Lightweight design method, system and storage medium for 3D printing products

Abstract

The present invention relates to the field of 3D printing technology, and in particular to a method, system, and storage medium for lightweight design of 3D printed products. The method comprises the following steps: obtaining geometric data information of a 3D printed model; extracting the internal spatial volume distribution characteristics of the model and evaluating the uniformity of the model wall thickness distribution based on the geometric data information, and predicting the trend of changes in the load bearing capacity of the model structure to obtain structural load prediction data; detecting the optimized spatial attenuation trend of the material distribution density based on the structural load prediction data; predicting the dynamic interference intensity of the removal of redundant materials within the model based on the optimized spatial attenuation trend to obtain redundant removal strength data; and determining the coupling balance trend of model lightweighting and strength maintenance based on the redundant removal strength data and the optimized spatial attenuation trend. The present invention achieves precise matching of load requirements and material distribution by establishing a correlation model between wall thickness uniformity and 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 Art

[0002] 3D printing is an additive manufacturing technology that creates physical parts by adding material layer by layer based on a three-dimensional digital model. The principle is to digitally slice a designed 3D model in a computer-aided design (CAD) environment. These slices are then further divided into voxels or track elements, depending on the printer type, and material is deposited layer by layer to form a three-dimensional object. Common 3D printing technologies currently 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). However, traditional 3D printing pre-processing software lacks the ability to deeply analyze the internal spatial volume distribution characteristics of complex geometric models. Previous methods primarily rely on simple, uniform parameter settings and fail to identify the differences in structural importance of different regions of the model, resulting in a lack of targeted optimization. Furthermore, they lack accurate prediction of the changing trends in the structure's load-bearing capacity. This results in structural weaknesses in some areas due to insufficient material, while other areas may be over-designed and over-designed, resulting in material redundancy. 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 printing products to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a lightweight design method for 3D printed products includes the following steps:

[0005] Step S1: Acquire geometric data information of the 3D printing model; extract the model's internal space volume distribution characteristics and evaluate the model's wall thickness uniformity based on the geometric data information, and predict the change trend of the model's structural load bearing capacity to obtain structural load prediction data;

[0006] Step S2: Detecting the optimized spatial attenuation trend of material distribution density based on the structural load prediction data; predicting the dynamic interference strength of redundant material removal within the model based on the optimized spatial attenuation trend to obtain redundant removal strength data; and determining the coupled balance trend between model lightweighting and strength preservation based on the redundant removal strength data and the optimized spatial attenuation trend;

[0007] Step S3: using the coupling balance trend to predict the deviation of the support structure layout requirements; based on the deviation of the support structure layout requirements, predicting the synchronization deterioration trend of the coordinated load bearing of multiple support points; and evaluating the degree of difference in the support structure material consumption and load bearing efficiency based on the synchronization deterioration trend;

[0008] Step S4: Evaluate the printing process parameter matching risk based on the degree of efficiency difference; trace the abnormality of the interlayer bonding strength of the slices based on the printing process parameter matching risk, and optimize the printing and filling parameters to obtain the printing and filling parameter optimization data;

[0009] Step S5: constructing a three-dimensional material distribution model based on the printing and filling parameter optimization data, and generating a lightweight printing path planning scheme; outputting a lightweight digital control model of the 3D printed product based on the printing path planning scheme.

[0010] The present invention further provides a 3D printed product lightweight design system for executing the above-mentioned 3D printed product lightweight design method, wherein the 3D printed product lightweight design system comprises:

[0011] The geometric feature analysis module is used to obtain the geometric data information of the 3D printing model; extract the internal volume distribution characteristics of the model and evaluate the uniformity of the model wall thickness distribution based on the geometric data information, and predict the change trend of the model structure load bearing capacity to obtain structural load prediction data;

[0012] The material optimization analysis module is used to detect the optimized spatial attenuation trend of material distribution density based on structural load prediction data; predict the dynamic interference strength of redundant material removal within the model based on the optimized spatial attenuation trend to obtain redundant removal strength data; and determine the coupled balance trend between model lightweighting and strength preservation based on the redundant removal strength data and the optimized spatial attenuation trend;

[0013] The support structure optimization module is used to predict the deviation of support structure layout requirements using the coupling balance trend; predict the deterioration trend of the synchronization of the coordinated load bearing of multiple support points based on the deviation of the support structure layout requirements; and evaluate the degree of difference in the efficiency of support structure material consumption and load bearing based on the deterioration trend of synchronization;

[0014] The process parameter optimization module is used to evaluate the matching risk of printing process parameters based on the degree of efficiency difference; based on the matching risk of printing process parameters, it can trace the abnormal bonding strength between slice layers and optimize the printing and filling parameters to obtain the printing and filling parameter optimization data;

[0015] The path planning generation module is used to construct a three-dimensional material distribution model based on the printing and filling parameter optimization data, and generate a lightweight printing path planning scheme; according to the printing path planning scheme, a lightweight digital control model of the 3D printed product is output.

[0016] The present invention also provides a computer storage medium storing a computer program, wherein the computer program, when executed, implements the above-mentioned lightweight design method for 3D printed products.

[0017] The present invention can effectively solve the technical bottlenecks of existing 3D printing products in the process of achieving weight reduction goals, such as low material utilization, difficulty in balancing structural strength, and unreasonable printing paths. In traditional 3D printing lightweight design, a unified filling density and fixed path strategy are often adopted, ignoring the differences in stress distribution borne by the product structure in different areas, resulting in redundant materials in some areas and fragile structures in other parts, and unstable overall structural performance. At the same time, the printing process is prone to material accumulation, energy waste, and reduced processing efficiency due to improper path design. This method introduces multi-level structural analysis, material efficiency evaluation, and path optimization mechanisms. Starting from the source of design, it integrates multi-dimensional parameters such as strength requirements, support performance, printing process, and equipment control to construct a set of intelligent lightweight design processes that take into account performance, materials, and processes. After constructing the initial structure of the model, this method first conducts a detailed evaluation of the bearing capacity of the support structure and its material usage, and combines the synchronization deterioration trend index to quantify the bearing efficiency corresponding to the unit material of different support structures. During this process, systematic calculations of support structure material consumption and load-bearing capacity enable performance-oriented data collection for material configuration. This not only improves analysis accuracy but also provides solid data support for subsequent evaluation of printing process suitability. Comparative analysis and coefficient of variation calculations of the unit material load-bearing efficiency of different support regions further reveal the balance of material distribution within the structure, helping to identify design issues such as irrational material distribution and unbalanced structural configurations, laying the foundation for optimizing the structure's inherent lightweight and load-bearing balance. Next, during the process parameter suitability assessment phase, this method analyzes the relationship between support structure efficiency differences and current printing process parameters (such as temperature, speed, and layer thickness) to quickly identify potential mismatching risks in the print setup. Combined with actual interlayer bond strength deviation assessment and process combination traceability technology, this method can identify bonding anomalies between different slice layers caused by improper parameter combinations, thereby avoiding structural fragility caused by substandard localized build quality. Furthermore, after identifying anomaly areas, the system automatically adjusts parameters such as infill density, infill pattern, and print path, resulting in a localized optimization strategy with enhanced mechanical stability and material efficiency. Through a series of parameter verifications and indicator comparisons (including weight reduction rate, load-bearing strength retention rate, and interlayer bond strength deviation), we achieve comprehensive coordination between lightweighting goals and strength standards, ensuring that material usage is reduced without compromising structural reliability. In the subsequent model optimization and printing control stages, this method establishes a three-dimensional material density distribution map, accurately mapping the optimized parameters to every spatial unit of the model. This breaks away from the traditional "uniform filling" approach and implements a regional filling strategy that is configured on demand and enables differentiated empowerment.Different infill patterns and density parameters are applied to each region based on its functional attributes and stress conditions, thereby improving the structural utilization efficiency of the overall printed model without increasing printing complexity. After slicing, a path coordinate sequence is automatically generated. During the path optimization process, strategies such as sequence adjustment and connection reconstruction are adopted to effectively reduce empty strokes and repeated paths, reduce printing energy consumption and material redundancy, and improve processing efficiency. After optimization, the path planning data is automatically converted into a control instruction format recognizable by the printer, and all control dimension parameters are integrated to construct a complete digital control model. In practical applications, this model can directly drive various models of 3D printing equipment without manual intervention during the equipment execution process, ensuring that the printing task is accurately executed according to the optimization results, and improving the consistency and repeatability of product molding. In addition, the lightweight digital control model generated by this method has strong portability and scalability, and is suitable for product manufacturing tasks in various industrial scenarios with high requirements for structural weight reduction and strength, such as drone shells, customized brackets, and aerospace assemblies. In general, 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 design logic and manufacturing capabilities is achieved. The final product is not only significantly reduced in weight, but also systematically improved in load-bearing capacity and processing efficiency. This integrated design and manufacturing method with data-driven, feedback closed-loop, and spatial precision control as the core has good engineering applicability and promotion prospects. It can be used as one of the key technical means to enhance the functionality of 3D printed products and save materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0019] Figure 1 This is a schematic diagram of the steps of the lightweight design method for 3D printed products of the present invention;

[0020] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0021] Figure 3 for Figure 1 Detailed step flow chart of step S3 in FIG. DETAILED DESCRIPTION

[0022] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0024] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0025] To achieve this, please refer to Figures 1 to 3 The present invention provides a lightweight design method for 3D printing products, the method comprising the following steps:

[0026] Step S1: Acquire geometric data information of the 3D printing model; extract the model's internal space volume distribution characteristics and evaluate the model's wall thickness uniformity based on the geometric data information, and predict the change trend of the model's structural load bearing capacity to obtain structural load prediction data;

[0027] The embodiment of the present invention imports the 3D model to be printed into the modeling software (such as SolidWorks or Fusion 360), extracts the triangular mesh data in the STL file format, and performs geometric preprocessing on the model to clean up non-manifold boundaries, repeated faces and isolated points, thereby ensuring the integrity of the model's topological structure. 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 layer-by-layer scanning method is used to obtain the solid / void state information of each voxel block, thereby statistically analyzing the spatial volume distribution characteristics inside the model. Voxel density cluster analysis is then used to divide the solid density of each area in the model, generate a distribution map of local wall thickness, and calculate the wall thickness variance as a uniformity evaluation index. Finally, finite element simulation software (such as ANSYS) is used to apply a standard static load (such as a 100N vertical load at the bottom of the model) and simulate the overall stress-strain distribution of the model. 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 structural load prediction data for subsequent material optimization is obtained.

[0028] Step S2: Detecting the optimized spatial attenuation trend of material distribution density based on the structural load prediction data; predicting the dynamic interference strength of redundant material removal within the model based on the optimized spatial attenuation trend to obtain redundant removal strength data; and determining the coupled balance trend between model lightweighting and strength preservation based on the redundant removal strength data and the optimized spatial attenuation trend;

[0029] According to the structural load prediction data obtained in step S1, the embodiment of the present invention uses a spatial density mapping algorithm to score the material load utilization rate of different areas of the model, wherein high-scoring areas represent relatively sufficient material load utilization, and low-scoring areas represent material redundancy. By constructing a spatial gradient attenuation map of material density-load efficiency, spatial areas with strong optimization potential in the model are extracted to form optimized spatial attenuation trend data. On this basis, the perturbation analysis method is used to predict the changes in the mechanical properties of the model after the redundant materials are removed. The method is as follows: the low-load area is gradually cavitated and the structural response of the model is simulated to obtain the rate of change of the structural response caused by the removal of unit material, thereby establishing a redundant removal dynamic interference intensity prediction model, and calculating the redundant removal strength data. Further combined with the above-mentioned optimization trend and interference intensity data, a multi-objective optimization method is used to construct a coupling function between model lightweighting and strength retention, and the Pareto frontier is used to screen the equilibrium point to determine the optimal coupling balance trend between lightweighting and reliability of the model structure.

[0030] Step S3: using the coupling balance trend to predict the deviation of the support structure layout requirements; based on the deviation of the support structure layout requirements, predicting the synchronization deterioration trend of the coordinated load bearing of multiple support points; and evaluating the degree of difference in the support structure material consumption and load bearing efficiency based on the synchronization deterioration trend;

[0031] The embodiment of the present invention is based on the coupling balance trend data constructed in step S2, and calls the structural stability evaluation function to perform a mechanical support layout analysis on the model. First, the main load-bearing paths in the model are identified through force conduction path reconstruction analysis, and the support structure layout position and strength requirements required by the model in the actual printing process are deduced based on these paths. The spatial gradient of the force in the support area is reversely inferred in combination with the coupling balance trend, and the degree of deviation between the current support layout and the theoretical support requirement is calculated to form a support structure layout requirement deviation status. Subsequently, under the parallel force state of simulating multiple support points, a synchronization index of multi-point coordinated load bearing is constructed to evaluate the difference in force transmission timing due to structural lightweighting, and quantify its synchronization deterioration trend. The material consumption of each support point is further compared with its actual load-bearing capacity, and the efficiency difference between different support structures in material usage and force effect is evaluated. The degree of difference between support structure material consumption and load-bearing efficiency is obtained, providing a basis for subsequent printing strategy adjustments.

[0032] Step S4: Evaluate the printing process parameter matching risk based on the degree of efficiency difference; trace the abnormality of the interlayer bonding strength of the slices based on the printing process parameter matching risk, and optimize the printing and filling parameters to obtain the printing and filling parameter optimization data;

[0033] Based on the efficiency differences obtained in step S3, this embodiment of the present invention performs a risk assessment on the compatibility of process parameters across the entire printed model. The specific method involves using a thermodynamically simulated printing process simulation system (such as Digimat-AM) to set initial printing parameters (e.g., nozzle temperature 220°C, print speed 60 mm / s, layer thickness 0.15 mm). The interlayer bonding strength of different support areas and their adjacent structures is simulated, and local strength anomalies caused by efficiency differences are identified. Subsequently, a retrospective analysis is performed on these anomalies to trace their causes and correlate them with process parameters, thereby identifying key parameter combinations that may have caused the anomalies. A sensitivity analysis method is then used to optimize infill parameters such as fill ratio (varying from 30% to 70%) and infill pattern (honeycomb, linear, grid). While maintaining a balance between lightweight structure and strength, the optimal parameter combination is selected to generate optimized printing infill parameter data, which serves as the basis for subsequent printing path planning.

[0034] Step S5: constructing a three-dimensional material distribution model based on the printing and filling parameter optimization data, and generating a lightweight printing path planning scheme; outputting a lightweight digital control model of the 3D printed product based on the printing path planning scheme.

[0035] Based on the optimized printing and filling parameter data obtained in step S4, this embodiment of the present invention first applies the optimized parameters to the original model using 3D reconstruction software (such as Materialise 3-matic). This reconstructs the material distribution within the model, including changes in filling density, reconstruction of localized reinforcement areas, and the placement of hollow structures, thereby forming a 3D material distribution model. This distribution model is then introduced into path planning software (such as Ultimaker Cura or Slic3r). This distribution model is then combined with the optimized parameters to set path generation rules, comprehensively considering nozzle movement efficiency, path heat accumulation balance, and cross-layer connection continuity. Through repeated simulation, an optimal lightweight printing path planning solution is generated. Finally, the path data is exported as G-code, which serves as control instructions for the 3D printer. A corresponding lightweight digital control model is then constructed within the control system, achieving efficient, material-saving, and robust printing of the final product. This model is suitable for applications requiring both lightweightness and strength, such as aerospace structural components, medical implants, and robotic housings.

[0036] Preferably, step S1 includes the following steps:

[0037] Step S11: obtaining geometric data information by scanning the surface of the 3D printed model; performing three-dimensional coordinate point cloud extraction processing on the geometric data information to obtain model surface contour data;

[0038] In this embodiment, a high-precision 3D scanner (such as the FARO Edge ScanArm HD or Creaform HandySCAN 3D) is used to perform multi-angle non-contact laser scanning of the surface of a 3D-printed model to obtain complete surface geometry data. The scanning accuracy is set to 0.05mm, and the scanning angle covers the entire visible outer surface of the model. An automatic stitching algorithm is used to fuse multiple sets of scan data into a single, integrated 3D model. The scan results are then imported into point cloud processing software (such as Geomagic Design X), where noise is removed, density is reconstructed, and normal vectors are corrected to extract the 3D coordinate point cloud data of the model surface. This point cloud data consists of a large number of irregularly distributed coordinate points, each containing XYZ coordinate information representing the tiny curved surface features of the model's outer surface. This ultimately forms high-density, well-regularized model surface contour data, laying the foundation for subsequent surface curvature analysis and structural lightweighting.

[0039] Step S12: calculating and processing the curvature distribution of the outer surface of the model according to the surface contour data of the model to obtain surface curvature distribution data;

[0040] In this embodiment of the present invention, the model surface contour data obtained in step S11 is imported into a CAD environment (such as MeshLab or the Rhino + Grasshopper plug-in). A triangulated mesh reconstruction algorithm is used to convert the point cloud data into a continuous surface structure, constructing a surface mesh (mesh) model. Based on this mesh model, a discrete curvature analysis algorithm (such as a combined Gaussian and mean curvature analysis method) is applied to calculate the local curvature of each mesh vertex. Specifically, a quadratic surface is fitted within the neighborhood of each vertex, and the rate of change in the principal curvature direction is calculated. The curvature results reflect the degree of concavity and convexity and the trend of change in the local area of the model surface. High-curvature areas represent complex surface structures (such as corners, grooves, or protrusions), while low-curvature areas represent relatively flat areas. The system normalizes all curvature results and outputs surface curvature distribution data in the form of a three-dimensional heat map, which is used for subsequent volume zoning and structural risk identification. For example, on the surface of a medical implant, this method can effectively identify the curvature characteristics of complex transition zones and smooth areas at the bone interface.

[0041] Step S13: performing internal space volume division processing on the surface curvature distribution data to obtain space volume distribution data; performing statistical analysis processing on the internal space density of the model based on the space volume distribution data to obtain internal space density distribution data; performing feature extraction on the internal space density distribution data to obtain feature data of the internal space volume distribution of the model;

[0042] The embodiment of the present invention uses the surface curvature distribution data obtained in step S12 to perform internal partitioning on the overall three-dimensional space of the model. Specifically, a curvature-dominated spatial voxel segmentation algorithm is used to divide the interior of the model into several spatial units controlled by the outer surface curvature. Each unit is constructed as a 0.5mm cubic voxel structure, and the spatial position, corresponding curvature information and envelope structure of the voxel are used for labeling to form spatial volume distribution data. Afterwards, these spatial voxels are statistically analyzed to calculate the degree of entity filling in each spatial unit, that is, to evaluate the material distribution density in the unit space, thereby obtaining the internal spatial density distribution data of the model. Furthermore, the density characteristic parameters of different regions are extracted using the method of the weighted mean of the volume density and the difference between the local density gradient, including the maximum density mutation value, the regional average density, the density center offset value, etc., to form the internal spatial volume distribution characteristic data of the model. Taking complex mechanical connection components as an example, the characteristic data can accurately point out whether there are potential risk points of stress concentration in the material transition zone.

[0043] Step S14: obtaining the wall thickness measurement data of each cross section of the model, calculating the thickness variation gradient of the wall thickness measurement data, and evaluating the wall thickness uniformity index to obtain wall thickness uniformity evaluation data;

[0044] Based on the model voxel unit structure constructed in step S13, this embodiment of the present invention uses a virtual slicing algorithm to construct dozens of two-dimensional cross-sectional images of the model from different angles (e.g., slices every 5 mm in the XY plane). Wall thickness values at each cross-section are measured using CAD software (such as SolidWorks Simulation or AutoCAD). A multi-point symmetric measurement method is used to obtain the minimum and maximum thicknesses, as well as the thickness distribution range, for each cross-section, forming a wall thickness measurement dataset. Next, a thickness gradient algorithm is used to calculate the spatial rate of change of wall thickness—that is, the rate of change of thickness between adjacent measurement points in three-dimensional space—to generate a wall thickness gradient map. This wall thickness gradient is then statistically evaluated, calculating the standard deviation, maximum difference, and mean deviation of thickness variations in each region. A wall thickness uniformity indicator (such as the thickness variation coefficient (CV), where a smaller coefficient indicates better uniformity) is introduced. Finally, wall thickness uniformity assessment data is output for analyzing the distribution of structural weaknesses. For example, in lightweight aviation structures, this data can be used to assess whether the shell structure contains localized areas prone to instability.

[0045] Step S15: using the wall thickness uniformity assessment data to predict the load bearing capacity change trend of the model structure to obtain structural load prediction data.

[0046] In this embodiment of the present invention, the wall thickness uniformity assessment data obtained in step S14 is imported as input parameters into a finite element structural simulation module (such as Abaqus or ANSYS). Boundary and loading conditions are applied to the structural model to simulate its load response under different operating conditions. First, constraint boundaries are set (e.g., fixed at the bottom), a load is applied (e.g., 150N applied vertically at the center of the top), and the stress distribution, deformation, and safety margin within the structure are calculated. The wall thickness uniformity data is used as a local model parameter to simulate stress concentrations caused by wall thickness variations, and a trend curve is calculated showing the load capacity per unit area as a function of wall thickness. A trend fitting method (e.g., quadratic polynomial fitting) is used to construct a mapping between the "wall thickness uniformity index" and the load capacity change rate. The model predicts the response trend of structural strength at different locations as the model changes with thickness, thereby generating structural load prediction data, providing basic data support for subsequent lightweight optimization and redundant material elimination. This method is particularly important in applications with high structural stiffness requirements, such as medical device housings and lightweight vehicle frames.

[0047] Preferably, step S15 includes the following steps:

[0048] Step S151: Obtaining model material attribute parameter data;

[0049] In conjunction with the wall thickness uniformity assessment data generated in step S15, this embodiment of the present invention further collects material property parameter data for the 3D printing material used. Specifically, these parameters include the material's elastic modulus, Poisson's ratio, yield strength, elongation at break, density, and thermal expansion coefficient. For a typical 3D printing nylon material (e.g., PA12), these parameters can be obtained through standard material databases (e.g., ANSYS Granta or MatWeb) or experimental measurements: its elastic modulus is approximately 1700 MPa, Poisson's ratio is 0.39, yield strength is 45 MPa, elongation at break is approximately 20%, and the material density is approximately 1.01 g / cm³. These parameters are organized into a unified material property parameter dataset and input into a finite element simulation environment. This dataset is used to define the material's behavior boundaries and response characteristics in mechanical calculations, serving as a prerequisite for subsequent structural stress calculations and critical value analysis. These material parameters can be calibrated through tensile testing, compression testing, or thermal analysis, and adjusted based on the actual printing direction (e.g., mechanical weakening along the Z-axis).

[0050] Step S152: Calculating the structural stress distribution based on the material property parameter data and the wall thickness uniformity evaluation data, and performing load bearing critical value analysis to obtain load bearing critical data;

[0051] In this embodiment of the present invention, the material property parameter data from step S151 and the wall thickness uniformity assessment data generated in step S14 are coupled and input into 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-printed structure. Specifically, the model is first subjected to actual load boundary conditions, for example, a uniform 500N compressive force is applied to the top of the bionic scaffold model, with fixed boundary constraints at the bottom. The model is meshed using Solid187 three-dimensional solid elements, with a mesh size controlled within 0.5mm. The maximum principal stress, shear stress, and von Mises stress distribution of each element are then obtained using a statics solver. After the calculations are completed, the critical load response of each region is further analyzed, with the "critical value" defined as the load state where the stress in the local region reaches above 95% of the material's yield strength. The system automatically extracts the overall stress distribution map and load-bearing boundary regions for this state, outputting load-bearing critical data. This data can be used to identify regions approaching the mechanical failure threshold under the current design, facilitating the avoidance of sensitive areas of critical stress concentration during subsequent lightweighting.

[0052] Step S153: Evaluate the load bearing capacity of the structure based on the load bearing critical data and the wall thickness uniformity evaluation data to obtain current load bearing capacity data;

[0053] This embodiment of the present invention comprehensively assesses the overall load-bearing capacity of the current structure based on the critical load-bearing data output in step S152 and the wall thickness uniformity assessment data from step S14. Specifically, the method involves comparing the ratio of the current actual load level (e.g., the maximum load under operational conditions) to the critical load-bearing capacity value to calculate a safety factor distribution diagram for the structure. If the safety factor of a local area is less than the design target value (e.g., a target value of 2.0), the area is marked as a "low reliability zone," requiring adjustment to the design thickness or rib structure. Furthermore, considering the impact of wall thickness uniformity on the load transfer path, the system modifies the load distribution coefficient of areas with poor uniformity to reflect the impact of wall thickness variations on stress concentration. The final output of the current load-bearing capacity data includes key parameters such as the overall safety factor mean, the lowest local safety factor, and the structural load-bearing trends in different directions (e.g., the Z-axis or radial direction). This data supports subsequent modeling, prediction, and lightweighting iterations. For example, using lightweight support components for aviation, this data can be used to verify the reliability assessment of the structure under flight load conditions, ensuring that strength redundancy is not compromised while reducing weight.

[0054] Step S154: performing time series variation trend fitting on the current load bearing capacity data, and establishing and processing a structural load prediction model based on the current load bearing capacity data to obtain structural load prediction data.

[0055] This embodiment of the present invention uses the current load-bearing capacity data obtained in step S153 as a time series input. By simulating stress response trends under different operating conditions (such as temperature changes and increasing loads), it constructs a model that models the evolution of the structural load-bearing capacity over time or load changes. A polynomial fitting algorithm based on a sliding time window is used to extract the rate of change of the load-bearing capacity at each time point or load level, and a time-series curve model of the load-bearing capacity is constructed. Subsequently, a multivariate regression modeling method is employed, using wall thickness uniformity indicators, density distribution characteristics, and initial stress distribution as independent variables, and the current load-bearing capacity change as the dependent variable. This model is trained to form a structural load prediction model capable of predicting the structure's response to new operating conditions based on design changes. The model can be improved in accuracy using nonlinear regression techniques such as random forests or LSTM neural networks, ultimately outputting structural load prediction data, including prediction curves, ultimate response trends, and material yield point approach rates. This data is crucial for intelligent lightweight design, guiding designers to pre-empt potential failure points and achieve on-demand reinforcement and intelligent weight reduction for 3D-printed structures. For example, when manufacturing high-performance racing car parts, predictive models can be used to optimize wall thickness and layout, thereby improving acceleration stability and impact resistance.

[0056] Preferably, detecting the optimized spatial attenuation trend of material distribution density according to the structural load prediction data in step S2 includes:

[0057] Based on the structural load prediction data, the 3D printing model is divided into 1mm-20mm grid spacing to obtain the spatial load distribution data;

[0058] According to the spatial load distribution data, the grid cells with load bearing strength lower than 60% of the design load threshold are screened to obtain the low load area distribution data;

[0059] Based on the low-load area distribution data, the material density redundancy of each area is calculated to obtain the material density optimization potential data;

[0060] According to the material density optimization potential data, the density distribution law from the center to the periphery of the model is analyzed to obtain the material density spatial attenuation data;

[0061] Based on the spatial attenuation data of material density, the areas with attenuation coefficients greater than 0.3 are marked and their volume proportions are counted to obtain optimized spatial distribution data;

[0062] The spatial attenuation trend characteristics when the volume ratio exceeds 25% are evaluated based on the optimized spatial distribution data, and the optimized spatial attenuation trend of the material distribution density is obtained.

[0063] After obtaining the structural load prediction data, the present embodiment enters the spatial meshing stage based on the structural response distribution. Specifically, the entire 3D printed model is subjected to a three-dimensional spatial meshing operation, dividing the model into multiple cubic voxel units according to a set grid spacing parameter. The grid spacing ranges from 1mm to 20mm, and is flexibly selected based on the actual application scenario and modeling accuracy requirements. For high-precision medical implants, for example, a 1mm spacing ensures sufficient reflection of local stress details; for large non-structural shell components, a coarser resolution of 15mm or higher can be used to conserve computing resources. This meshing operation can be performed using the "volume partitioning" module in commercial CAE software (such as HyperMesh or COMSOL). The structural load prediction data is interpolated to calculate the average load value within each grid cell, generating the corresponding spatial load distribution data. This data records the magnitude and direction of the load applied by each grid cell and is an important basis for identifying the rationality of material distribution and the zoning of strong and weak loading responses. Based on this spatial load distribution data, the load bearing strength of each grid cell is further analyzed and compared with the set design load threshold. The design load threshold is typically determined based on the ultimate load or standard safety margin under the target product's operating conditions. For example, if the design ultimate load of an aviation bracket is 600N, the 60% threshold corresponds to 360N. All grid cells are screened, retaining those with an average load-bearing strength below 60% of the threshold. This generates low-load area distribution data. These low-load elements are often located at the edges of the load path or in geometric transition zones, where the material structure contributes less to the overall load-bearing capacity and offers significant weight reduction potential. This data, typically in the form of a 3D point cloud or voxel grid, records the spatial location, volume, and corresponding stress level of each low-load element, serving as the basic input for subsequent material redundancy calculations. Once the low-load area distribution data is obtained, a material redundancy analysis is performed on each low-load area to assess whether the current structure suffers from material waste due to "overdesign." The "material density redundancy" here is defined as the ratio of the current material density per unit space to the minimum density required to achieve the desired structure. In implementation, the minimum material density required to maintain 60% of the design strength within the low-load area is assumed. The required lower density limit is then inferred based on the mechanical properties of the printed material (such as the elastic modulus). The actual density of the current structure in that area is then compared, for example, using the 3D model volume and material infill percentage to calculate the actual density. For example, for a region with a current density of 1.2g / cm³ and a theoretically required density of 0.6g / cm³, the redundancy is 50%. By batch processing all low-load units, "material density optimization potential data" is generated. This data is used to quantitatively identify areas with excess density and serves as the primary metric for determining candidate lightweighting areas.Based on this material density optimization potential data, to further identify structural weight reduction patterns and optimization paths, the material density distribution trend of the entire model from the center to the periphery was modeled and analyzed. Specifically, the model's geometric center was used as the reference point, and multiple shell intervals were divided radially outward at equal intervals. Within each interval, the average material density and redundancy potential of the mesh elements contained within were calculated, and the variation of these metrics with shell radius was analyzed. If the material density or redundancy potential decreases significantly with increasing distance from the center (i.e., the density redundancy is higher at the edge), this indicates the presence of "material spatial attenuation." This phenomenon is common in shell-type components or structures with non-uniform loading and serves as a key basis for adjusting material distribution strategies. This ultimately generates "material density spatial attenuation data," a set of distribution relationships with distance as the independent variable and density or redundancy as the dependent variable, which is used to guide subsequent parametric design iterations. Based on this material density spatial attenuation data, the significance of the attenuation in each region is further evaluated to identify areas with potential for lightweighting. 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 inner neighboring regions. By calculating the attenuation coefficient for each shell region and screening for regions with an attenuation coefficient greater than 0.3, indicating significant material redundancy, the team then calculated the contribution of these regions to the overall model volume. A high contribution (e.g., exceeding 25%) indicates significant potential for density optimization within the model. The final output is "optimized spatial distribution data," typically a collection of labeled voxels within the 3D model structure, along with volume ratio information, enabling engineers to directly perform "material removal" operations within CAD modeling. Finally, based on this optimized spatial distribution data, the material distribution trends and magnitude of variation at different spatial locations across the entire model are evaluated, resulting in an "optimized spatial attenuation trend of material distribution density." This approach involves further analyzing high attenuation regions exceeding 25% of the volume, examining the gradient of their density attenuation along different spatial directions. For example, the gradient of density attenuation is analyzed to determine whether the rate of change from the center of the model to the edges is continuous and whether there are any abrupt changes. By combining this with a topology optimization algorithm (such as the SIMP method or a density penalty function), an optimization design function is constructed, using this trend as a constraint to reassign local density design values to the model. In typical applications, such as new energy vehicle battery tray structures, if the optimized spatial attenuation trend indicates high edge redundancy and continuous gradients, layer-by-layer thinning, hollowing, or multi-material infill strategies can be implemented in this area, ultimately achieving a material weight reduction of over 20% without compromising structural safety. This trend analysis not only directly guides iterative structural optimization design but also reverse-validates the rationality of load prediction models, providing data-driven closed-loop support for intelligent and precise 3D printing lightweight design.

[0064] Preferably, in step S2, performing dynamic interference intensity prediction for removing redundant materials within the model according to the optimized spatial attenuation trend includes:

[0065] According to the optimized spatial attenuation trend, the removable material area is divided into 0.5mm-5mm precision divisions to obtain redundant material removal unit data;

[0066] Based on the redundant material removal unit data, the stress transfer influence range of each unit after removal is calculated on the adjacent area, and the stress influence diffusion data is obtained;

[0067] Analyze the load redistribution path during the material removal process based on the stress influence diffusion data to obtain the load transfer path data;

[0068] Predicting the structural deformation propagation strength caused by the removal operation based on the load transfer path data to obtain deformation propagation strength data;

[0069] The degree of interference of the material removal process on the overall stability of the model is evaluated based on the deformation propagation intensity data, and the structural stability interference data is obtained;

[0070] The interference intensity coefficient is quantified based on the structural stability interference data and the dynamic impact range of the removal operation is predicted to obtain the redundancy removal intensity data.

[0071] Based on the optimized spatial attenuation trend data of material distribution density obtained in the previous stage, this embodiment of the present invention first performs a fine-scale meshing operation on high-density redundant regions marked as eligible for optimization to generate removable material elements. Specifically, a localized meshing strategy with the optimization region as the boundary is employed. Finite element analysis software (such as Abaqus or Ansys) or a custom geometry processing program is used to partition this region into voxels with a resolution of 0.5mm to 5mm. The meshing accuracy is set based on the geometric complexity and load sensitivity of the target part, for example, 0.5mm for micro-precision devices and 3-5mm for large structural support components. Each element is recorded with its 3D position, volume, and information about the structural neighborhood that may be affected after removal. This data is then used to generate "redundant material removal element data," which serves as a basis for subsequent stress transfer analysis and is essential for achieving precise removal without compromising structural continuity. After obtaining the redundant material removal element data, a mechanical response simulation is performed on each element to analyze the extent to which its removal affects the stress state of the surrounding structure. The specific method uses a statics simulation module to perform comparative calculations on the structure before and after element removal. An envelope region (e.g., a 10mm spherical domain) is defined around each removed element to assess the maximum principal stress change and impact range within that region before and after the removal operation. Stress diffusion data indicates the adjacent regions to which the stress change can "diffuse" after the removal of a material element. For example, in an aviation connection, if the stress change impact radius of a 3mm voxel after removal reaches 12mm and the stress change within the impact region exceeds 15% of the original stress value, the element is considered to have a medium-to-high diffusion risk. The resulting "stress diffusion data" includes information such as element identification, impact radius, and stress gradient, which is used in the next step of load path reconstruction. Based on this stress diffusion data, the structural stress field reconstruction behavior caused by redundant material removal—that is, the redistribution of the load path—is analyzed. The specific method utilizes force streamline visualization technology and equivalent stress transmission network modeling to track the path changes of the overall model load from the load input point to the fixed boundary after each element is removed. By comparing the changes in the main stress vector flow direction and stress concentration path between the original model and the one after the removal operation, it is possible to identify whether the original load path has been transferred, branched, or offset due to local material removal. Taking a complex support structure as an example, the original load is transmitted along the central axis, but after removing the highly redundant edge material, it is found that the load is partially transferred to the peripheral frame, indicating that there is a trend of path offset. Finally, the "load transfer path data" is obtained. This data is expressed in a path graph structure, marking the starting point, end point, change amplitude, and path weight change of each path. It is an important basic data for predicting the stability of structural response. Based on the load transfer path data, the deformation propagation effect of the structure after the material removal operation is predicted and analyzed, and the reconstruction of the deformation chain caused by the change in the stress path is evaluated.During implementation, a nodal deformation response propagation matrix modeling method was employed to construct the mechanical coupling relationship between nodes in a three-dimensional structure. Simulations were then performed to determine the transfer coefficient of displacement changes at each node following changes in the load transfer path. For example, if removing a material element from a certain region of the model causes a displacement increase of more than 20% at five nearby nodes, and this effect propagates to distant boundary nodes, the deformation propagation intensity in that region is considered high. This data, called "deformation propagation intensity data," records the maximum deformation increase, number of affected nodes, and propagation direction caused by each removal operation. This data serves as a key indicator for controlling the risk of cumulative deformation instability. Based on this deformation propagation intensity data, the impact of material removal on the overall structural stability of the 3D printed model was further analyzed. Stability disturbance is defined here as the degree to which stress and deformation propagation caused by localized material removal weaken the overall stiffness matrix and the constraint capacity of boundary conditions. This evaluation method involves constructing the overall finite element stiffness matrix before and after the removal operation and comparing the changes in the main mode frequency, the reduction in the critical buckling load, and the magnitude of the change in the displacement degrees of freedom of the displacement-constrained boundary nodes in the modal analysis. For example, if removing redundant material from the center of a car bumper reduces the lowest natural frequency by 10% and the stiffness of the nodes in the boundary region decreases by 25%, this region is considered to have structural stability interference. The output "structural stability interference data" is presented as interference scores for each structural component. Each score is based on a comprehensive assessment of the stiffness reduction ratio, modal variation intensity, and deformation degree of freedom distribution, providing a quantitative basis for risk management and control in material optimization. Based on this structural stability interference data, the concept of a "interference intensity coefficient" is proposed to further quantitatively control the potential structural risks associated with material removal. This coefficient is used to predict the potential dynamic impact range of redundant material removal operations. The interference intensity coefficient is defined as a weighted index of the combined effects of stiffness reduction and deformation propagation caused by material removal in a specific region. It is calculated by combining the aforementioned interference data and propagation intensity data. The acceptable dynamic impact range of the model is then defined based on different interference coefficient values. For example, when the interference intensity coefficient is less than 0.2, the structural impact of material removal is manageable, and local hollowing or gradient filling can be implemented. When the coefficient is between 0.2 and 0.5, embedded microstructures are recommended as an alternative to removal to maintain load transmission continuity. The final "redundant removal strength data" includes information such as the interference intensity value of the removed area, the volume of the affected area, and recommended optimization methods. It is the last layer of physical constraint verification before the structural intelligent lightweight system generates the final design model. It is a key control indicator for achieving both performance and material savings.

[0072] Preferably, in step S2, determining the coupled balance trend between model lightweighting and strength preservation based on redundantly removed strength data and optimized spatial attenuation trend includes:

[0073] The corresponding relationship between material removal amount and strength loss is established based on the redundant removal strength data and the optimized spatial attenuation trend, and the strength loss correlation data is obtained;

[0074] Based on the strength loss correlation data, the change rate of the structural bearing capacity under different material removal ratios is calculated to obtain the bearing capacity change data;

[0075] The trade-off between lightweighting and strength retention is evaluated based on the load-bearing capacity change data to obtain lightweight-strength trade-off data.

[0076] Determine the safe threshold range of material removal based on lightweight-strength trade-off data and determine safe removal threshold data;

[0077] Predicting the equilibrium state between the lightweight effect and the strength change within the threshold range based on the safety removal threshold data, and obtaining equilibrium state prediction data;

[0078] Based on the equilibrium state prediction data, the dynamic balance development direction of lightweighting and strength maintenance is analyzed, and the coupled balance trend of model lightweighting and strength maintenance is obtained.

[0079] Based on acquired redundant removal strength data and optimized spatial attenuation trends, this embodiment of the present invention constructs a mapping relationship between material removal and structural strength loss. Specifically, the following steps are performed: First, parameters are input based on material removal units in different regions. Previously acquired redundant removal strength data is then used to determine the structural strength reduction (expressed as a percentage of principal stress reduction) resulting from a unit volume removed (e.g., 1 cubic millimeter) for each unit. Combined with the optimized spatial attenuation trend data, the material density attenuation gradient is coupled with the mechanical sensitivity of the corresponding region to analyze the nonlinear behavior of the structure in response to material removal. For example, in an aerospace support structure, each cubic millimeter of material reduction in the redundant edge region results in only a 0.5% strength loss, while the same volume removed from the central framework region results in a 3%-5% strength reduction. Finally, a nonlinear correlation model between material removal and strength loss is established through interpolation or fitting, outputting "strength loss correlation data" that is used to simulate the structural performance response under different removal strategies. Once the strength loss correlation data is obtained, the impact of different material removal ratios (e.g., 5%, 10%, 15%, 20%, etc.) on the overall structural load-bearing capacity is further calculated. The specific method involves using a finite element simulation platform to perform static load simulations using models with varying material removal ratios as input, recording changes in the maximum load and stress distribution. The load-bearing capacity change rate is defined as the percentage change in the maximum load the structure can withstand after material removal compared to the original structure. For example, in a lightweight automotive chassis structure, removing 10% of redundant material results in an 8% decrease in maximum load-bearing capacity, while removing 15% results in a 13% decrease, demonstrating a nonlinear, accelerated attenuation trend. Ultimately, "load-bearing capacity change data" is generated, recording the rate of change in the structure's maximum load, the magnitude of the change in overall stiffness, and the increase in deformation response at varying material removal ratios. This data is used to support subsequent safety assessments of lightweighting designs. The load-bearing capacity change data is used to comprehensively evaluate the trade-off between lightweighting and structural strength preservation. Specifically, a two-dimensional parameter balance diagram is constructed, representing the "lightweighting rate" and "load-bearing capacity retention rate," with the horizontal axis representing the material reduction ratio and the vertical axis representing the load-bearing capacity retained. Based on this, the unit strength loss associated with unit weight reduction is calculated (e.g., the number of Newtons of load-bearing capacity lost per 100g of material removed), and an efficiency ratio parameter is introduced for evaluation. Taking an engineering robot arm as an example, if a 10% mass reduction maintains 92% of its load-bearing capacity, the performance ratio is 0.92 / 0.1 = 9.2, making it a highly cost-effective solution. This analysis outputs "lightweight-strength trade-off data," clarifying the cost of structural performance loss under different lightweighting strategies, providing a theoretical basis for design selection and structural adjustments. Based on this lightweight-strength trade-off data, the safe threshold for material removal is further determined, defining the maximum proportion of material that can be removed without significantly compromising structural strength.The specific method involves setting a minimum strength retention threshold (e.g., no less than 90% of the original load-bearing capacity), traversing data on lightweighting ratios and load-bearing capacity changes, and selecting the maximum removal ratio that meets this requirement. In a 3D-printed drone shell application, experimental results show that within a 13% removal ratio, the structure still retains 91.5% of its load-bearing capacity. This ratio is therefore designated the "safe removal threshold." Furthermore, the strength sensitivity of different regions must be considered, with a lower threshold (e.g., 5%) set for high-stress concentration areas and a higher threshold (e.g., 18%) for low-stress areas. Ultimately, "safe removal threshold data" is generated, recording the maximum acceptable material removal ratio for each region and serving as a key boundary condition for generating lightweight printed models. Using this safe removal threshold data, the balance between the lightweighting effect and structural strength change resulting from material removal within this threshold range is predicted. In implementation, a combination of simulation calculations and empirical function fitting methods is used to estimate the actual weight reduction, maximum structural deformation, and overall stiffness retention of the printed model. Taking the battery tray structure of an electric vehicle as an example, after applying a safety threshold removal strategy, the overall weight was reduced by approximately 14%, the maximum principal stress increased by no more than 9%, and the displacement increase was kept within 3mm, indicating that the structure had reached a "balance between mechanics and lightweighting." The prediction results are output as "equilibrium state prediction data," recording the lightweighting effect, the structural response increase, and its stability coefficient, which serve as the evaluation benchmark for printed model generation. Based on the equilibrium state prediction data, the coupled balance between lightweighting and strength retention is analyzed from the perspective of dynamic structural performance evolution. The specific method is to construct a joint response surface for lightweighting and strength retention based on multi-stage simulation data at different material removal ratios, and observe its evolution under different applied loads and boundary constraints. Principal component analysis and trendline fitting techniques are used to extract the coupling patterns between key variables. For example, it is found that when the lightweighting ratio exceeds 15%, the structural strength retention curve shows an inflection point, indicating that the system transitions from a stable state to a critical state. The design evolution direction is determined based on actual product requirements (such as whether it is used in high-speed motion or high-impact load scenarios). The output "coupling balance trend between 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, verifiable lightweight strategies for 3D printed products.

[0080] Preferably, step S3 includes the following steps:

[0081] Step S31: performing support requirement area identification processing on the 3D printing model according to the coupling balance trend to obtain support requirement distribution data;

[0082] After obtaining the coupled balance trend between model lightweighting and strength maintenance, the embodiment of the present invention further identifies the support requirement area of the lightweight 3D printed model. The specific operation is: importing the three-dimensional model after lightweight design, calling CAD software or structural analysis module to extract the geometric features of the model shape, and combining the gravity direction to identify the vertically suspended overhanging structure, large-span bridging structure, and local thin-walled or sharp-angled structure in the model. These areas may cause printing failure or deformation due to the lack of underlying material support during the printing process, and are therefore defined as "support requirement areas." For example, when printing a lightweight drone shell, there is a 2.5mm thick cantilever extension structure at the edge of the bottom plate of the lower battery compartment, with a span of 18mm. This area is determined to be a high-risk support requirement area. The "support requirement distribution data" is generated through the above identification process, and the positions and area ranges of all support structures to be added are marked in the form of spatial coordinates.

[0083] Step S32: Calculate the overhang angle and span distance of the support demand distribution data to obtain support arrangement reference data;

[0084] Based on the support demand distribution data, the embodiment of the present invention calculates the overhang angle and span distance of each area to generate a design basis for the subsequent support layout. During implementation, the angle between the model surface normal vector and the printing direction is calculated using triangulation, and angles greater than 45 degrees (usually the threshold for self-support in FDM printing) are identified as overhang surfaces; and the actual overhang length and bridge span (i.e., the horizontal projection distance between adjacent support points) corresponding to each support demand area are calculated. For example, when printing a lightweight robot arm component, it was found that there was an inclined surface with an overhang angle of 63 degrees in the shoulder connection area, and the span below it was 22mm, which obviously exceeded the self-support span allowed by the process. The final output is "support layout reference data", including the overhang angle matrix, span distance vector, and regional risk level, which is used to guide the selection of support structure type and distribution strategy.

[0085] Step S33: evaluating and calculating the support density requirements of each region of the 3D printing model based on the support arrangement reference data to obtain support density requirement data;

[0086] The embodiment of the present invention uses support layout reference data to quantitatively evaluate the support density requirements of each area of the model. Specifically: according to the size of the overhang angle and the length of the span distance, a support density grading standard is set. For example, the area with an angle greater than 60 degrees and a span exceeding 20mm is defined as a high-density support area, and 5 support points are set per square centimeter; the area with an angle between 45-60 degrees and a span between 10-20mm is defined as a medium-density area, and 3 support points are set per square centimeter; the rest are low-density support areas. Taking the 3D-printed lightweight aviation joint as an example, the bottom connection edge is a high-density area, and its support density requirement is 5 points / cm², and the middle curved surface is a medium-density area with a support density requirement of 3 points / cm². By traversing the entire model, "support density requirement data" is generated, which provides a quantitative basis for the next step of rationality analysis of support structure layout.

[0087] Step S34: performing a comparative analysis based on the support density requirement data and the pre-acquired actual support arrangement plan to obtain a support structure arrangement requirement deviation status;

[0088] The embodiment of the present invention compares and analyzes the support density requirement data with the pre-generated actual support layout plan to determine the deviation of the support structure distribution. Specifically, the printing path generation tool (such as Ultimaker Cura or Materialise Magics) is called to extract the actual support layout plan used by the current model, and the number of support points and layout density in each support requirement area are counted, and the difference is compared with the aforementioned density requirement data. If the actual support density deviates from the expected by more than 20%, it is considered that there is a layout deviation. For example, the required density of the side wall area of a lightweight printed medical device fixture is 4 points / cm², but the actual layout is only 2 points / cm², which is insufficient support; and the bottom plane area requires 2 points / cm², but the actual layout is 5 points / cm², which is too dense support, and both require adjustment and optimization. Output "support structure layout requirement deviation status", record all support areas that do not meet the design requirements and their deviation amounts, for reference in subsequent structural performance verification.

[0089] Step S35: Analyze and calculate the load sharing of multiple support points based on the support structure layout requirement deviation to obtain support point load distribution data; calculate the load transfer coordination between each support point based on the support point load distribution data, and predict the deterioration trend of the synchronization of the coordinated load bearing of multiple support points;

[0090] Based on the deviations in support structure layout requirements, the present invention performs mechanical simulations to calculate the load sharing among support points. Finite element analysis is used to create an overall mechanical model of the model and its support structure. Deadweight and typical printing process loads (such as cooling shrinkage stress and interlayer shear force) are applied to the model. The vertical and lateral forces borne by each support point throughout the printing process are calculated to determine whether a reasonable load sharing relationship is achieved. Furthermore, the coordination of load transfer between multiple support points in time and space is analyzed. Specifically, when a support point in a certain area has insufficient load-bearing capacity, can the surrounding support points promptly share the load to prevent local warping or collapse? For example, when printing a lightweight heat exchanger component with six support points on the overhanging lower edge, simulations revealed that two of these points prematurely overloaded, causing local deformation, indicating poor coordination. Based on the load response data, "support point load distribution data" is established. Combined with time series analysis, this data predicts the downward trend in the simultaneous load-bearing capacity of multiple support points during printing, known as the "synchronicity deterioration trend," to proactively identify the risk of support failure.

[0091] Step S36: Evaluate the degree of difference in support structure material consumption and load-bearing efficiency based on the synchronization deterioration trend.

[0092] Based on the synchronization deterioration trend, the embodiment of the present invention comprehensively evaluates the degree of difference between the support structure material consumption and its load-bearing efficiency. The specific method is: the material consumption of each support area is counted, and the average load that can be carried by the unit volume of support material is calculated, which is defined as the "support load-bearing efficiency". At the same time, a comparison index is introduced to perform a ratio analysis between the current support efficiency and the theoretical efficiency under the ideal state (synchronization of each support point, minimum total support, and no load concentration). For example, in a lightweight printed bionic beam structure, the bottom support consumes about 28cm³ of support material, and the average unit load-bearing efficiency is only 0.45N / mm³. After the optimized distribution, the efficiency is increased to 0.68N / mm³, an increase of more than 50%. The final output evaluation results, including the support structure material redundancy rate, support efficiency depreciation rate and optimization potential ratio, are used to support process feedback and cost control of the lightweight printing strategy, and provide a basis for achieving stable printing with minimum material input.

[0093] It is particularly important that step S36 includes the following steps:

[0094] Step S361: Statistically calculate the material consumption of each support structure according to the synchronization deterioration trend to obtain support material consumption data;

[0095] Based on the synchronization deterioration trend data obtained in the previous embodiment, this embodiment of the present invention counts the material usage of each support structure for the support structure area with coordinated load-bearing problems, thereby obtaining complete support material consumption data. In the specific operation, the lightweight design 3D printed model is imported into a slicing software (such as Simplify3D or PrusaSlicer), and the support analysis plug-in is enabled. The support structure is divided into independent units by region and their volume information is extracted. Taking the printing of a lightweight building component with a honeycomb structure as an example, the three regions of the support bottom edge, the curved support, and the internal through-hole support are counted separately, and the corresponding volumes are obtained as 10.5 cm³, 6.2 cm³, and 3.8 cm³, respectively. In addition, the structural type (columnar, grid-like, dendritic) and the generation method (automatic generation or manual editing) of each support segment are recorded as the basis for subsequent efficiency analysis.

[0096] Step S362: Calculate and evaluate the actual load-bearing capacity of each support structure based on the support material consumption data to obtain support load-bearing capacity data;

[0097] After obtaining the volume data of the support material, the embodiment of the present invention further measures and evaluates the actual load-bearing capacity of each support structure to generate support load-bearing capacity data. This step is achieved through finite element simulation: import the model into a structural simulation platform (such as ANSYS or Abaqus), set the printing material properties in each support area (such as the elastic modulus of the PLA material is 3200MPa and the Poisson's ratio is 0.35), and apply the load simulated from the printing process (including the model's own weight, thermal shrinkage reaction force, and interlayer tensile force) to the support end point to observe its maximum load-bearing capacity and instability point. For example, for the bottom columnar support body, a gradually increasing pressure is applied to its upper surface until the structure produces plastic deformation or local buckling, and the ultimate load is recorded as 78N. After similar analysis of other areas, the maximum load value corresponding to each support structure is output to form "support load-bearing capacity data."

[0098] Step S363: Calculating the load-bearing efficiency value of each unit material according to the support load-bearing capacity data to obtain unit material load-bearing efficiency data;

[0099] The embodiment of the present invention calculates the support bearing capacity data and the corresponding material usage in the previous step to obtain the effective load that each unit of support material can bear, and generates unit material bearing efficiency data. In the specific processing process, the support limit load is divided by its volume, and the ratio is defined as "unit material bearing efficiency", with the unit being N per cubic millimeter. For example, the material used for the bottom columnar support is 10.5cm³, and the bearing capacity is 78N, so its unit bearing efficiency is about 0.743N per cubic millimeter; while the middle arc surface support is 6.2cm³, and the bearing capacity under the material support is only 28N, so the efficiency is 0.452N / mm³. Finally, these results are combined into an efficiency data matrix with regional ID and efficiency value, which serves as the basis for subsequent comparative analysis of efficiency differences.

[0100] Step S364: performing comparative analysis on the efficiency performance of different support areas based on the unit material load efficiency data to obtain regional efficiency comparison data;

[0101] The embodiment of the present invention compares and analyzes the efficiency performance between different support areas based on the unit material load-bearing efficiency data to obtain regional efficiency comparison data. This step unifies the dimensions of the efficiency values of each region by normalization, and displays the efficiency distribution differences in the form of a chart or heat map. The specific method is: select the area with the highest efficiency value as the reference standard, and take the ratio of the unit efficiency of other areas to this area as the relative efficiency level. For example, if the efficiency of the bottom support area is 0.743N / mm³ and it is set to 100%, the relative efficiency of the arc support area is about 60.8%, and the relative efficiency of the internal support of the channel is about 74%. Combined with the spatial position identification, "regional efficiency comparison data" is generated, and areas where the efficiency is significantly lower than the average value can be further marked for structural optimization.

[0102] Step S365: Calculate the standard deviation and coefficient of variation of the efficiency values of each region based on the regional efficiency comparison data to obtain quantitative data on efficiency differences;

[0103] After obtaining the efficiency comparison data for each region, the embodiment of the present invention further calculates the standard deviation and coefficient of variation of the efficiency values of each region to quantify the overall difference in the efficiency of the support structure and generate efficiency difference quantitative data. The standard deviation represents the absolute degree of dispersion of the efficiency between different regions, and the coefficient of variation represents the relative dispersion level of the efficiency value (i.e., the standard deviation divided by the average efficiency value). For example, the unit efficiency of multiple regions is 0.743, 0.452, 0.615, and 0.578 N / mm³, respectively. The calculated standard deviation is approximately 0.11 N / mm³, the average value is 0.597 N / mm³, and the coefficient of variation is 18.4%. These statistics help determine 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 quantitative data" is generated for feedback optimization strategy.

[0104] Step S366: Evaluate the degree of efficiency difference in support structure material consumption and load-bearing based on the efficiency difference quantification data.

[0105] The embodiment of the present invention uses the quantitative data of efficiency differences to comprehensively evaluate the degree of efficiency difference 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. This step introduces the "efficiency depreciation rate" indicator, which is the ratio of the inefficient area to the total amount of material, to evaluate the degree of waste of the overall support. Specifically: the area with an efficiency lower than 80% of the overall average is defined as an "inefficient area", and the ratio of its total material volume to the volume of all support materials is calculated. For example, in a lightweight printed mechanical tray structure, the total support material volume is 21 cm³, and the support material in the inefficient area is 7.2 cm³. The efficiency depreciation rate is 34.3%, indicating that more than one-third of the support structures are inefficient and should be optimized by reconstruction or redistribution. Finally, the evaluation results of this step are output to form an "assessment report on the difference between support structure material consumption and load-bearing efficiency", which provides a quantitative basis and optimization direction for the iteration of support schemes for lightweight products.

[0106] Preferably, step S4 includes the following steps:

[0107] Step S41: Evaluate the adaptability of current printing process parameters based on the difference in support structure material consumption and load-bearing efficiency, and obtain process parameter adaptability data;

[0108] After completing the aforementioned assessment of the difference between support structure material consumption and load-bearing efficiency, the present embodiment compares this efficiency difference with the process parameters currently used in printing to evaluate the adaptability of the process parameters to support structure efficiency performance, thereby generating process parameter adaptability data. The specific method involves extracting core process parameters recorded during the printing process, including nozzle temperature (e.g., 210°C), print speed (e.g., 60 mm / s), and layer thickness (e.g., 0.2 mm), and performing a correlation analysis based on the distribution of the support structure's unit load-bearing efficiency. Regression analysis is used to identify the relationship between parameter changes and efficiency differences. For example, if a batch using low layer thickness (0.1 mm) and low speed (40 mm / s) shows a significant reduction in inefficient areas, this indicates that the current parameter combination has a positive impact on support efficiency and is rated as highly adaptable. Finally, "process parameter adaptability data" is generated, recording each parameter's contribution to efficiency performance and its adaptability score.

[0109] Step S42: Identifying 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;

[0110] After obtaining process parameter compatibility data, the present embodiment further identifies the risk levels for different parameter combinations, focusing specifically on the compatibility between temperature, speed, and layer thickness. This generates a printing process parameter mismatch risk profile. In specific implementations, a risk classification standard is introduced. For example, excessively high temperatures combined with low speeds can easily lead to overmelting collapse, resulting in a high risk profile; excessively high speeds combined with high and thick layers can lead to insufficient interlayer adhesion, resulting in a medium risk profile; and temperatures, speeds, and layer thicknesses all within reasonable ranges and in harmony with each other, resulting in a low risk profile. During the actual printing of a lightweight support structure part, a set of test parameter combinations was input into the risk assessment model. Five typical combinations were assigned risk scores, and two combinations were identified as exhibiting high mismatch risks (e.g., 230°C + 80 mm / s + 0.3 mm), which could lead to interlayer warping. The mismatch risk level and location identifier for each parameter combination were output to form a "printing process parameter mismatch risk profile."

[0111] Step S43: Analyze the deviation between the expected value and the actual value of the bonding strength between each slice layer according to the matching risk of the printing process parameters to obtain the inter-layer bonding strength deviation data;

[0112] Based on identified high-risk parameter combinations, the present invention evaluates the bond strength between different slice layers through actual testing combined with simulation. The deviation between expected and actual values is analyzed to generate interlayer bond strength deviation data. In implementation, the standard interlayer bonding stress in the structural simulation model is used as the expected value (for example, for PLA material, the theoretical bond strength is set at 8 MPa). Actual values are obtained by printing samples and then conducting microtensile tests (using a universal material testing machine) to measure the actual bond strength at different layers. For example, in the risk group sample, the actual measured bond strength of the bottom 10 layers was 4.5 MPa, and that of the middle layers was 6.2 MPa, with deviations of 43.75% and 22.5% from the expected values, respectively. These deviations are recorded layer by layer to form "interlayer bond strength deviation data," and areas with significant deviations are marked.

[0113] Step S44: tracing back the process parameter combination that causes abnormal bonding strength based on the interlayer bonding strength deviation data to obtain abnormal tracing data of interlayer bonding strength of slices;

[0114] After obtaining the interlayer bonding strength deviation data, the embodiment of the present invention traces back the process parameter combination that caused the outstanding deviation, traces the cause of the abnormality, and outputs the slice interlayer bonding strength abnormality tracing data. The specific method is to correlate the slice layer information corresponding to the layer segment with a deviation 20% higher than the average level with the printing parameter log, and identify the temperature, speed, and layer thickness values used in the abnormal strength layer segment. For example, in a certain batch of samples, it was found that the central area with outstanding deviations used a combination of 230°C temperature, 100mm / s speed and 0.2mm layer thickness. After tracing, it was confirmed that the parameters were temporarily adjusted and not synchronized with the material flow rate, resulting in poor bonding. Finally, the "slice interlayer bonding strength abnormality tracing data" is formed, and each set of data includes the abnormal layer segment number, parameter combination, deviation amplitude and remarks analysis.

[0115] Step S45: adjusting the filling density, filling pattern and printing path parameters according to the abnormal tracing data of the inter-layer bonding strength of the slices to obtain filling parameter adjustment data;

[0116] Based on the tracing results, the embodiment of the present invention optimizes the filling strategy of the printing area that causes abnormal interlayer bonding strength, focuses on adjusting the filling density, filling pattern and printing path parameters, and outputs the filling parameter adjustment data. During implementation, the filling parameters of the local area are set through slicing software (such as Cura). For example, for areas where the bonding strength deviation exceeds 30%, the filling density is increased from the original 10% to 20%, and the filling pattern is changed from linear to triangular honeycomb. At the same time, the "staggered path" function is enabled to improve the local interlayer cross-linking strength. For the printing path, the Z-shape is changed to spiral inward-outward filling to enhance the uniformity of the thermal gradient. When printing a lightweight grid frame support component, the above parameters are applied to locally enhance the key layer segment in the middle to obtain "filling parameter adjustment data", including the values before and after parameter adjustment, adjustment position, optimization purpose and expected impact.

[0117] Step S46: verifying the degree to which the adjusted parameters meet the lightweight target and strength requirements based on the filled parameter adjustment data, and obtaining parameter verification data;

[0118] After adjusting the filling parameters, the embodiment of the present invention verifies its impact on the lightweight goal and strength requirements by reprinting the sample and conducting performance tests, evaluates whether the adjustment is reasonable, and generates parameter verification data. During implementation, the adjusted sample is printed and mass measurement and structural strength testing are performed. The lightweight index is calculated by comparing the total mass of the sample with the original mass (for example, the original sample is 58g, and after optimization it is 42.8g, 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 120N, and after adjustment it is 110N, with a retention rate of 91.7%). The interlayer bonding strength error is controlled within ±0.3MPa. These data are comprehensively used to evaluate the adjustment effect to generate "parameter verification data", including verification results of dimensions such as lightweight achievement rate, bearing strength retention rate, and interlayer bonding deviation amplitude.

[0119] Step S47: According to the parameter verification data, a printing and filling parameter configuration is selected that satisfies the following conditions: a material weight reduction rate greater than 25%, a structural bearing strength retention rate not less than 90%, and an interlayer bonding strength deviation less than 5%, to obtain printing and filling parameter optimization data.

[0120] According to the aforementioned parameter verification data, the embodiment of the present invention selects the optimal printing and filling parameter configuration that meets the three index requirements from all test parameter combinations: the material weight reduction rate is greater than 25%, the structural bearing strength retention rate is not less than 90%, and the interlayer bonding strength deviation is less than 5%, and finally obtains the printing and filling parameter optimization data. In the specific operation, the filtering conditions are established, and all test sample data are imported into Excel or a database analysis system. 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 selected as the recommended configuration. For example, the optimal combination finally determined is a filling density of 15%, a honeycomb filling pattern, a staggered inward path strategy, a printing temperature of 210°C, a speed of 60mm / s, and a layer thickness of 0.15mm. Output the "printing and filling parameter optimization data" as the standard process configuration for the subsequent formal printing of lightweight products.

[0121] It is particularly important that step S5 includes the following steps:

[0122] Perform material distribution density mapping on the model's internal space based on the print filling parameter optimization data to obtain three-dimensional material density distribution data;

[0123] Construct material filling strategies for different regions based on the three-dimensional material density distribution data to obtain regional material filling data;

[0124] Generate corresponding printing path coordinate sequence according to the regional material filling data to obtain basic printing path data;

[0125] Based on the basic printing path data, the printing sequence and path connection method are optimized to reduce material waste and obtain lightweight printing path planning data;

[0126] Convert the printing path planning scheme into control instruction codes executable by the printer to obtain digital control instruction data;

[0127] Integrate printing parameters, path information and material control commands based on digital control instruction data to obtain digital control model data;

[0128] Based on the complete digital control model data, a lightweight digital control model that can be directly used for 3D printing equipment is output.

[0129] The embodiment of the present invention performs material distribution density mapping processing on the internal space of the model based on the printing filling parameter optimization data, 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 performed according to the structural strength and functional requirements required by different areas. In operation, the model is divided into a number of grid units using the results of finite element analysis, and the most suitable material filling density is calculated for each unit, such as setting it to above 0.8 in the connection support area with greater load-bearing capacity, and setting it to about 0.2 in the secondary decorative area, and generating the material filling rate corresponding to each voxel position in the three-dimensional space, thereby forming complete three-dimensional material density distribution data, which is usually stored in units of volume pixels (voxel) and presented as different grayscale or color density layers after visualization. After obtaining the three-dimensional material density distribution data, continue to implement the regional material filling strategy construction. The specific method involves spatially clustering the model based on the spatial density distribution map, grouping adjacent or similarly filled voxels into the same region. For example, a density-based spatial clustering algorithm (such as DBSCAN or region growing) is used to identify high-density reinforced areas, medium-density functional areas, and low-density lightweight areas. A corresponding material infill strategy is then developed for each region: for example, honeycomb infill is used to enhance structural stability in high-density areas, triangular grid infill is used to balance strength and material consumption in medium-density areas, and linear or hollow infill is used to reduce weight in low-density areas. The final output is a set of regional material infill data containing spatial coordinates and corresponding infill strategies. Based on this regional material infill data, a corresponding printing path coordinate sequence is generated. During this process, the 3D model is sliced layer by layer using slicing software (such as Cura or Slic3r). The region division information for each layer is combined with the infill strategy data, and a pre-defined path planning algorithm is used to generate the coordinates of the specific infill path trajectory points for each layer. For example, for honeycomb infill areas, the path planning layout will arrange the coordinate points along the diagonal lines of the hexagon, while for linear infill, a linear trajectory is directly generated in parallel or intersecting directions. This path information is output in G-code or other structured formats to form basic printing path data for subsequent path optimization processing. Lightweight printing path optimization is performed based on the basic printing path data. This step mainly solves the problem of material waste caused by frequent path switching or uneven material stacking in traditional path planning. Optimization methods include sequential optimization and connection optimization: sequential optimization reduces nozzle empty strokes through path sorting algorithms (such as greedy nearest neighbors), and connection optimization uses jump connections or spiral connections to reduce the number of path interruptions, thereby improving printing efficiency and reducing material redundancy. After optimization, the path data is regenerated, which is lightweight printing path planning data, which is more in line with the balance between material saving and structural strength. After the printing path planning data is generated, it needs to be converted into control instruction codes that can be recognized by the printer.Operationally, the path translation module encodes path coordinate information and control parameters corresponding to the fill strategy, such as speed, temperature, and layer height, into a G-code command sequence. Syntax matching and command encapsulation are performed based on the printer's hardware characteristics. For example, the corresponding move command (G1) for each path segment is accompanied by the corresponding nozzle temperature setting (M104) and material extrusion speed (E value), and the resulting output is digital control command data that can be executed by the specific printer. Based on this digital control command data, the printing parameters, path information, and material control commands are integrated to form a complete digital control model data. During this process, a unified data structure file (such as JSON or XML format) is constructed. This file contains data such as material fill density distribution, path trajectory points, nozzle control parameters, and cooling fan control timing. A data interface is provided for reading by different brands and models of printing equipment, thus achieving integrated model-process-equipment mapping control. Based on this constructed digital control model data, a lightweight digital control model that can be directly used in 3D printing equipment is output. In specific applications, this control model can be directly imported into FDM devices such as Ultimaker and Raise3D. Printing tasks can be executed through the device's built-in control program, achieving a precise printing process 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 greater than 90%. For example, when printing a lightweight drone bracket, the system can automatically distribute material density to different areas, achieving center reinforcement and edge weight reduction, thereby reducing overall material cost and weight while ensuring flight strength.

[0130] The present invention further provides a 3D printed product lightweight design system for executing the above-mentioned 3D printed product lightweight design method, wherein the 3D printed product lightweight design system comprises:

[0131] The geometric feature analysis module is used to obtain the geometric data information of the 3D printing model; extract the internal volume distribution characteristics of the model and evaluate the uniformity of the model wall thickness distribution based on the geometric data information, and predict the change trend of the model structure load bearing capacity to obtain structural load prediction data;

[0132] The material optimization analysis module is used to detect the optimized spatial attenuation trend of material distribution density based on structural load prediction data; predict the dynamic interference strength of redundant material removal within the model based on the optimized spatial attenuation trend to obtain redundant removal strength data; and determine the coupled balance trend between model lightweighting and strength preservation based on the redundant removal strength data and the optimized spatial attenuation trend;

[0133] The support structure optimization module is used to predict the deviation of support structure layout requirements using the coupling balance trend; predict the deterioration trend of the synchronization of the coordinated load bearing of multiple support points based on the deviation of the support structure layout requirements; and evaluate the degree of difference in the efficiency of support structure material consumption and load bearing based on the deterioration trend of synchronization;

[0134] The process parameter optimization module is used to evaluate the matching risk of printing process parameters based on the degree of efficiency difference; based on the matching risk of printing process parameters, it can trace the abnormal bonding strength between slice layers and optimize the printing and filling parameters to obtain the printing and filling parameter optimization data;

[0135] The path planning generation module is used to construct a three-dimensional material distribution model based on the printing and filling parameter optimization data, and generate a lightweight printing path planning scheme; according to the printing path planning scheme, a lightweight digital control model of the 3D printed product is output.

[0136] The present invention also provides a computer medium storing a computer program, which implements the above-mentioned 3D printing product lightweight design method when executed.

[0137] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0138] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A lightweight design method for 3D printing products, characterized in that: The following steps are involved: Step S1: Acquire geometric data information of the 3D printing model; extract the model's internal space volume distribution characteristics and evaluate the model's wall thickness uniformity based on the geometric data information, and predict the change trend of the model's structural load bearing capacity to obtain structural load prediction data; Step S2: detecting the optimized spatial attenuation trend of the material distribution density based on the structural load prediction data, wherein the optimized spatial attenuation trend is specifically a decreasing trend of the material density of different regions with distance from the center of the model outward; According to the optimized spatial attenuation trend, the dynamic interference intensity of the removal of redundant materials inside the model is predicted to obtain redundant removal intensity data, wherein the redundant removal intensity data specifically refers to the degree of influence of the removal of redundant materials on the stability of the model; Determine the coupled balance trend between model lightweighting and strength preservation based on redundant strength data removal and optimized spatial attenuation trend; Step S3: using the coupling balance trend to predict the support structure layout demand deviation; based on the support structure layout demand deviation, predicting the synchronization deterioration trend of the coordinated load bearing of multiple support points; Evaluate the degree of difference in efficiency between support structure material consumption and load-bearing capacity based on the synchronization deterioration trend; Step S4: Evaluate the matching risk of printing process parameters based on the degree of efficiency difference; Based on the matching risk of printing process parameters, the abnormal bonding strength between slice layers is traced, and the printing and filling parameters are optimized to obtain the printing and filling parameter optimization data; Step S5: constructing a three-dimensional material distribution model based on the printing and filling parameter optimization data, and generating a lightweight printing path planning scheme; outputting a lightweight digital control model of the 3D printed product based on the printing path planning scheme.

2. The lightweight design method for 3D printed products according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining geometric data information by scanning the surface of the 3D printed model; performing three-dimensional coordinate point cloud extraction processing on the geometric data information to obtain model surface contour data; Step S12: calculating and processing the curvature distribution of the outer surface of the model according to the surface contour data of the model to obtain surface curvature distribution data; Step S13: performing internal space volume division processing on the surface curvature distribution data to obtain space volume distribution data; performing statistical analysis processing on the internal space density of the model based on the space volume distribution data to obtain internal space density distribution data; performing feature extraction on the internal space density distribution data to obtain feature data of the internal space volume distribution of the model; Step S14: obtaining the wall thickness measurement data of each cross section of the model, calculating the thickness variation gradient of the wall thickness measurement data, and evaluating the wall thickness uniformity index to obtain wall thickness uniformity evaluation data; Step S15: using the wall thickness uniformity assessment data to predict the load bearing capacity change trend of the model structure to obtain structural load prediction data.

3. The lightweight design method for 3D printed products according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: Obtaining model material attribute parameter data; Step S152: Calculating the structural stress distribution based on the material property parameter data and the wall thickness uniformity evaluation data, and performing load bearing critical value analysis to obtain load bearing critical data; Step S153: Evaluate the load bearing capacity of the structure based on the load bearing critical data and the wall thickness uniformity evaluation data to obtain current load bearing capacity data; Step S154: performing time series variation trend fitting on the current load bearing capacity data, and establishing and processing a structural load prediction model based on the current load bearing capacity data to obtain structural load prediction data.

4. The lightweight design method for 3D printed products according to claim 3, characterized in that: In step S2, detecting the optimized spatial attenuation trend of the material distribution density based on the structural load prediction data includes: Based on the structural load prediction data, the 3D printing model is divided into 1mm-20mm grid spacing to obtain the spatial load distribution data; According to the spatial load distribution data, the grid cells with load bearing strength lower than 60% of the design load threshold are screened to obtain the low load area distribution data; Based on the low-load area distribution data, the material density redundancy of each area is calculated to obtain the material density optimization potential data; According to the material density optimization potential data, the density distribution law from the center to the periphery of the model is analyzed to obtain the material density spatial attenuation data; Based on the spatial attenuation data of material density, the areas with attenuation coefficients greater than 0.3 are marked and their volume proportions are counted to obtain optimized spatial distribution data; The spatial attenuation trend characteristics when the volume ratio exceeds 25% are evaluated based on the optimized spatial distribution data, and the optimized spatial attenuation trend of the material distribution density is obtained.

5. The lightweight design method for 3D printing products according to claim 4, characterized in that: In step S2, the dynamic interference intensity prediction of removing redundant materials inside the model according to the optimized spatial attenuation trend includes: According to the optimized spatial attenuation trend, the removable material area is divided into 0.5mm-5mm precision divisions to obtain redundant material removal unit data; Based on the redundant material removal unit data, the stress transfer influence range of each unit after removal is calculated on the adjacent area, and the stress influence diffusion data is obtained; Analyze the load redistribution path during the material removal process based on the stress influence diffusion data to obtain the load transfer path data; Predicting the structural deformation propagation strength caused by the removal operation based on the load transfer path data to obtain deformation propagation strength data; The degree of interference of the material removal process on the overall stability of the model is evaluated based on the deformation propagation intensity data, and the structural stability interference data is obtained; The interference intensity coefficient is quantified based on the structural stability interference data and the dynamic impact range of the removal operation is predicted to obtain the redundancy removal intensity data.

6. The lightweight design method for 3D printing products according to claim 5, characterized in that: In step S2, the coupled balance trend of model lightweighting and strength preservation is determined based on redundant removal of strength data and optimization of spatial attenuation trends, including: The corresponding relationship between material removal amount and strength loss is established based on the redundant removal strength data and the optimized spatial attenuation trend, and the strength loss correlation data is obtained; Based on the strength loss correlation data, the change rate of the structural bearing capacity under different material removal ratios is calculated to obtain the bearing capacity change data; The trade-off between lightweighting and strength retention is evaluated based on the load-bearing capacity change data to obtain lightweight-strength trade-off data. Determine the safe threshold range of material removal based on lightweight-strength trade-off data and determine safe removal threshold data; Predicting the equilibrium state between the lightweight effect and the strength change within the threshold range based on the safety removal threshold data, and obtaining equilibrium state prediction data; Based on the equilibrium state prediction data, the dynamic balance development direction of lightweighting and strength maintenance is analyzed, and the coupled balance trend of model lightweighting and strength maintenance is obtained.

7. The lightweight design method for 3D printing products according to claim 6, characterized in that: Step S3 includes the following steps: Step S31: performing support requirement area identification processing on the 3D printing model according to the coupling balance trend to obtain support requirement distribution data; Step S32: Calculate the overhang angle and span distance of the support demand distribution data to obtain support arrangement reference data; Step S33: evaluating and calculating the support density requirements of each region of the 3D printing model based on the support arrangement reference data to obtain support density requirement data; Step S34: performing a comparative analysis based on the support density requirement data and the pre-acquired actual support arrangement plan to obtain a support structure arrangement requirement deviation status; Step S35: Analyze and calculate the load sharing of multiple support points based on the support structure layout requirement deviation to obtain support point load distribution data; calculate the load transfer coordination between each support point based on the support point load distribution data, and predict the deterioration trend of the synchronization of the coordinated load bearing of multiple support points; Step S36: Evaluate the degree of difference in support structure material consumption and load-bearing efficiency based on the synchronization deterioration trend.

8. The lightweight design method for 3D printing products according to claim 7, characterized in that: Step S4 includes the following steps: Step S41: Evaluate the adaptability of current printing process parameters based on the difference in support structure material consumption and load-bearing efficiency, and obtain process parameter adaptability data; Step S42: Identifying 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 matching risk of the printing process parameters to obtain the inter-layer bonding strength deviation data; Step S44: tracing back the process parameter combination that causes abnormal bonding strength based on the interlayer bonding strength deviation data to obtain abnormal tracing data of interlayer bonding strength of slices; Step S45: adjusting the filling density, filling pattern and printing path parameters according to the abnormal tracing data of the inter-layer bonding strength of the slices to obtain filling parameter adjustment data; Step S46: verifying the degree to which the adjusted parameters meet the lightweight target and strength requirements based on the filled parameter adjustment data, and obtaining parameter verification data; Step S47: According to the parameter verification data, a printing and filling parameter configuration is selected that satisfies the following conditions: a material weight reduction rate greater than 25%, a structural bearing strength retention rate not less than 90%, and an interlayer bonding strength deviation less than 5%, to obtain printing and filling parameter optimization data.

9. A lightweight design system for 3D printing products, characterized in that: For executing the 3D printing product lightweight design method according to claim 1, the 3D printing product lightweight design system comprises: The geometric feature analysis module is used to obtain the geometric data information of the 3D printing model; extract the internal volume distribution characteristics of the model and evaluate the uniformity of the model wall thickness distribution based on the geometric data information, and predict the change trend of the model structure load bearing capacity to obtain structural load prediction data; The material optimization analysis module is used to detect the optimized spatial attenuation trend of material distribution density based on structural load prediction data; predict the dynamic interference strength of redundant material removal within the model based on the optimized spatial attenuation trend to obtain redundant removal strength data; and determine the coupled balance trend between model lightweighting and strength preservation based on the redundant removal strength data and the optimized spatial attenuation trend; The support structure optimization module is used to predict the deviation of support structure layout requirements using the coupling balance trend; predict the deterioration trend of the synchronization of the coordinated load bearing of multiple support points based on the deviation of the support structure layout requirements; and evaluate the degree of difference in the efficiency of support structure material consumption and load bearing based on the deterioration trend of synchronization; The process parameter optimization module is used to evaluate the matching risk of printing process parameters based on the degree of efficiency difference; based on the matching risk of printing process parameters, it can trace the abnormal bonding strength between slice layers and optimize the printing and filling parameters to obtain the printing and filling parameter optimization data; The path planning generation module is used to construct a three-dimensional material distribution model based on the printing and filling parameter optimization data, and generate a lightweight printing path planning scheme; according to the printing path planning scheme, a lightweight digital control model of the 3D printed product is output.

10. A computer medium storing a computer program, characterized in that: When the computer program is executed, the method for lightweight design of 3D printed products according to any one of claims 1 to 8 is implemented.

Images