard is achieved, ensuring that the structural reliability is not damaged while reducing material usage. In the subsequent model optimization and printing control stage, this method establishes a three-dimensional spatial material density distribution map, accurately maps the optimized parameters to each spatial unit of the model, breaks the traditional "whole-piece unified filling" method, and realizes the regional filling strategy of configuration on demand and differential empowerment.Each region applies different filling patterns and density parameters according to its functional attributes and stress conditions, thereby improving the structural utilization efficiency of the overall printed model without increasing the printing complexity. After slicing, a sequence of path coordinates is automatically generated, and strategies such as sequential adjustment and connection method reconstruction are adopted during the path optimization process to effectively reduce the idle travel and repeated paths, lower the printing energy consumption and material redundancy, and improve the processing efficiency. The path planning data is automatically converted into a control instruction format recognizable by the printer after optimization, and then all control dimension parameters are integrated to construct a complete digital control model. In practical applications, this model can directly drive various types of 3D printing devices, and no manual intervention is required during the device execution process to ensure that the printing task is accurately implemented according to the optimization results, improving the product forming consistency and repeatability. In addition, the lightweight digital control model generated by this method has strong portability and scalability, and is suitable for product manufacturing tasks with high requirements for structural weight reduction and strength in various industrial scenarios, such as unmanned aerial vehicle shells, customized brackets, aerospace fittings, etc. Generally speaking, this method breaks through the limitations of the fragmented processing of material filling, structural load-bearing, parameter setting, and path execution in traditional 3D printing lightweight design, and establishes a full-process closed-loop optimization mechanism from structural design evaluation to printing path control. By introducing key technologies such as structural difference identification, parameter risk assessment, combined strength traceability, and intelligent path planning, a high degree of coordination between the design logic and manufacturing capabilities is achieved. The final product not only significantly reduces in weight, but also systematically improves in load-bearing capacity and processing efficiency. This design and manufacturing integration method centered on data-driven, feedback closed-loop, and spatial precise control has good engineering applicability and promotion prospects, and can be used as one of the key technical means for both enhancing the functionality and saving materials of 3D printed products. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings: Figure 1 It is a schematic diagram of the step flow of the lightweight design method for 3D printed products of the present invention; Figure 2 is Figure 1 a detailed step flow schematic diagram of step S1 in Figure 3 is Figure 1 a detailed step flow schematic diagram of step S3 in DETAILED DESCRIPTION OF THE EMBODIMENTS

[0009] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work belong to the scope of protection of the present invention.

[0010] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0011] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0012] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a lightweight design method for 3D printing products, and the method includes the following steps: Step S1: Obtain the geometric data information of the 3D printing model; extract the internal space volume distribution characteristics of the geometric data information and evaluate the wall thickness uniformity distribution of the model, and predict the change trend of the model's structural load-bearing capacity to obtain the structural load prediction data; In the embodiment of the present invention, the 3D model to be printed is imported into a modeling software (such as SolidWorks or Fusion 360), the triangular patch mesh data in its STL file format is extracted, and the model is geometrically preprocessed to clean non-manifold boundaries, duplicate faces, and isolated points, so as to ensure the integrity of the model topology. Then, the voxelization method is used to convert the internal space of the model into a three-dimensional voxel grid of uniform size. For example, the model is divided into voxel blocks with a side length of 0.2 mm, and the solid / vacancy state information of each voxel block is obtained using the layer-by-layer scanning method, so as to statistically analyze the spatial volume distribution characteristics inside the model. Then, voxel density clustering analysis is used to divide the solid density of each region in the model, generate a distribution map of the local wall thickness, and calculate the wall thickness variance as an evenness evaluation index. Finally, a standard static load (such as a vertical load of 100 N is applied to the bottom of the model) is applied using a finite element simulation software (such as ANSYS), and the overall stress-strain distribution of the model is simulated. By extracting the relationship between the stress peak change curve and the voxel distribution change, a change trend model of the structural bearing capacity is established, and the structural load prediction data for subsequent material optimization is obtained.

[0013] Step S2: Detect the optimization space attenuation trend of the material distribution density according to the structural load prediction data; predict the dynamic interference intensity of removing redundant materials inside the model according to the optimization space attenuation trend, and obtain the redundant removal intensity data; determine the coupling balance trend between the model lightweight and strength retention based on the redundant removal intensity data and the optimization space attenuation trend; In the embodiment of the present invention, according to the structural load prediction data obtained in step S1, the space density mapping algorithm is used to score the material bearing utilization rate of different regions of the model. Among them, the high-scoring region represents that the material bearing utilization is relatively sufficient, and the low-scoring region represents material redundancy. By constructing a spatial gradient attenuation map of material density-bearing efficiency, the spatial region with strong optimization potential inside the model is extracted to form the optimization space attenuation trend data. On this basis, the perturbation analysis method is used to predict the mechanical property changes of the model after removing redundant materials. The method is as follows: gradually cavitate the low-bearing region and simulate the structural response of the model to obtain the change rate of the structural response caused by removing unit material, so as to establish a redundant removal dynamic interference intensity prediction model and calculate the redundant removal intensity data. Further, combining the above optimization trend and interference intensity data, a multi-objective optimization method is used to construct a coupling function between the model lightweight and strength retention, and the Pareto front is used to screen the balance point to determine the optimal coupling balance trend between the model lightweight and reliability.

[0014] Step S3: Predict the deviation status of the support structure layout requirements by using the coupling balance trend; predict the synchronous deterioration trend of the coordinated load bearing of multiple support points based on the deviation status of the support structure layout requirements; evaluate the efficiency difference degree between the material consumption and load bearing of the support structure according to the synchronous deterioration trend; In the embodiment of the present invention, based on the coupling balance trend data constructed in Step S2, a structural stability evaluation function is called to perform mechanical support layout analysis on the model. First, the main load-bearing paths in the model are identified through the reconstruction analysis of the force conduction path, and the required support structure layout positions and strength requirements of the model during the actual printing process are deduced based on these paths. Combining the coupling balance trend, the spatial gradient of the force on the support area is inversely inferred, and the deviation degree between the current support layout and the theoretical support requirements is calculated to form the deviation status of the support structure layout requirements. Subsequently, under the parallel force-bearing state of simulating multiple support points, a synchronization index for multi-point coordinated load bearing is constructed to evaluate the time-sequence difference of force transmission caused by structural lightweighting, and its synchronous deterioration trend is quantified. Furthermore, the material consumption of each support point is compared with its actual load-bearing capacity to evaluate the efficiency difference between different support structures in terms of material use and force-bearing effect, and the difference degree between the material consumption and load-bearing efficiency of the support structure is obtained, providing a basis for subsequent printing strategy adjustment.

[0015] Step S4: Evaluate the matching risk situation of the printing process parameters based on the efficiency difference degree; trace the abnormal interlayer bonding strength of the slice based on the matching risk situation of the printing process parameters, and optimize the printing filling parameters to obtain the optimized data of the printing filling parameters; In the embodiment of the present invention, based on the efficiency difference degree obtained in Step S3, the risk of the process parameter matching of the entire printing model is evaluated. The specific method is as follows: Use a printing process simulation system based on thermo-mechanical simulation (such as Digimat-AM), set the initial printing parameters (such as nozzle temperature 220°C, printing speed 60mm / s, layer thickness 0.15mm, etc.), simulate the interlayer bonding strength of different support areas and their adjacent structures, and mark the local strength abnormal points caused by the efficiency difference. Subsequently, a retrospective analysis is performed on the abnormal points to trace their formation reasons and perform correlation analysis with the process parameters, so as to identify the key parameter combinations that may cause the abnormality. The sensitivity analysis method is used to optimize the filling parameters such as the filling rate (changing from 30% to 70%) and the filling pattern (honeycomb, linear, grid) one by one. On the premise of meeting the structural lightweighting and strength balance, the optimal parameter combination is selected to form the optimized data of the printing filling parameters, which serves as the basis for subsequent printing path planning.

[0016] Step S5: Construct a three-dimensional space material distribution model according to the optimized data of the printing filling parameters, and generate a lightweight printing path planning scheme; output a lightweight digital control model of the 3D printing product according to the printing path planning scheme.

[0017] Based on the optimized printing filling parameter data obtained in step S4, in the embodiments of the present invention, first, a three-dimensional reconstruction software (such as Materialise 3-matic) is used to apply the optimized parameters to the original model, and the internal material distribution of the reconstructed model is formed, including changes in filling density, reconstruction of local strengthening regions, and setting of hollow structures, etc., to form a three-dimensional space material distribution model. Then, the distribution model is introduced into a path planning software (such as Ultimaker Cura or Slic3r), and the path generation rules are set in combination with the optimized parameters. Considering the nozzle movement efficiency, path heat accumulation balance, and continuity of cross-layer connections, an optimal lightweight printing path planning scheme is generated through simulation and iterative methods. Finally, the path data is exported in the form of G-code (G-code) as the control instructions for the 3D printer, and a corresponding lightweight digital control model is constructed in the control system to achieve efficient, material-saving, and stable printing and manufacturing of the final product. This model is applicable to application scenarios that require both lightweight and strength, such as aerospace structural components, medical implants, and robot shells.

[0018] Preferably, step S1 includes the following steps: Step S11: Obtain geometric data information by scanning the surface of the 3D printing model; perform three-dimensional coordinate point cloud extraction processing on the geometric data information to obtain the model surface contour data; In the embodiments of the present invention, a high-precision three-dimensional scanner (such as FARO Edge ScanArm HD or Creaform HandySCAN 3D) is used to perform multi-angle non-contact laser scanning on the surface of the 3D printing model to obtain complete surface geometric data information. During the scanning process, the scanning accuracy is set to 0.05 mm, the scanning angle covers all visible outer surfaces of the model, and an automatic stitching algorithm is used to fuse multiple groups of scanning data into an integrated three-dimensional model. Subsequently, the scanning result is imported into point cloud processing software (such as Geomagic Design X), and noise removal, density reconstruction, and normal vector correction processing are performed on the original data to extract the three-dimensional coordinate point cloud data of the model surface. This point cloud data consists of a large number of irregularly distributed coordinate points, each point containing XYZ three-dimensional coordinate information, representing the tiny surface features of the model outer surface, and finally forming a high-density and well-regulated model surface contour data, laying a foundation for subsequent surface curvature analysis and structural lightweight processing.

[0019] Step S12: Calculate and process the curvature distribution of the model outer surface based on the model surface contour data to obtain the surface curvature distribution data; In the embodiment of the present invention, the model surface contour data obtained in step S11 is imported into a CAD environment (such as MeshLab or Rhino+Grasshopper plugin), and the point cloud data is converted into a continuous surface structure using a triangular mesh reconstruction algorithm to construct a model surface mesh model. Based on this mesh model, a discrete curvature analysis algorithm (such as a combined analysis method of Gaussian curvature and mean curvature) is applied to calculate the local curvature of each mesh vertex. The specific method is as follows: Fit a quadratic surface within the neighborhood of each vertex, and then calculate the rate of change in the principal curvature direction. The curvature results are used to reflect the concavity and convexity degree and the change trend of the local area of the model surface. Among them, the high-curvature area represents complex surface structures (such as corners, grooves, or protrusions), and the low-curvature area is a relatively flat area. After the system performs standardized normalization processing on all curvature results, the surface curvature distribution data in the form of a three-dimensional heat map is output for subsequent volume partitioning and structural risk identification. Taking the surface of a medical implant as an example, the curvature characteristics of the complex transition area and the smooth area at the bone interface can be effectively identified.

[0020] Step S13: Perform internal space volume partitioning processing on the surface curvature distribution data to obtain space volume distribution data; perform statistical analysis processing on the internal space density of the model according to the space volume distribution data to obtain internal space density distribution data; extract features from the internal space density distribution data to obtain model internal space volume distribution feature data; In the embodiment of the present invention, the overall three-dimensional space of the model is internally partitioned using the surface curvature distribution data obtained in step S12. Specifically, a space voxel segmentation algorithm based on curvature dominance is adopted to divide the inside of the model into several space units controlled by the outer surface curvature. Each unit is constructed as a 0.5mm cubic voxel structure, and is labeled using the spatial position of the voxel, the corresponding curvature information, and the envelope structure to form space volume distribution data. Then, statistical analysis is performed on these space voxels to calculate the solid filling degree within each space unit, that is, to evaluate the material distribution density within the unit space, so as to obtain the internal space density distribution data of the model. Further, a method of volume density weighted mean and local density gradient difference is used to extract density characteristic parameters of different regions, including the maximum density mutation value, the regional average density, the density center offset value, etc., to form the model internal space volume distribution feature data. Taking a complex mechanical connection component as an example, this feature data can accurately indicate whether there are potential risk points of stress concentration in the material transition area.

[0021] Step S14: Obtain the wall thickness measurement data of each cross-section of the model, calculate the thickness change gradient of the wall thickness measurement data, and evaluate the wall thickness uniformity index to obtain wall thickness uniformity evaluation data; The embodiment of the present invention uses a virtual slice algorithm to construct dozens of two-dimensional cross-sectional images of the model from different angles (such as intercepting a layer every 5 mm in the XY plane) based on the model voxel unit structure constructed in step S13. The wall thickness value on each section is measured using CAD software (such as SolidWorks Simulation or AutoCAD), and the minimum thickness, maximum thickness and thickness distribution range on each section are obtained using a multi-point symmetric measurement method to form a wall thickness measurement data set. Next, the thickness gradient algorithm is used to calculate the rate of change of the wall thickness in space, that is, the rate of change of the thickness between adjacent measurement points in three-dimensional space, and a wall thickness gradient map is generated. Subsequently, the wall thickness change gradient is statistically evaluated, the standard deviation, maximum difference value and mean deviation of the thickness change in each region are calculated, and a wall thickness uniformity index (such as the thickness variation coefficient CV, the smaller the coefficient, the better the uniformity) is introduced, and finally the wall thickness uniformity evaluation data is output for analyzing the distribution of structural weak areas. Taking the aviation lightweight structure as an example, this data can be used to evaluate whether there are local areas in the shell structure that are prone to instability.

[0022] Step S15: using the wall thickness uniformity assessment data to predict the change trend of the model structure load bearing capacity to obtain structural load prediction data.

[0023] In the embodiment of the present invention, the wall thickness uniformity evaluation data obtained in step S14 is imported as an input parameter into a finite element structure simulation module (such as Abaqus or ANSYS), and boundary conditions and load conditions are applied in the structural model to simulate its load response under different working conditions. First, the constraint boundary (such as bottom fixation) is set, the load is applied (such as 150N applied vertically at the top center), and the stress distribution, deformation and safety margin inside the structure are calculated. The wall thickness uniformity data is used as a local model parameter to simulate the stress concentration caused by the wall thickness difference, and the trend curve of the unit area bearing capacity changing with the wall thickness is statistically analyzed. The mapping relationship between "wall thickness uniformity index-bearing capacity change rate" is constructed by a trend fitting method (such as quadratic polynomial fitting), and the response trend of the structural strength of different parts of the model with the change of thickness is predicted, thereby generating structural load prediction data, which provides basic data support for subsequent lightweight optimization and redundant material elimination. This method is particularly important in scenarios with high requirements for structural stiffness, such as medical device housings and lightweight vehicle body frames.

[0024] Preferably, step S15 comprises the following steps: Step S151: obtaining model material property parameter data; In an embodiment of the present invention, in combination with the wall thickness uniformity evaluation data generated in the foregoing step S15, material property parameter data of the 3D printing material used is further collected, specifically including the elastic modulus, Poisson's ratio, yield strength, fracture elongation, density, thermal expansion coefficient, etc. of the material. Taking a typical 3D printing nylon material (such as PA12) as an example, it can be obtained through a standard material database (such as ANSYS Granta or MatWeb) or experimental measurement methods: its elastic modulus is about 1700 MPa, Poisson's ratio is 0.39, yield strength is 45 MPa, fracture elongation is about 20%, and material density is about 1.01 g / cm³. These parameters are uniformly sorted into a material property parameter data set and input into a finite element simulation environment to define the behavior boundaries and response characteristics of the material in mechanical calculations, which is a prerequisite for subsequent structural stress calculations and critical value analyses. The acquisition method of these material parameters can be calibrated through tensile tests, compression tests, or thermal analysis experiments, and adjusted according to the actual printing direction (such as the mechanical weakening in the Z-axis direction).

[0025] Step S152: Calculate the structural stress distribution based on the material property parameter data and the wall thickness uniformity evaluation data, and perform a critical load-bearing value analysis to obtain critical load-bearing data; In an embodiment of the present invention, the material property parameter data in step S151 and the wall thickness uniformity evaluation data generated in step S14 are coupled and input into a finite element analysis software (such as ANSYS Workbench) to construct a physical response model and calculate the overall structural stress distribution of the target 3D printing structure. The specific operation is as follows: First, apply actual load boundary conditions to the model. For example, uniformly apply a 500N compressive force to the top of the bionic scaffold model and fix the boundary constraint at the bottom. Use the Solid187 three-dimensional solid element for mesh division, and control the mesh size within 0.5 mm; then obtain the maximum principal stress, shear stress, and von Mises stress distributions of each element through the static analysis solution module. After the calculation is completed, further analyze the critical load response of each region, and define the "critical value" as the load state when the stress in the local region reaches more than 95% of the material yield strength. The system automatically extracts the stress distribution map and the load-bearing boundary region of the entire structure in this state and outputs it as critical load-bearing data. This data can be used to identify which regions are close to the mechanical failure threshold under the current design, facilitating the avoidance of sensitive areas with critical stress concentration during subsequent lightweight processing.

[0026] Step S153: Evaluate the structural load-bearing capacity based on the critical load-bearing data and the wall thickness uniformity evaluation data to obtain the current load-bearing capacity data; In the embodiment of the present invention, based on the critical load-bearing data output in step S152 and the wall thickness uniformity evaluation data in step S14, the overall load-bearing capacity of the current structure is comprehensively evaluated. The specific method is as follows: Compare the ratio between the current actual load level (such as the maximum force under the operating state of the equipment) and the critical bearing capacity value, and calculate the safety factor distribution map of the structure. If the safety factor in a local area is less than the design target value (for example, the target value is 2.0), then this area is marked as the "low reliability area", and the design thickness or the stiffener structure needs to be adjusted. In addition, considering the influence of the wall thickness uniformity index on the load transfer path, the system corrects the load distribution coefficient in the area with poor uniformity, reflecting the effect trend of the wall thickness change on stress concentration. The finally output current load-bearing capacity data includes key parameters such as the overall safety factor mean value, the lowest local safety factor, and the structural bearing trends in different directions (such as the Z-axis or the radial direction), which are used to support subsequent modeling prediction and lightweight iteration. Taking the lightweight support for aviation as an example, this data can be used to verify the reliability assessment of the structure under the flight stress environment, ensuring that the strength redundancy is not damaged on the basis of weight reduction.

[0027] Step S154: Fit the time series change trend of the current load-bearing capacity data, and establish a structural load prediction model based on the current load-bearing capacity data to obtain structural load prediction data.

[0028] In the embodiment of the present invention, taking the current load-bearing capacity data obtained in step S153 as the time series input basis, by simulating the stress response trends under different working conditions (such as temperature change, gradually increasing load), an evolution model of the structural bearing capacity with the change of service time or load is constructed. Using a polynomial fitting algorithm based on time window sliding, the change rate of the bearing capacity at each time point or load level is extracted to construct a time series curve model of the load-bearing capacity. Subsequently, a multivariable regression modeling method is adopted, with the wall thickness uniformity index, density distribution characteristics, and initial stress distribution as independent variables, and the current bearing capacity change as the dependent variable, to train and form a structural load prediction model, which can predict the response ability of the structure to new working conditions according to design changes. The model can use non-linear regression techniques such as random forest or LSTM neural network to improve the accuracy, and finally output structural load prediction data, including prediction curves, ultimate response trends, material yield point approach rates, etc. This data is crucial for intelligent lightweight design, which can guide designers to pre-avoid potential failure points and achieve "on-demand strengthening" and "intelligent weight reduction" of 3D printed structures. For example, when manufacturing high-performance racing car parts, the wall thickness and layout are optimized through the prediction model, thereby improving the acceleration stability and impact resistance.

[0029] Preferably, in step S2, detecting the optimization space attenuation trend of the material distribution density according to the structural load prediction data includes: The 3D printing model is divided into grids with a spacing of 1 mm - 20 mm according to the structural load prediction data to obtain the spatial load distribution data; Grid cells with a load-bearing strength lower than 60% of the design load threshold are screened according to the spatial load distribution data to obtain the low-load area distribution data; Based on the low-load area distribution data, the material density redundancy degree of each area is calculated to obtain the material density optimization potential data; According to the analysis of the material density optimization potential data, the density distribution law from the model center to the periphery is obtained to get the material density spatial attenuation data; Based on the material density spatial attenuation data, areas with an attenuation coefficient greater than 0.3 are marked and their volume proportion is counted to obtain the optimized space distribution data; According to the optimized space distribution data, the spatial attenuation trend characteristics when the volume proportion exceeds 25% are evaluated to obtain the optimized space attenuation trend of the material distribution density.

[0030] After obtaining the structural load prediction data, the embodiment of the present invention enters the spatial grid division processing stage based on the structural response distribution. The specific method is to perform a three-dimensional spatial division operation on the entire 3D printed model, and divide the model into multiple cubic voxel units according to the set grid spacing parameters. The division spacing ranges from 1mm to 20mm, which is flexibly selected according to the actual application scenario and modeling accuracy requirements. Taking high-precision medical implants as an example, the use of 1mm spacing can ensure the full reflection of local stress details; while for large non-structural shell components, a coarse resolution of more than 15mm can be selected to save computing resources. The division operation can be assisted by the "volume division" module in commercial CAE software (such as HyperMesh or COMSOL), and the average load value is calculated by interpolation in each grid unit through the structural load prediction data to generate the corresponding spatial load distribution data. This data records the size and direction of the load it bears in units of grid units, which is an important basis for identifying the rationality of material distribution and the strong and weak zoning of loading response. Based on the above spatial load distribution data, the load bearing strength of each grid unit is further analyzed and compared with the set design load threshold. The design load threshold is usually formulated based on the ultimate load or standard safety redundancy under the use conditions of the target product. For example, if the design ultimate load of an aviation bracket part is 600N, the 60% threshold corresponds to 360N. All grid cells are screened, and those areas with an average load-bearing strength lower than 60% of the threshold are retained to generate low-load area distribution data. Such low-load units are often located at the edge of the force path or in the transition zone of geometric features. The material structure contributes less to the overall load-bearing capacity and has a high potential for weight reduction. The data format is generally in the form of a three-dimensional point cloud or voxel grid, which records the spatial position, volume and corresponding stress level of each low-load unit, and is the basic input for subsequent calculation of material redundancy. After obtaining the low-load area distribution data, material redundancy analysis is performed on each low-load area, that is, to evaluate whether the current structure has material waste caused by "overdesign". The "material density redundancy degree" here is defined as the difference ratio between the current material density in a unit space and the minimum density required to realize the structure. In implementation, it is first assumed that the structure maintains the minimum material density required for 60% of the design strength in the low-load area, and the required density lower limit is deduced in combination with the mechanical properties of the printed material (such as elastic modulus). Then compare the actual density of the current structure in this area, for example, using the 3D model volume and material filling ratio (Infill Percentage) to calculate the actual density. Take a section with a current density of 1.2g / cm³ and a theoretical required density of 0.6g / cm³ as an example, the redundancy is 50%. By batch processing all low-load units, "material density optimization potential data" is generated. This data is used to quantitatively express which areas have excess density and is the primary judgment indicator for designing lightweight candidate areas.Based on the potential data of material density optimization, in order to further discover the weight reduction law and optimization path of the structure, the material density distribution trend from the center to the periphery of the entire model is modeled and analyzed. The specific operation is as follows: Taking the geometric center point of the model as the reference origin, multiple shell intervals are equally divided along the radial direction outward. The average material density and redundancy potential value of the grid cells contained in each interval are statistically analyzed, and the change trends of these indicators with the shell radius are analyzed. If it is found that with the increase of the distance from the center, the material density or redundancy potential decreases significantly (that is, the density redundancy in the edge region is higher), it indicates the existence of the "material spatial attenuation phenomenon". This phenomenon is common in shell-type components or non-uniform loading scenarios of structures and is the core basis for adjusting the material distribution strategy. Finally, "material density spatial attenuation data" is generated. This data is a set of distribution relationship data with distance as the independent variable and density or redundancy as the dependent variable, which is used to guide subsequent parametric design iterations. On the basis of obtaining the material density spatial attenuation data, the significance of the attenuation degree of each region is further evaluated to identify the lightweight potential region. This implementation step introduces the concept of "attenuation coefficient", which is defined as the ratio of the average density of a region to the density of its adjacent inner region. By calculating the attenuation coefficient of each shell region and screening out the regions with an attenuation coefficient greater than 0.3, it indicates that there is obvious redundancy in the material use of this region. Then, the proportion of this part of the region in the overall model volume is statistically analyzed. If the proportion is relatively high (for example, more than 25%), it indicates that there is strong density optimization space in the overall model. The final output is "optimization space distribution data", and its data format is usually a set of voxel units marked in the three-dimensional model structure, accompanied by volume proportion information, which is convenient for engineers to directly implement the "material removal" operation in CAD modeling. Finally, according to the optimization space distribution data, the material distribution trend and change amplitude of the entire model at different spatial positions are evaluated, so as to output the "optimization space attenuation trend of material distribution density". The specific method is to further analyze the high attenuation regions with a volume proportion exceeding 25%, and analyze the gradient change of its density attenuation along different spatial directions, such as whether the density change rate from the center to the edge of the model is continuous and whether there are mutation intervals. Combining topological optimization algorithms (such as the SIMP method or density penalty function), an optimization design function is constructed, and this trend is used as a constraint condition to reassign local density design values to the model. In typical applications, such as for the battery tray structure of new energy vehicles, if the optimization space attenuation trend shows high edge redundancy and continuous gradient, layer-by-layer thinning, hollowing or multi-material filling strategies can be implemented in this region, and finally a weight reduction of more than 20% can be achieved without affecting the structural safety performance. The results of this trend analysis can not only directly guide the iterative design of structural optimization, but also reverse verify the rationality of the load prediction model, providing data closed-loop support for realizing intelligent and precise 3D printing lightweight design.

[0031] Preferably, in step S2, predicting the dynamic interference intensity of removing redundant materials inside the model according to the optimization space decay trend includes: Performing a precision division process on the removable material area according to the optimization space decay trend with an accuracy of 0.5 mm - 5 mm to obtain redundant material removal unit data; Calculating the stress transfer influence range on adjacent areas after the removal of each unit based on the redundant material removal unit data to obtain stress influence diffusion data; Analyzing the load redistribution path during the material removal process according to the stress influence diffusion data to obtain load transfer path data; Predicting the structure deformation propagation intensity caused by the removal operation based on the load transfer path data to obtain deformation propagation intensity data; Evaluating the interference degree of the material removal process on the overall stability of the model according to the deformation propagation intensity data to obtain structure stability interference data; Quantifying the interference intensity coefficient based on the structure stability interference data and predicting the dynamic influence range of the removal operation to obtain redundant removal intensity data.

[0032] Based on the optimized spatial decay trend data of the material distribution density obtained in the previous stage, the embodiments of the present invention first perform a fine division operation on the marked high-density redundant regions that can be optimized to generate material units to be removed. In the specific operation, a local refinement grid division strategy with the optimized region as the boundary is adopted. Through finite element analysis software (such as Abaqus or Ansys) or a custom geometry processing program, a voxel division of 0.5 mm to 5 mm is implemented for this region. The division accuracy is set according to the geometric complexity and load sensitivity of the target part. For example, it can be set to 0.5 mm for micro-precision devices and 3 - 5 mm for large structural support components. Each division unit records its three-dimensional position, volume, and the information of the structural neighborhood that may be affected after removal, generating "redundant material removal unit data". This data is the carrier for subsequent stress transfer analysis and is the basis for achieving precise removal without damaging the structural continuity. After obtaining the redundant material removal unit data, a mechanical response simulation is performed on each unit to analyze the influence range of the stress state of the surrounding structure after its removal. The specific method is to use the static simulation module to compare and calculate the structure before and after the unit removal. A surrounding area (such as a spherical domain within 10 mm) is set with each removed unit as the center, and the change amplitude and influence range of the maximum principal stress in this area before and after the removal operation are evaluated. The stress influence diffusion data represents the adjacent regions to which the stress change can "diffuse" after a certain material unit is removed. For example, in an aviation connection node, if the stress change influence radius reaches 12 mm after removing a 3-mm voxel and the stress change within the affected area exceeds 15% of the original stress value, it is considered that this unit has a medium-high diffusion risk. The finally formed "stress influence diffusion data" will contain information such as unit identification, influence range radius, and stress change gradient, which is used for the next load path reconstruction. Based on the stress influence diffusion data, analyze the structural stress field reconstruction behavior caused by the removal of redundant materials, that is, the redistribution of the load path. The specific method is to use the force streamline visualization technology and the equivalent stress transmission network modeling method to track the path change of the overall load of the model from the load input point to the fixed boundary after each unit is removed. By comparing the stress principal vector flow direction and the stress concentration path change of the original model and the model after the removal operation, identify whether the original load path has shifted, branched, or deviated due to local material removal. Taking a complex support structure as an example, the original load is conducted along the central axis. After removing the high-redundancy edge materials, it is found that part of the load is transferred to the outer frame, indicating a path deviation trend. Finally, "load transfer path data" is obtained. This data is expressed in a path map structure, marking the starting point, ending point, change amplitude of each path, and the path weight change of the alternative path, which is an important basic data for predicting the structural response stability. Based on the load transfer path data, predict and analyze the deformation propagation effect of the structure after the material removal operation, and evaluate the reconstruction situation of the deformation chain caused by the stress path change.In the implementation, a modeling method of the deformation response propagation matrix between nodes is adopted to construct the mechanical coupling relationship between nodes in a three-dimensional structure. Through simulation calculation, the transfer coefficient of the displacement change of each node after the change of the load transfer path is obtained. For example, if the removal of material elements in a certain area of the model causes the displacement of five nearby nodes to increase by more than 20%, and this effect can continue to propagate to the far-end boundary nodes, it is considered that the deformation propagation intensity in this area is relatively high. Finally, "deformation propagation intensity data" is formed. This data records the maximum deformation increase value, the number of affected nodes and the propagation direction caused by each removal operation, and is a key reference index for controlling the risk of deformation cumulative instability. Based on the deformation propagation intensity data, the influence degree of material removal on the structural stability of the entire 3D printing model is further analyzed. The stability interference is defined here as: the degree of weakening of the overall stiffness matrix and the boundary condition constraint ability of the structure caused by the stress and deformation propagation resulting from local material removal. The evaluation method is: construct the overall finite element stiffness matrix before and after removal, and compare the change of the main vibration mode frequency, the reduction ratio of the critical buckling load and the change range of the displacement degrees of freedom of the displacement constraint boundary nodes in the modal analysis. For example, if the removal of redundant materials in the middle area of the car bumper reduces the lowest natural frequency by 10% and the stiffness of the nodes in the boundary area drops by 25%, it is considered that there is a structural stability interference in this area. The output "structural stability interference data" is given in the form of interference scores for each part of the structure. Each score value is comprehensively evaluated based on the stiffness reduction ratio, the modal change intensity and the distribution of deformation degrees of freedom, providing a quantitative basis for the risk control of material optimization. Based on the structural stability interference data, in order to further quantitatively control the potential structural risks brought by material removal, the concept of "interference intensity coefficient" is proposed, and the dynamic influence range that may be generated by redundant removal operations is predicted accordingly. The interference intensity coefficient is defined as the weighted index of the combined influence of stiffness weakening and deformation propagation caused by the material removal operation in a certain area, and is calculated by combining the aforementioned interference data and propagation intensity data. Then, the acceptable dynamic influence range of the model is defined according to different interference coefficient values. For example, when the interference intensity coefficient is less than 0.2, the influence on the structure after material removal is controllable, and local hollowing or gradient filling treatment can be implemented; when the coefficient is between 0.2 and 0.5, it is recommended to use embedded microstructures to replace the removal operation to maintain the continuity of load conduction. The finally formed "redundant removal intensity data" includes information such as the interference intensity value of the removal area, the volume of the affected area, and the recommended optimization method. It is the last layer of physical constraint verification before the structure intelligent lightweight system generates the final design model, and is the key control index for achieving both performance and material conservation.

[0033] Preferably, in step S2, determining the coupling balance trend of model lightweighting and strength retention based on the redundant removal intensity data and the optimization space attenuation trend includes: Establish the corresponding relationship between the material removal amount and the strength loss according to the redundant removal intensity data and the optimization space attenuation trend to obtain the strength loss correlation data; Calculate the change rate of the structural bearing capacity under different material removal ratios based on the strength loss correlation data to obtain the bearing capacity change data; Evaluate the trade-off relationship between the lightweight degree and the strength retention level according to the bearing capacity change data to obtain the lightweight strength trade-off data; Determine the safe threshold range of material removal based on the lightweight strength trade-off data to obtain the safe removal threshold data; Predict the balance state of the lightweight effect and the strength change within the threshold range according to the safe removal threshold data to obtain the balance state prediction data; Analyze the dynamic balance development direction of lightweight and strength retention based on the balance state prediction data to obtain the coupling balance trend of model lightweight and strength retention.

[0034] Based on the obtained redundant removal intensity data and optimized spatial attenuation trend, the embodiments of the present invention construct a mapping relationship between the material removal amount and the structural strength loss. The specific operation is as follows: First, taking the material removal units in different regions as the basic input parameters, call the redundant removal intensity data obtained previously, and set the amount of structural strength decline caused by a unit removal volume (for example, 1 cubic millimeter) for each unit (which can be represented by the percentage decrease in the principal stress). Then, combined with the optimized spatial attenuation trend data, perform a coupled analysis of the material density attenuation gradient and the mechanical sensitivity of its corresponding region to obtain the non-linear behavior of the structure's response to material removal. For example, in an aviation support structure, reducing 1 cubic millimeter of material in the edge redundant region only causes a 0.5% strength loss, while removing the same volume in the central skeleton region results in a 3%-5% strength reduction. Finally, establish a non-linear correspondence model between the material removal amount and the strength loss through interpolation or fitting, and output the "strength loss correlation data", which is used to simulate the structural performance response under different removal strategies. After obtaining the strength loss correlation data, further calculate the influence degree of different material removal ratios (such as 5%, 10%, 15%, 20%, etc.) on the overall structural bearing capacity. The specific method is as follows: Taking the models with different ratios of removed materials as the input, use the finite element simulation platform to conduct static load simulation tests, and record the maximum bearing load and the change in stress distribution. The bearing capacity change rate is defined as the percentage change in the maximum load that the structure can withstand after removal compared to the original structure. For example, in an automotive lightweight chassis structure, after removing 10% of the redundant materials, its maximum bearing capacity decreases by 8%, and when removing 15% of the materials, the decrease reaches 13%, showing a non-linear acceleration attenuation trend. Finally, form the "bearing capacity change data", which records the change rate of the maximum bearing value of the structure, the change range of the overall stiffness of the structure, and the increase in the deformation response under different removal ratios, and is used to support the safety judgment of subsequent lightweight design. Using the bearing capacity change data, comprehensively evaluate the trade-off relationship between the lightweight effect and the maintenance of structural strength. The specific operation is as follows: Construct a two-dimensional parameter balance map of the "lightweight rate" and the "bearing capacity retention rate", with the horizontal axis representing the ratio of material reduction and the vertical axis representing the ratio of the bearing capacity retained by the structure. On this basis, calculate the unit strength loss brought about by the reduction of unit weight (for example, how many Newtons of bearing capacity loss are caused by reducing 100g of material), and introduce an efficiency ratio parameter for evaluation. Taking a construction machinery arm as an example, if 92% of the bearing capacity is still maintained under a 10% mass reduction, the efficiency ratio is 0.92 / 0.1 = 9.2, which belongs to a cost-effective solution. The analysis result is output as the "lightweight strength trade-off data", which clarifies the cost of structural performance loss under different lightweight strategies and provides a theoretical basis for design selection and structural adjustment. According to the lightweight strength trade-off data, further determine the safe threshold range of material removal, that is, the maximum proportion of material that can be removed without significantly damaging the structural strength.The specific method is as follows: Set a bottom line for strength retention (for example, not less than 90% of the original load-bearing capacity), traverse the data of the lightweighting ratio and the change in load-bearing capacity, and screen out the maximum removal ratio that meets this condition. In the application of 3D printing the structural shell of an unmanned aerial vehicle, the experimental results show that within a removal ratio of 13%, the structure still retains 91.5% of its load-bearing capacity, so this ratio is marked as the "safe removal threshold". In addition, the strength sensitivity of different regions needs to be considered. Set a lower threshold (such as 5%) in the high stress concentration area, and it can be appropriately increased in the low stress area (such as 18%). Finally, output the "safe removal threshold data", which records the maximum acceptable material removal ratio for each region and serves as an important boundary condition for generating the lightweight printing model. Use the safe removal threshold data to predict the balance state between the lightweighting effect and the change in structural strength caused by the material removal operation within this threshold range. During implementation, through a combination of simulation calculations and empirical function fitting methods, estimate the weight reduction amount, the maximum structural deformation amplitude, and the overall stiffness retention ratio of the model after actual printing. Taking the structure of an electric vehicle battery tray as an example, after applying the safe threshold removal strategy, the overall weight is reduced by about 14%, the maximum value of the principal stress increases by no more than 9%, and the displacement increase is controlled within 3 mm, indicating that the structure has reached the "balance point between mechanics and lightweighting" at this time. The prediction results are output in the form of "balance state prediction data", which records the lightweighting effect, the increase in structural response, and its stability coefficient, and is the evaluation benchmark for generating the printing model. Based on the obtained balance state prediction data, further analyze the coupled balance development trend between lightweighting and strength retention from the perspective of the dynamic structural performance evolution. The specific method is as follows: Based on the multi-stage simulation data under different material removal ratios, construct a joint response surface of the lightweighting degree and the strength retention ability, and observe its change trajectory under different application loads and boundary constraint conditions. Extract the coupling mode between key variables through principal component analysis and trend line fitting techniques. For example, it is found that when the lightweighting rate exceeds 15%, the structural strength retention curve shows an inflection point and decreases, that is, the system changes from a stable state to a critical state. Combine the actual product requirements (such as whether it is used in high-speed movement, high-impact load scenarios) to determine its design evolution direction. The output "coupled balance trend of model lightweighting and strength retention" will provide a visual design reference to help designers understand the stable path and performance critical points during the structural evolution process, and support the formulation of high-quality and verifiable 3D printing product lightweighting strategies.

[0035] Preferably, step S3 includes the following steps: Step S31: Identify the support requirement areas of the 3D printing model according to the coupled balance trend to obtain the support requirement distribution data; After obtaining the coupling balance trend of model lightweighting and strength retention in the embodiments of the present invention, the lightweight 3D printing model is further processed to identify the regions requiring support. The specific operation is as follows: Import the three-dimensional model after lightweight design, call the CAD software or structural analysis module to extract the geometric features of the model's shape, and combine with the gravity direction to identify the overhanging structures with vertical suspension, bridging structures with large spans, and local thin-walled or sharp-corner structures in the model. These regions may cause printing failures or deformations due to the lack of support from the underlying material during printing, and are therefore defined as "regions requiring support". For example, when printing a lightweight drone housing, there is an overhanging extension structure with a thickness of 2.5 mm at the edge of the lower battery compartment floor, with a span of 18 mm. This region is determined to be a high-risk support requirement area. Through the above identification process, "support requirement distribution data" is generated, which marks the positions and area ranges of all locations where support structures need to be added in the form of spatial coordinates.

[0036] Step S32: Calculate the overhanging angle and span distance for the support requirement distribution data to obtain the support layout reference data; In the embodiments of the present invention, based on the support requirement distribution data, the overhanging angle and span distance of each region are calculated to generate a design basis for subsequent support layout. During implementation, the triangular method is used to calculate the angle between the normal vector of the model surface and the printing direction. An angle greater than 45 degrees (usually the threshold for non-self-supporting in FDM printing) is identified as an overhanging surface; and the actual overhanging length and bridging span (i.e., the horizontal projection distance between adjacent support points) corresponding to each support requirement region are calculated. For example, when printing a lightweight robot arm assembly, it is found that there is an inclined surface with an overhanging angle of 63 degrees in the shoulder connection area, and its span below is 22 mm, which significantly exceeds the self-supporting span allowed by the process. Finally, "support layout reference data" is output, including the overhanging angle matrix, span distance vector, and regional risk level, which is used to guide the selection of support structure types and distribution strategies.

[0037] Step S33: Evaluate and calculate the support density requirements for each region of the 3D printing model based on the support layout reference data to obtain the support density requirement data; In the embodiments of the present invention, the support density requirements for each region of the model are quantitatively evaluated using support layout reference data. Specifically: according to the size of the overhang angle and the length of the span distance, a support density classification standard is set. For example, a region with an angle greater than 60 degrees and a span exceeding 20 mm is defined as a high-density support area, with 5 support points set per square centimeter; a region with an angle between 45 - 60 degrees and a span between 10 - 20 mm is defined as a medium-density area, with 3 support points set per square centimeter; and the rest are low-density support areas. Taking a lightweight aviation joint printed by 3D printing as an example, the bottom connection edge is a high-density area, with a support density requirement of 5 points / cm², and the middle arc surface is a medium-density area with 3 points / cm². By traversing the entire model, "support density requirement data" is generated, which provides a quantitative basis for the rationality analysis of the next support structure layout.

[0038] Step S34: Compare and analyze the support density requirement data with the pre-obtained actual support layout plan to obtain the deviation situation of the support structure layout requirements; In the embodiments of the present invention, the support density requirement data is compared and analyzed with the pre-generated actual support layout plan to judge the deviation of the support structure distribution. Specifically: call a printing path generation tool (such as Ultimaker Cura or Materialise Magics) to extract the actual support layout plan used by the current model, count the number of support points and the layout density in each support requirement region, and compare the difference with the aforementioned density requirement data. If the actual support density deviates from the expectation by more than 20%, it is considered that there is a layout deviation. For example, for a lightweight printed medical device clamp, the required density in its side wall region is 4 points / cm², while the actual layout is only 2 points / cm², which is insufficient support; and the requirement for the bottom plane region is 2 points / cm², and the actual value reaches 5 points / cm², which is over-dense support, both of which need to be adjusted and optimized. Output the "deviation situation of the support structure layout requirements", record all support regions that do not meet the design requirements and their deviation amounts, for reference in subsequent structural performance verification.

[0039] Step S35: Analyze and calculate the load sharing situation of multiple support points based on the deviation situation of the support structure layout requirements to obtain the support point load distribution data; calculate the load transfer coordination between each support point according to the support point load distribution data, and predict the deterioration trend of the synchronous bearing of multiple support points; In the embodiments of the present invention, based on the deviation condition of the support structure layout requirements, mechanical simulation calculations are performed on the load sharing of the support points. Using the finite element analysis method, an overall mechanical model of the model and its support structure is established, and self-weight and typical printing process loads (such as cooling shrinkage stress and interlayer shear force) are applied. The vertical force and lateral component force borne by each support point during the entire printing process are calculated, and then it is judged whether a reasonable load sharing relationship is formed. Further, the load transfer coordination among multiple support points in terms of time and space is analyzed, that is, when the bearing capacity of the support points in a certain area is insufficient, whether the surrounding support points can share the load in time to prevent local warping or collapse. Taking the printing of a lightweight heat exchanger component as an example, there are 6 support points at its overhanging lower edge. The simulation finds that 2 of these points bear too much load prematurely, resulting in local deformation, indicating a problem of poor coordination. Based on the load response data, "support point load distribution data" is established, and combined with the time series analysis method, the downward trend of the ability of multiple support points to bear loads synchronously during the printing process, that is, the "synchronization deterioration trend", is predicted, which is used to identify in advance the risk of possible support failure.

[0040] Step S36: Evaluate the difference degree between the material consumption and the bearing efficiency of the support structure according to the synchronization deterioration trend.

[0041] In the embodiments of the present invention, based on the obtained synchronization deterioration trend, the difference degree between the material consumption of the support structure and its bearing efficiency is comprehensively evaluated. The specific method is as follows: The material usage of each support area is counted, and the average load size that can be borne by the support material per unit volume is calculated, which is defined as the "support bearing efficiency". At the same time, a comparison index is introduced, and the current support efficiency is analyzed by taking the ratio with the theoretical efficiency in the ideal state (synchronization of each support point, minimum total support amount, no load concentration). For example, in the bionic beam structure of lightweight printing, the bottom support consumes about 28 cm³ of support material, and the average unit bearing efficiency is only 0.45 N / mm³, while the efficiency is increased to 0.68 N / mm³ under the optimized distribution, with an increase of more than 50%. Finally, the evaluation results are output, including the support structure material redundancy rate, the support efficiency loss rate, and the optimization potential ratio, which are used for the process feedback and cost control of the lightweight printing strategy, providing a basis for realizing stable printing with the minimum material input.

[0042] Especially importantly, step S36 includes the following steps: Step S361: Statistically calculate the material usage of each support structure according to the synchronization deterioration trend to obtain the support material consumption data; Based on the synchronization deterioration trend data obtained in the previous embodiment, for the support structure areas with coordinated bearing problems, the material consumption of each support structure is statistically analyzed to obtain complete support material consumption data. In specific operations, the 3D printing model after lightweight design is imported into slicing software (such as Simplify3D or PrusaSlicer), the support analysis plug-in is enabled, and the support structures are divided into independent units by region and their volume information is extracted. Taking the lightweight building component with a honeycomb structure as an example, the three regions of the lower edge support, arc surface support, and internal through-hole support are statistically analyzed respectively, and the corresponding volumes are 10.5 cm³, 6.2 cm³, and 3.8 cm³. In addition, the structural type (columnar, grid-like, dendritic) and its generation method (automatically generated or manually edited) of each support segment are also recorded as the basis for subsequent efficiency analysis.

[0043] Step S362: Calculate and evaluate the actual bearing capacity of each support structure based on the support material consumption data to obtain support bearing capacity data; In the embodiment of the present invention, after obtaining the volume data of the support material, the actual bearing capacity of each support structure is further calculated and evaluated to generate support bearing capacity data. This step is realized by finite element simulation: the model is imported into a structural simulation platform (such as ANSYS or Abaqus), the printing material properties are set in each support area (for example, the elastic modulus of PLA material is 3200 MPa and the Poisson's ratio is 0.35), and the loads simulated during the printing process (including the self-weight of the model, thermal shrinkage reaction force, and interlayer tensile force) are applied at the support endpoints to observe its maximum bearing capacity and instability point. For example, for the bottom columnar support, a gradually increasing pressure is applied to its upper surface until the structure undergoes plastic deformation or local buckling, and the ultimate load is recorded as 78 N. After similar analysis of other regions, the maximum bearing value corresponding to each support structure is output to form "support bearing capacity data".

[0044] Step S363: Calculate the bearing efficiency value per unit material according to the support bearing capacity data to obtain unit material bearing efficiency data; In the embodiment of the present invention, the support bearing capacity data in the previous step is calculated with the corresponding material consumption to obtain the payload that can be borne by each unit of support material, and the unit material bearing efficiency data is generated. In the specific processing process, the support ultimate load is divided by its volume, and this ratio is defined as the "unit material bearing efficiency", with the unit of N per cubic millimeter. For example, the material used for the bottom columnar support is 10.5 cm³ and the bearing capacity is 78 N, then its unit bearing efficiency is about 0.743 N per cubic millimeter; while the middle arc surface support has a bearing capacity of only 28 N under the support of 6.2 cm³ of material, and the efficiency is 0.452 N / mm³. Finally, these results are combined into an efficiency data matrix with the region ID and the efficiency value as the basis for subsequent efficiency difference comparative analysis.

[0045] Step S364: Based on the unit material bearing efficiency data, compare and analyze the efficiency performance of different support regions to obtain region efficiency comparison data; In the embodiment of the present invention, based on the unit material bearing efficiency data, the efficiency performance between different support regions is compared and analyzed to obtain region efficiency comparison data. In this step, the dimension of the efficiency values of each region is unified through normalization, and the efficiency distribution difference is displayed in the form of a chart or a heat map. The specific method is: select the region with the highest efficiency value as the reference standard, and take the ratio of the unit efficiency of other regions to this region as the relative efficiency level. For example, if the efficiency of the bottom support region is 0.743 N / mm³ and is set to 100%, then the relative efficiency of the arc surface support region is about 60.8%, and the relative efficiency of the support inside the pore channel is about 74%. Combine the spatial position identifier to generate "region efficiency comparison data", and regions with efficiency significantly lower than the average value can be further marked for structural optimization.

[0046] Step S365: Calculate the standard deviation and coefficient of variation of the efficiency values of each region according to the region efficiency comparison data to obtain efficiency difference quantification data; In the embodiment of the present invention, after obtaining the region efficiency comparison data of each region, the standard deviation and coefficient of variation of the efficiency values of each region are further calculated to quantify the overall difference degree of the support structure efficiency and generate efficiency difference quantification data. The standard deviation represents the absolute dispersion degree of the efficiency between different regions, and the coefficient of variation represents the relative dispersion level of the efficiency value (that is, the standard deviation divided by the average efficiency value). For example, the unit efficiencies of multiple regions are 0.743, 0.452, 0.615, 0.578 N / mm³ respectively. It can be calculated that the standard deviation is about 0.11 N / mm³, the average value is 0.597 N / mm³, and the coefficient of variation is 18.4%. These statistics help to judge whether the support efficiency distribution is too concentrated or dispersed. If the coefficient of variation is higher than 20%, it indicates that there is a large imbalance in the current support layout, which may lead to the coexistence of material redundancy and local structural failure. Finally, "efficiency difference quantification data" is generated for feedback on the optimization strategy.

[0047] Step S366: Evaluate the efficiency difference degree between the material consumption and load bearing of the support structure based on the efficiency difference quantification data.

[0048] In the embodiment of the present invention, the efficiency difference quantification data is used to comprehensively evaluate the efficiency difference degree between the material consumption and the actual load bearing of each support structure, so as to judge the optimization space of the overall support scheme. In this step, the "efficiency loss rate" index is introduced, that is, the ratio of the total material amount in the low-efficiency area, which is used to evaluate the waste degree of the overall support. Specifically: the area with an efficiency lower than 80% of the overall average value is defined as the "low-efficiency area", and the total volume of its materials and the ratio of the volume of all support materials are statistically calculated. For example, in a mechanical tray structure for lightweight printing, the total volume of the support material is 21 cm³, and the support material in the low-efficiency area is 7.2 cm³, then the efficiency loss rate is 34.3%, indicating that more than one-third of the support structure has low efficiency and should be optimized by reconstruction or redistribution. Finally, the evaluation result of this step is output to form an "Evaluation Report on the Efficiency Difference between the Material Consumption and Load Bearing of the Support Structure", providing a quantitative basis and optimization direction for the iteration of the support scheme of lightweight products.

[0049] Preferably, step S4 includes the following steps: Step S41: Evaluate the adaptability of the current printing process parameters according to the efficiency difference degree between the material consumption and load bearing of the support structure, and obtain the process parameter adaptability data; In the embodiment of the present invention, after the evaluation of the efficiency difference between the material consumption and load bearing of the support structure is completed, the adaptability of the process parameters to the efficiency performance of the support structure is evaluated by comparing and analyzing the efficiency difference degree with the process parameters used in the current printing, and the process parameter adaptability data is obtained. The specific method is to extract the core process parameters recorded during the printing process, including the nozzle temperature (such as 210 °C), printing speed (such as 60 mm / s), layer thickness (such as 0.2 mm), etc., and perform a correlation analysis in combination with the distribution of the unit load bearing efficiency of the support structure. The regression analysis method is used to identify the relationship between the parameter change and the efficiency difference. For example, it is found that when a low layer thickness (0.1 mm) and a low speed (40 mm / s) are used in a certain batch, the low-efficiency area is significantly reduced, indicating that the current parameter combination has a positive impact on the support efficiency, and the adaptability evaluation is high. Finally, the "process parameter adaptability data" is generated, recording the contribution degree and adaptability score of each parameter to the efficiency performance.

[0050] Step S42: Identify the matching risk levels of temperature, speed, and layer thickness based on the process parameter adaptability data, and obtain the matching risk situation of the printing process parameters; After obtaining the process parameter adaptability data, the embodiments of the present invention further identify the risk levels under different parameter combinations, particularly focus on the matching situation among the three parameters of temperature, speed, and layer thickness, and generate the matching risk situation of the printing process parameters. In specific implementation, a risk level classification standard is introduced. For example, when the temperature is too high and the speed is low, it is easy to cause over-melting and collapse, which belongs to high risk; when the speed is too fast and the layer thickness is high, it causes insufficient interlayer bonding, which belongs to medium risk; when the temperature, speed, and layer thickness are all within a reasonable range and coordinated with each other, it belongs to low risk. When actually printing a lightweight support structure part, the parameter combinations of the test group are input into the risk assessment model, and risk scores are given to five typical combinations among them, and it is identified that two of them have high matching risks (such as 230°C + 80 mm / s + 0.3 mm), resulting in warping problems between printing layers. The matching risk levels and position identifiers of each parameter combination are output to form the "matching risk situation of the printing process parameters".

[0051] Step S43: Analyze the deviation between the expected value and the actual value of the bonding strength between slices according to the matching risk situation of the printing process parameters, and obtain the bonding strength deviation data between slices; Based on the identified high-risk parameter combinations, the embodiments of the present invention evaluate the bonding strength between different slices through actual tests combined with simulations, analyze the deviation degree between the expected value and the actual value, so as to obtain the bonding strength deviation data between slices. In implementation, the standard interlayer bonding stress in the structural simulation model is used as the expected value (for example, the theoretical bonding strength is set to 8 MPa for PLA material), and the actual value is obtained by using a micro tensile test after printing the sample (using a universal material testing machine) to measure the actual bonding strength of different layers. For example, in the risk group sample, the actual bonding strength of the bottom 10 layers is measured to be 4.5 MPa, and the middle layer is 6.2 MPa, and the deviations from the expected value are 43.75% and 22.5% respectively. The deviation values are recorded layer by layer to form the "bonding strength deviation data between slices", and the severely deviated areas are marked.

[0052] Step S44: Trace back the process parameter combinations that cause abnormal bonding strength based on the bonding strength deviation data between slices, and obtain the abnormal bonding strength traceability data between slices; After obtaining the data on the deviation of the interlayer bonding strength in the embodiments of the present invention, trace back the process parameter combinations that cause prominent deviations, conduct root cause analysis of anomalies, and output the trace data of the abnormal interlayer bonding strength of the slices. The specific method is to conduct correlation analysis between the slice layer information corresponding to the layer segments with deviations higher than 20% of the average level and the printing parameter logs, and identify the temperature, speed, and layer thickness values adopted by the abnormal strength layer segments. For example, in a batch of sample parts, it is found that the central region with prominent deviations uses a combination of a temperature of 230 °C, a speed of 100 mm / s, and a layer thickness of 0.2 mm. After tracing, it is confirmed that the parameters are temporarily adjusted and not synchronously adjusted with the material flow rate, resulting in poor bonding. Finally, "trace data of the abnormal interlayer bonding strength of the slices" is formed, and each set of data includes the abnormal layer segment number, parameter combination, deviation amplitude, and remarks analysis.

[0053] Step S45: Adjust the filling density, filling pattern, and printing path parameters according to the trace data of the abnormal interlayer bonding strength of the slices to obtain the adjusted filling parameter data; Based on the trace results, the embodiments of the present invention optimize the filling strategy for the printing areas that cause abnormal interlayer bonding strength, focus on adjusting the filling density, filling pattern, and printing path parameters, and output the adjusted filling parameter data. During implementation, set the filling parameters for the local area through slicing software (such as Cura). For example, for the area where the bonding strength deviation exceeds 30%, increase the filling density from the original 10% to 20%, change the filling pattern from linear to triangular honeycomb, and at the same time enable the "staggered path" function to enhance the local interlayer cross-linking strength. For the printing path, change from a zigzag to a spiral filling from the inside out to enhance the uniformity of the thermal gradient. When printing a lightweight grid frame support part, apply the above parameters to locally enhance the key middle layer segments to obtain "adjusted filling parameter data", including the values before and after parameter adjustment, adjustment position, optimization purpose, and expected impact.

[0054] Step S46: Verify the degree to which the adjusted parameters meet the lightweight target and strength requirements based on the adjusted filling parameter data to obtain the parameter verification data; In the embodiments of the present invention, after adjusting the filling parameters, print the sample parts again and conduct performance tests to verify their impact on the lightweight target and strength requirements, evaluate whether the adjustment is reasonable, and form the parameter verification data. During implementation, print the adjusted sample parts and conduct quality measurement and structural strength tests. The lightweight index is calculated by comparing the total mass of the sample parts with the original mass (for example, the original sample part is 58 g, and after optimization, it is 42.8 g, with a weight reduction rate of 26.2%). The strength index is evaluated by comparing the maximum bearing load with the original design value (for example, the original bearing capacity is 120 N, and after adjustment, it is 110 N, with a retention rate of 91.7%). The interlayer bonding strength error is controlled within ±0.3 MPa. Comprehensively evaluate the adjustment effect of these data to generate "parameter verification data", including the verification results in dimensions such as the lightweight achievement rate, bearing strength retention rate, and interlayer bonding deviation amplitude.

[0055] Step S47: Select a printing filling parameter configuration that simultaneously satisfies a material weight reduction rate greater than 25%, a structural load-bearing strength retention rate not lower than 90%, and an interlayer bonding strength deviation less than 5% according to the parameter verification data, and obtain optimized printing filling parameter data.

[0056] In the embodiment of the present invention, according to the foregoing parameter verification data, the optimal printing filling parameter configuration that simultaneously meets the requirements of the three indicators is screened out from all test parameter combinations: that is, the material weight reduction rate is greater than 25%, the structural load-bearing strength retention rate is not lower than 90%, and the interlayer bonding strength deviation is less than 5%. Finally, optimized printing filling parameter data is obtained. In specific operations, filter conditions are established, and all test sample data is imported into an Excel or database analysis system, and each combination is screened in turn to see if it meets the three hard indicators. If there are multiple combinations that meet the conditions, the one with the highest structural strength retention rate is preferentially selected as the recommended configuration. For example, the finally determined optimal combination is a filling density of 15%, a filling pattern of honeycomb, a path strategy of staggered inward, a printing temperature of 210 °C, a speed of 60 mm / s, and a layer thickness of 0.15 mm. Output "optimized printing filling parameter data" as the standard process configuration for subsequent official printing of lightweight products.

[0057] Particularly importantly, step S5 includes the following steps: Perform a material distribution density mapping process on the internal space of the model according to the optimized printing filling parameter data to obtain three-dimensional space material density distribution data; Construct a material filling strategy for different regions based on the three-dimensional space material density distribution data to obtain sub-region material filling data; Generate a corresponding printing path coordinate sequence according to the sub-region material filling data to obtain basic printing path data; Optimize the printing order and path connection method based on the basic printing path data to reduce material waste and obtain lightweight printing path planning data; Convert the printing path planning scheme into a control instruction code executable by the printer to obtain digital control instruction data; Integrate printing parameters, path information, and material control commands based on the digital control instruction data to obtain digital control model data; Output a lightweight digital control model that can be directly used for 3D printing equipment according to the complete digital control model data.

[0058] In the embodiments of the present invention, the internal space of the model is processed by material distribution density mapping according to the optimized data of printing filling parameters, that is, based on the CAD data of the three-dimensional model, combined with the optimized filling parameter configuration selected in the previous step, the density distribution at the voxel level is carried out according to the structural strength and functional requirements of different regions. In operation, the model is divided into several mesh units by using the finite element analysis results, and the most suitable material filling density is calculated for each unit. For example, it is set to be above 0.8 in the connection support area with greater load-bearing capacity, and about 0.2 in the secondary decorative area, and the material filling rate corresponding to each voxel position in the three-dimensional space is generated, so as to form the complete three-dimensional space material density distribution data. This data is usually stored in units of volume pixels (voxels) and presented as different grayscale or color density layers after visualization. After obtaining the three-dimensional space material density distribution data, continue to implement the construction of the material filling strategy for each region. The specific method is to perform spatial clustering processing on the model based on the spatial density distribution map, and classify the voxels with adjacent or similar filling rates into the same region. For example, use density-based spatial clustering algorithms (such as DBSCAN or region growing algorithms) to identify high-density strengthening regions, medium-density functional regions, and low-density lightweight regions, and formulate corresponding material filling strategies for each type of region: for example, use honeycomb structures to fill the high-density regions to enhance structural stability, use triangular grids to fill the medium-density regions to balance strength and material consumption, and use linear or hollow filling in the low-density regions to reduce weight. The final output is a set of regional material filling data including spatial coordinates and corresponding filling strategies. Generate the corresponding printing path coordinate sequence based on the regional material filling data. In this process, use slicing software (such as Cura, Slic3r) to slice the three-dimensional model layer by layer, combine the regional division information of each layer with the filling strategy data, and call the preset path planning algorithm to generate the coordinate points of the specific filling path trajectory for each layer. For example, for the honeycomb filling area, the path planning will layout the coordinate points along the diagonal direction of the hexagon, and for the linear filling, directly generate linear trajectories in parallel or cross directions. This path information is output in the form of G-code or other structured formats to form the basic printing path data for subsequent path optimization processing. Optimize the lightweight printing path based on the basic printing path data. This step mainly solves the problem of material waste caused by frequent path switching or uneven material accumulation in traditional path planning. The optimization methods include sequential optimization and connection method optimization: sequential optimization reduces the nozzle idle travel through the path sorting algorithm (such as greedy nearest neighbor), and the connection method optimization uses jump connection or spiral connection methods to reduce the number of path interruptions, thereby improving the printing efficiency and reducing material redundancy. After optimization, the path data is regenerated, which is the lightweight printing path planning data, and this data better meets the balance requirements of material saving and structural strength. After the printing path planning data is generated, it needs to be converted into a control instruction code recognizable by the printer.Operationally, the call path translation module encodes the path coordinate information and control parameters such as speed, temperature, and layer height corresponding to the filling strategy into a command sequence in G-code format, and performs syntax matching and instruction encapsulation considering the printer hardware characteristics. For example, it attaches the corresponding nozzle temperature setting (M104), material extrusion speed (E value), etc. to the movement command (G1) corresponding to each path segment, and outputs digital control instruction data that can be executed by a specific printer. Based on the above digital control instruction data, integrated printing parameters, path information, and material control commands are formed to create complete digital control model data. During this process, a unified data structure file (such as in JSON or XML format) is constructed, which contains data content such as material filling density distribution, path trajectory points, nozzle control parameters, and cooling fan control timing, and provides data interfaces for different brands or models of printing devices to read, thus realizing integrated model-process-device mapping control. According to the constructed digital control model data, a lightweight digital control model that can be directly used in 3D printing devices is output. In specific applications, this control model can be directly imported into FDM devices such as Ultimaker and Raise3D, and the printing task is executed through the device's built-in control program, realizing a printing process that precisely executes according to the optimization strategy, ensuring that the finished product meets the goals of a material weight reduction rate greater than 25% and a structural strength retention rate higher than 90%. For example, when printing a lightweight drone bracket, the system can automatically distribute the material density in different areas, achieve central reinforcement and edge weight reduction, and reduce the overall material cost and weight while ensuring flight strength.

[0059] The present invention also provides a 3D printing product lightweight design system for implementing the above-mentioned 3D printing product lightweight design method. The 3D printing product lightweight design system includes: A geometric feature analysis module for obtaining the geometric data information of the 3D printing model; extracting the internal space volume distribution characteristics of the model and evaluating the wall thickness uniformity distribution of the model from the geometric data information, and predicting the change trend of the model's structural load-bearing capacity to obtain structural load prediction data; A material optimization analysis module for detecting the optimization space attenuation trend of the material distribution density according to the structural load prediction data; predicting the dynamic interference intensity of removing redundant materials inside the model according to the optimization space attenuation trend to obtain redundant removal intensity data; determining the coupling balance trend of model lightweighting and strength retention based on the redundant removal intensity data and the optimization space attenuation trend; A support structure optimization module for predicting the deviation status of the support structure layout requirements using the coupling balance trend; predicting the synchronous deterioration trend of the coordinated load-bearing of multiple support points based on the deviation status of the support structure layout requirements; evaluating the efficiency difference between the material consumption and load-bearing of the support structure according to the synchronous deterioration trend; The process parameter optimization module is used to evaluate the matching risk situation of printing process parameters based on the efficiency difference degree; trace the abnormal bonding strength between slices based on the matching risk situation of printing process parameters, and optimize the printing filling parameters to obtain the optimized data of printing filling parameters; The path planning and generation module is used to construct a three-dimensional space material distribution model according to the optimized data of printing filling parameters, and generate a lightweight printing path planning scheme; output a lightweight digital control model of the 3D printed product according to the printing path planning scheme.

[0060] The present invention also provides a computer medium storing a computer program, and when the computer program is executed, the lightweight design method of the 3D printed product described above is implemented.

[0061] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be included in the present invention.

[0062] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A lightweight design method for 3D printed products, characterized in that, It includes the following steps: Step S1: Obtain the geometric data information of the 3D printing model; extract the internal space volume distribution characteristics of the model and evaluate the wall thickness uniformity distribution of the model, and predict the change trend of the model structure's load-bearing capacity to obtain the structure load prediction data; Step S2: Detect the optimization space attenuation trend of the material distribution density according to the structure load prediction data; predict the dynamic interference intensity of removing redundant materials inside the model according to the optimization space attenuation trend to obtain the redundant removal intensity data; Determine the coupling balance trend of model lightweight and strength maintenance based on the redundant removal intensity data and the optimization space attenuation trend; Step S3: Use the coupling balance trend to predict the deviation of the support structure layout requirements; predict the deterioration trend of the synchronization of multi-support point coordinated bearing based on the deviation of the support structure layout requirements; Evaluate the efficiency difference degree between the material consumption and bearing of the support structure according to the synchronization deterioration trend; Step S4: Evaluate the matching risk situation of the printing process parameters based on the efficiency difference degree; Trace the abnormality of the interlayer bonding strength of the slice based on the matching risk situation of the printing process parameters, and optimize the printing filling parameters to obtain the optimized printing filling parameter data; Step S5: Construct a three-dimensional space material distribution model according to the optimized printing filling parameter data, and generate a lightweight printing path planning scheme; output a lightweight digital control model of the 3D printing product according to the printing path planning scheme.

2. The lightweight design method of the 3D printing product according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the geometric data information by scanning the surface of the 3D printing model; perform three-dimensional coordinate point cloud extraction processing on the geometric data information to obtain the model surface contour data; Step S12: Calculate and process the outer surface curvature distribution of the model according to the model surface contour data to obtain the surface curvature distribution data; Step S13: Perform internal space volume division processing on the surface curvature distribution data to obtain the space volume distribution data; perform statistical analysis processing on the internal space density of the model according to the space volume distribution data to obtain the internal space density distribution data; perform feature extraction on the internal space density distribution data to obtain the model internal space volume distribution feature data; Step S14: Obtain the wall thickness measurement data of each section of the model, calculate the thickness change gradient of the wall thickness measurement data, and perform wall thickness uniformity index evaluation to obtain the wall thickness uniformity evaluation data; Step S15: Use the wall thickness uniformity evaluation data to predict the change trend of the model structure's load-bearing capacity to obtain the structure load prediction data.

3. The lightweight design method of the 3D printing product according to claim 2, wherein Step S15 includes the following steps: Step S151: Obtain the model material property parameter data; Step S152: Calculate the structural stress distribution according to the material property parameter data and the wall thickness uniformity evaluation data, and perform load-bearing critical value analysis to obtain the load-bearing critical data; Step S153: Evaluate the structure load-bearing capacity based on the load-bearing critical data and the wall thickness uniformity evaluation data to obtain the current load-bearing capacity data; Step S154: Fit the time-series change trend of the current load-bearing capacity data, and establish and process the structural load prediction model based on the current load-bearing capacity data to obtain the structural load prediction data.

4. The lightweight design method of the 3D printing product according to claim 3, wherein The detection of the optimization space attenuation trend of the material distribution density according to the structural load prediction data in Step S2 includes: Perform a grid spacing division process on the 3D printing model with a grid spacing of 1 mm - 20 mm according to the structural load prediction data to obtain the spatial load distribution data; Screen the grid cells with a load-bearing strength lower than 60% of the design load threshold according to the spatial load distribution data to obtain the low-load area distribution data; Calculate the material density redundancy degree of each area based on the low-load area distribution data to obtain the material density optimization potential data; Analyze the density distribution law from the model center to the periphery according to the material density optimization potential data to obtain the material density spatial attenuation data; Mark the areas with an attenuation coefficient greater than 0.3 based on the material density spatial attenuation data and count their volume proportion to obtain the optimization space distribution data; Evaluate the spatial attenuation trend characteristics when the volume proportion exceeds 25% according to the optimization space distribution data to obtain the optimization space attenuation trend of the material distribution density.

5. The lightweight design method of the 3D printing product according to claim 4, characterized in that The prediction of the dynamic interference intensity of removing redundant materials inside the model according to the optimization space attenuation trend in Step S2 includes: Perform a precision division process on the removable material area with a precision of 0.5 mm - 5 mm according to the optimization space attenuation trend to obtain the redundant material removal unit data; Calculate the stress transfer influence range of each unit on the adjacent area after removal based on the redundant material removal unit data to obtain the stress influence diffusion data; Analyze the load redistribution path during the material removal process according to the stress influence diffusion data to obtain the load transfer path data; Predict the structural deformation propagation intensity caused by the removal operation based on the load transfer path data to obtain the deformation propagation intensity data; Evaluate the interference degree of the material removal process on the overall stability of the model according to the deformation propagation intensity data to obtain the structural stability interference data; Quantify the interference intensity coefficient based on the structural stability interference data and predict the dynamic influence range of the removal operation to obtain the redundant removal intensity data.

6. The lightweight design method of the 3D printing product according to claim 5, wherein The determination of the coupling balance trend of model lightweighting and strength retention based on the redundant removal intensity data and the optimization space attenuation trend in Step S2 includes: Establish the corresponding relationship between the material removal amount and the strength loss according to the redundant removal intensity data and the optimization space attenuation trend to obtain the strength loss correlation data; Calculate the change rate of the structural load-bearing capacity under different material removal ratios based on the strength loss correlation data to obtain the load-bearing capacity change data; Evaluate the trade-off relationship between the lightweighting degree and the strength retention level according to the load-bearing capacity change data to obtain the lightweighting-strength trade-off data; Determine the safe threshold range of material removal based on the lightweighting-strength trade-off data to obtain the safe removal threshold data; Predict the balance state of the lightweighting effect and the strength change within the threshold range according to the safe removal threshold data to obtain the balance state prediction data; Analyze the dynamic balance development direction of lightweighting and strength retention based on the balance state prediction data to obtain the coupling balance trend of model lightweighting and strength retention.

7. The lightweight design method of the 3D printing product according to claim 6, characterized in that, Step S3 includes the following steps: Step S31: Identify the support requirement areas of the 3D printing model according to the coupling balance trend to obtain the support requirement distribution data; Step S32: Calculate the overhang angle and span distance of the support requirement distribution data to obtain the support layout reference data; Step S33: Evaluate and calculate the support density requirements for each area of the 3D printing model based on the support layout reference data to obtain the support density requirement data; Step S34: Compare and analyze the support density requirement data with the pre-acquired actual support layout plan to obtain the deviation status of the support structure layout requirements; Step S35: Analyze and calculate the load sharing of multiple support points based on the deviation status of the support structure layout requirements to obtain the support point load distribution data; Calculate the load transfer coordination between support points according to the support point load distribution data, and predict the deterioration trend of the synchronous bearing of multiple support points; Step S36: Evaluate the efficiency difference between the support structure material consumption and the bearing according to the synchronous deterioration trend.

8. The lightweight design method of the 3D printing product according to claim 7, wherein, Step S4 includes the following steps: Step S41: Evaluate the adaptability of the current printing process parameters according to the efficiency difference between the support structure material consumption and the bearing to obtain the process parameter adaptability data; Step S42: Identify the matching risk levels of temperature, speed, and layer thickness based on the process parameter adaptability data to obtain the printing process parameter matching risk situation; Step S43: Analyze the deviation between the expected value and the actual value of the bonding strength between each slice layer according to the printing process parameter matching risk situation to obtain the interlayer bonding strength deviation data; Step S44: Trace the process parameter combination that causes abnormal bonding strength based on the interlayer bonding strength deviation data to obtain the abnormal interlayer bonding strength tracing data of the slice; Step S45: Adjust the filling density, filling pattern, and printing path parameters according to the abnormal interlayer bonding strength tracing data of the slice to obtain the filling parameter adjustment data; Step S46: Verify the degree to which the adjusted parameters meet the lightweight target and strength requirements based on the filling parameter adjustment data to obtain the parameter verification data; Step S47: Select the printing filling parameter configuration that simultaneously meets the material weight reduction rate greater than 25%, the structural bearing strength retention rate not less than 90%, and the interlayer bonding strength deviation less than 5% according to the parameter verification data to obtain the optimized printing filling parameter data.

9. A lightweight design system for 3D printing products, characterized in that, A 3D printing product lightweight design system for executing the 3D printing product lightweight design method as described in claim 1, the 3D printing product lightweight design system includes: A geometric feature analysis module, configured to obtain the geometric data information of the 3D printing model; Extract the internal space volume distribution characteristics of the model and evaluate the wall thickness uniformity distribution of the model for the geometric data information, and predict the change trend of the model structure's load-bearing capacity to obtain the structural load prediction data; A material optimization analysis module, configured to detect the optimization space attenuation trend of the material distribution density according to the structural load prediction data; Predict the dynamic interference intensity of removing redundant materials inside the model according to the optimization space attenuation trend to obtain the redundant removal intensity data; Determine the coupling balance trend between model lightweight and strength retention based on the redundant removal intensity data and the optimization space attenuation trend; The support structure optimization module is used to utilize the coupling balance trend to predict the deviation condition of the support structure layout requirements; predict the synchronous deterioration trend of the coordinated load-bearing of multiple support points based on the deviation condition of the support structure layout requirements; evaluate the efficiency difference degree between the material consumption and load-bearing of the support structure according to the synchronous deterioration trend; The process parameter optimization module is used to evaluate the matching risk situation of the printing process parameters based on the efficiency difference degree; trace back the abnormal bonding strength between slices based on the matching risk situation of the printing process parameters, and optimize the printing filling parameters to obtain optimized data of the printing filling parameters; The path planning generation module is used to construct a three-dimensional space material distribution model according to the optimized data of the printing filling parameters, and generate a lightweight printing path planning scheme; output a lightweight digital control model of the 3D printed product according to the printing path planning scheme.

10. A computer medium stores a computer program, characterized in that, When the computer program is executed, it realizes the 3D printed product lightweight design method according to any one of claims 1 to 8.

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