3D Printing Model Slice Feature Compensation Method, System and Medium

Abstract

The present invention relates to the technical field of 3D printing models, and particularly to a method, system and medium for compensating slice features of 3D printing models. The method includes the following steps: collecting a 3D printing model and splitting out the model feature skeleton to generate horizontal slice features, then collecting the 3D printing environment and performing virtual simulation, analyzing the environmental stress of the skeleton slice features to obtain environmental stress data, then deducing the deformation scale and predicting thermal deformation based on the environmental stress data to generate relevant data. On this basis, performing reverse deformation simulation, evaluating the deformation repair requirements and planning the deformation compensation path, and finally realizing the compensation simulation of the model feature skeleton, analyzing non-target deformations and adjusting the compensation path, so as to optimize the feature compensation method. The present invention realizes a method for compensating slice features of 3D printing models that overall improves the accuracy and quality of 3D printing models.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D printing models, and particularly to a 3D printing model slice feature compensation method, system and medium. Background Art

[0002] In the actual 3D printing process, due to the influence of environmental conditions (such as temperature, humidity, air flow, etc.), material properties (such as thermal expansion coefficient, curing shrinkage rate, internal stress release behavior, etc.) and printing process parameters (such as scanning strategy, layer thickness, filling method, etc.), 3D printing models often have problems such as deformation, warping, and stress concentration. These problems not only lead to a decrease in the dimensional accuracy of the printed product, but also may cause deterioration of the structural performance, making the final product unable to meet the design requirements, thus affecting the popularization and development of 3D printing technology. At present, for the deformation problems that occur during 3D printing, traditional compensation methods mainly rely on empirical rules or simple deformation compensation models. Most of these methods fail to accurately predict based on physical environment, material stress and thermal deformation data, resulting in low reliability of the compensation strategy. The temperature gradient of the printing environment, the binding force of the support structure, and the dynamic change of the internal stress of the material may all lead to unpredictable deformation, which is difficult to effectively handle by traditional methods. Due to the failure to optimize for different printing environments, material properties and printing parameters, the existing compensation strategies are often less versatile and cannot meet the precise compensation requirements under different working conditions. Summary of the Invention

[0003] Based on this, it is necessary to provide a 3D printing model slice feature compensation method, system and medium to solve at least one of the above technical problems.

[0004] To achieve the above object, the 3D printing model slice feature compensation method includes the following steps:

[0005] Step S1: Collect a 3D printing model; split the model feature skeleton of the 3D printing model; perform a horizontal slice mapping on the model feature skeleton to generate skeleton slice features;

[0006] Step S2: Collect the 3D printing environment; perform a virtual environment field simulation according to the 3D printing environment, and perform an environmental stress analysis on the skeleton slice features based on the simulated environment field to generate environmental stress data;

[0007] Step S3: Perform a deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; perform a thermal deformation prediction on the skeleton slice features according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data;

[0008] Step S4: Perform reverse deformation simulation based on the thermal deformation data, and analyze the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure; Plan the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements;

[0009] Step S5: Perform compensation simulation on the model feature skeleton according to the deformation compensation path to obtain the simulated compensation process; Analyze the non-target deformation in the simulated compensation process, and adjust the compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0010] By collecting the 3D printing model and splitting out the model feature skeleton, the present invention can lay a foundation for subsequent analysis and processing. The skeleton slice features generated by the lateral slice mapping of the model feature skeleton provide structural detail information. Collecting the 3D printing environment and performing virtual environment field simulation can comprehensively understand the external influencing factors during the printing process. Environmental stress analysis provides key data for the performance evaluation of the skeleton slice features under specific environments. The deformation scale correlation deduction based on the environmental stress data provides theoretical support for understanding the deformation behavior of materials under different stress conditions. Thermal deformation prediction can quantify the thermal influence on the materials during the printing process, and the generated thermal deformation data provides a basis for model optimization. The implementation of reverse deformation simulation can clarify the deviation between the printer settings and the physical model. The comparative analysis of the standard printing model and the simulated reverse deformation structure helps to accurately identify the deformation repair requirements. The planning of the deformation compensation path provides a strategic guidance for the repair process. The compensation simulation of the model feature skeleton can be pre-verified before actual printing. Analyzing the non-target deformation in the simulated compensation process provides feedback and adjustment basis for optimizing the compensation scheme, overall improving the accuracy and quality of the 3D printing model, and providing effective technical support for subsequent printing process control and finished product quality assurance.

[0011] Preferably, step S1 includes the following steps:

[0012] Step S11: Collect the 3D printing model; Perform high-precision point cloud data collection on the 3D printing model to obtain the original point cloud data;

[0013] Step S12: Perform grid reconstruction on the original point cloud data and perform smoothing processing to generate a refined grid structure;

[0014] Step S13: Extract the center line of the refined grid structure, and extract the model feature skeleton based on the center line for the refined grid structure;

[0015] Step S14: Perform multi-directional rasterization encoding on the model feature skeleton to obtain a skeleton voxel grid; Perform inertial principal axis positioning on the skeleton voxel grid, and perform inertial principal axis calibration on the inertial principal axis to obtain an oriented slice coordinate system;

[0016] Step S15: Perform dynamic step slicing on the directional slicing coordinate system to generate a sliced cross-sectional profile; extract feature key points from the sliced cross-sectional profile to generate a skeleton slice feature.

[0017] The present invention provides an accurate basis for the digital reconstruction of 3D printing models through the acquisition of high-precision point cloud data. The acquisition of the original point cloud data ensures the integrity of the model details. The refined grid structure generated by the meshing reconstruction and smoothing process improves the operability and visualization effect of the model. The extraction of the center line lays a key foundation for the subsequent model feature analysis. The skeleton extraction can effectively simplify complex models, making the feature analysis and compensation more efficient. The multi-directional rasterization coding enhances the structural representation ability of the skeleton. The inertial principal axis positioning and calibration ensure the consistency and accuracy of the slicing process. The establishment of the directional slicing coordinate system provides a clear reference framework for the slicing process. The dynamic step slicing process combined with the generation of the cross-sectional profile can achieve a more accurate slicing effect. The extraction of feature key points provides important data support for the subsequent compensation and optimization, overall improving the accuracy and quality of the 3D printing model, and providing a solid technical guarantee for the subsequent printing process and model application.

[0018] Preferably, step S2 includes the following steps:

[0019] Step S21: Collect the 3D printing environment; identify the distribution of environmental parameters according to the 3D printing environment; perform interpolation processing on the distribution of environmental parameters and construct a three-dimensional environmental gradient field;

[0020] Step S22: Calculate the hydrodynamic parameters of the three-dimensional environmental gradient field. During the calculation process, the fluid viscosity is set to 1.85×10-5 Pa·s and the density is set to 1.225 kg / m³; perform virtual environment field simulation on the distribution of environmental parameters based on the hydrodynamic parameters to obtain a simulated environment field;

[0021] Step S23: Perform superposition analysis on the simulated environment field and the skeleton slice feature, and construct an interaction influence matrix;

[0022] Step S24: Perform heat flow - material stress transfer simulation based on the interaction influence matrix to generate simulated stress transfer data; determine the environmental stress data based on the simulated stress transfer data.

[0023] The present invention provides a basis for comprehensively understanding the distribution of environmental parameters by collecting the capabilities of the 3D printing environment. The interpolation processing of environmental parameters ensures the continuity and accuracy of environmental data. The construction of the three-dimensional environmental gradient field provides rich information for subsequent simulation analysis, including the calculation of hydrodynamic parameters, which enables the environmental simulation to have a real physical basis. The realization of virtual environmental field simulation can quickly evaluate the impacts under different environmental conditions. The superimposed analysis of the simulation environmental field and the skeleton slice features strengthens the correlation between the model and the environment. The establishment of the interaction influence matrix provides a systematic data framework, laying a foundation for analyzing the influence of environmental factors on model deformation. The development of heat flux-material stress transfer simulation quantifies the stress transfer process of materials under specific environments, and the generated simulated stress transfer data provides key parameters for subsequent environmental stress assessment, overall enhancing the ability to identify and compensate for environmental impacts during the 3D printing process and providing a scientific basis for high-quality printing.

[0024] Preferably, the deformation scale correlation deduction of the skeleton slice features based on the environmental stress data in step S3 includes:

[0025] Conduct stress contact simulation on the environmental stress data and the skeleton slice features, and perform elastoplastic tensor mapping based on the simulated contact data to generate an initial deformation tensor field;

[0026] Fit the micro-layer thickness uniformity distribution of the skeleton slice features to obtain layer thickness stable data;

[0027] Conduct heat transfer response simulation on the layer thickness stable data, and analyze the thermal inertia of the skeleton material based on the simulated heat transfer response data;

[0028] Match the material thermal inertia data according to the preset material database to obtain model material data;

[0029] Endow the initial deformation tensor field with material property constraints based on the model material data, and determine the skeleton deformation amplitude;

[0030] Identify the deformation critical point according to the skeleton deformation amplitude, and segment the skeleton deformation sensitivity based on the deformation critical point;

[0031] Correspondingly correlate the environmental stress data based on the skeleton deformation sensitivity to obtain the skeleton stress deformation scale.

[0032] The present invention provides a profound understanding of the performance of the model in the actual environment through stress contact simulation of environmental stress data and the characteristics of the skeleton slices. The initial deformation tensor field generated by the elastoplastic tensor mapping lays a mathematical foundation for subsequent deformation analysis. The fitting of the micro-layer thickness uniformity distribution can effectively evaluate the consistency of the layer thickness, thereby increasing the quality stability of the printed product. The implementation of the heat transfer response simulation provides the response characteristics of the model material to thermal changes, which helps to deeply analyze the thermal inertia of the skeleton material and improves the prediction ability of material behavior. The material feature matching realizes accurate material selection with the support of a preset material database. The acquisition of model material data provides a specific material response basis for deformation analysis. The process of constraining the initial deformation tensor field ensures the rationality and scientificity of the model deformation amplitude. The identification of the deformation critical point provides a specific reference for optimization design and production. The segmentation of the deformation sensitivity degree can effectively formulate corresponding compensation strategies for specific regions. Finally, the correlation and integration of environmental stress data and the skeleton stress deformation scale form a comprehensive mechanical behavior analysis, providing exact data support and theoretical basis for improving the accuracy and quality of 3D printed models.

[0033] Preferably, the thermal deformation prediction of the skeleton slice features according to the skeleton stress deformation scale and environmental stress data in step S3 includes:

[0034] Performing heat conduction attenuation simulation according to the environmental stress data to obtain temperature gradient change data;

[0035] Performing heat conduction response mapping on the skeleton stress deformation scale based on the temperature gradient change data and constructing a thermal stress distribution matrix;

[0036] Performing thermal field hysteresis effect fitting according to the thermal stress distribution matrix and performing hysteresis compensation to generate thermal inertia compensation data;

[0037] Performing interlayer thermal expansion analysis based on the thermal inertia compensation data to obtain interlayer expansion data;

[0038] Predicting the deformation trajectory of the skeleton slice features according to the interlayer expansion data to generate thermal deformation data.

[0039] Through the simulation of the thermal conduction attenuation of environmental stress data, the present invention can effectively reveal the influence of temperature changes on material properties. The temperature gradient change data provides an important basis for subsequent thermal stress analysis. The thermal conduction response mapping of the skeleton stress deformation scale can quantify the behavior of materials under different temperature conditions. The construction of the thermal stress distribution matrix deepens the understanding of the internal stress state of materials. The fitting of the thermal field hysteresis effect helps to identify and compensate for the dynamic response caused by temperature changes. The generated thermal inertia compensation data provides a reasonable compensation basis for the model in a temperature-changing environment. The implementation of the interlayer thermal expansion fitting ensures a more uniform thermal stress distribution between different layers. The acquisition of the interlayer expansion data provides necessary information for the accurate prediction of the deformation trajectory. The finally generated thermal deformation data provides a scientific quantitative basis for the deformation phenomenon occurring during the 3D printing process, overall improving the adaptability of the printed model to the influence of thermal stress and the prediction accuracy, and providing strong support for high-quality printing.

[0040] Preferably, the reverse deformation simulation based on the thermal deformation data and the analysis of the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure in step S4 include:

[0041] Perform vector reversal processing on the thermal deformation data and construct a reverse deformation matrix;

[0042] Perform recursive mirror transformation on the reverse deformation matrix until the transformation error of the reverse deformation matrix is lower than the preset error threshold to generate reverse deformation mapping data;

[0043] Perform reverse projection on the thermal deformation data through the reverse deformation mapping data and construct a simulated reverse deformation structure based on the reverse projection data;

[0044] Perform structural differential comparison on the preset standard printing model and the simulated reverse deformation structure to obtain structural difference data;

[0045] Analyze the deformation repair requirements based on the structural difference data to obtain the deformation repair requirements.

[0046] The present invention lays a foundation for reverse deformation simulation through vector reverse processing of thermal deformation data. The construction of the reverse deformation matrix provides a systematic mathematical model. Recursive mirror transformation ensures the accuracy and precision of matrix transformation. Controlling the transformation error within a preset threshold guarantees the reliability of the results. The generated reverse deformation mapping data provides assurance for the inverse projection of thermal deformation data. The establishment of the inverse projection data supports the construction of the simulated reverse deformation structure. The differential comparison between the preset standard printing model and the simulated reverse deformation structure deepens the understanding of structural changes. The obtained structural difference data provides a scientific basis for the analysis of subsequent deformation repair requirements. The analysis of deformation repair requirements can formulate compensation plans targeted, overall improving the adaptability and accuracy of the model under different printing conditions, providing rational theoretical guidance and practical guarantee for high-quality printing.

[0047] Preferably, planning the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements in step S4 includes:

[0048] Determine the required deformation area according to the deformation repair requirements;

[0049] Match the skeleton stress deformation scale corresponding to the area based on the required deformation area;

[0050] Extract the required deformation amplitude of the deformation repair requirements;

[0051] Perform required stress conversion on the skeleton stress deformation scale corresponding to the area based on the required deformation amplitude to obtain the required deformation stress;

[0052] Deduce the compensation path based on the required deformation stress and the required deformation area to generate the deformation compensation path.

[0053] The present invention ensures the accurate identification of the problem area of the model through the positioning of deformation repair requirements. The confirmation of the required deformation area provides a clear goal for subsequent compensation work. The matching of the skeleton stress deformation scale enhances the understanding and application of the stress state of the model. The confirmed required deformation amplitude lays a foundation for the formulation of compensation strategies. The required stress conversion process ensures the scientificity and rationality of the compensation strategy. The deduction of the compensation path based on the required deformation stress enables targeted compensation implementation. The generated deformation compensation path provides reliable guidance for actual operation, overall improving the accuracy and structural stability of the 3D printing model, providing effective data support and decision-making basis for optimizing the printing process.

[0054] Preferably, step S5 includes the following steps:

[0055] Step S51: Perform path discretization processing on the deformation compensation path to obtain a set of path control points; perform deformation compensation simulation on the model feature skeleton based on the set of path control points to generate a simulation compensation process;

[0056] Step S52: Identify the deformation compensation target area during the compensation process; analyze the non-target areas during the compensation process based on the deformation compensation target area;

[0057] Step S53: Compare the deformation before and after compensation of the non-target area according to the model feature skeleton to obtain the non-target deformation;

[0058] Step S54: Trace back the compensation process based on the non-target deformation, and determine the initial time point of the non-target deformation based on the traced-back compensation process; analyze the cause of the non-target deformation according to the initial time point of the non-target deformation;

[0059] Step S55: Simulate the path adjustment of the deformation compensation path based on the cause of the non-target deformation, and perform conflict detection on the simulated adjusted compensation path to generate a conflict set of compensation path changes. Among them, the step size of the path adjustment simulation is limited to 0.5 mm, and the standard for conflict detection is that the part where the compensation paths are no more than 2 mm apart is regarded as the conflict area, and the generation basis of the conflict set is that the cumulative path error value is greater than 0.05 mm;

[0060] Step S56: Adjust the compensation path of the deformation compensation path according to the cause of the non-target deformation and the conflict set of compensation path changes to obtain an optimized feature compensation method.

[0061] Through the kinematic discretization process of the deformation compensation path, the present invention makes the acquisition of the path control point set operable and accurate. Based on the path control point set, the compensation simulation of the model feature skeleton generates a credible compensation process. The identification of the compensation target area ensures the pertinence of the compensation work, and the analysis of the non-target area provides data support for identifying potential problems. The comparison of the deformation before and after makes the understanding of the non-target deformation deeper. The trace-back of the compensation process provides a basis for determining the initial time point of the non-target deformation. The analysis of the cause of the non-target deformation provides a key clue for optimizing the compensation path. The path adjustment simulation can timely identify the conflicts in the compensation path, and the generated conflict set of compensation path changes lays a foundation for the improvement of the actual compensation plan. The final adjustment of the compensation path realizes the optimization of the feature compensation method, overall improving the adaptability and accuracy of the 3D printing model, and providing comprehensive support and guarantee for improving the quality of the printed product.

[0062] The present invention also provides a 3D printing model slice feature compensation system for performing the 3D printing model slice feature compensation method as described above. The 3D printing model slice feature compensation system includes:

[0063] A model skeleton extraction module for collecting a 3D printing model; splitting the model feature skeleton of the 3D printing model; performing a transverse slice mapping on the model feature skeleton to generate skeleton slice features;

[0064] An environmental stress simulation module, which is used to collect the 3D printing environment; perform virtual environment field simulation according to the 3D printing environment, and conduct environmental stress analysis on the skeleton slice features based on the simulated environment field to generate environmental stress data;

[0065] A deformation scale deduction module, which is used to perform deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; predict the thermal deformation of the skeleton slice features according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data;

[0066] A reverse deformation repair module, which is used to perform reverse deformation simulation based on the thermal deformation data, and analyze the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure; plan the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements;

[0067] A deformation compensation optimization module, which is used to perform compensation simulation on the model feature skeleton according to the deformation compensation path to obtain the simulated compensation process; analyze the non-target deformation in the simulated compensation process, and adjust the compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0068] Through the introduction of the model skeleton extraction module, the present invention can systematically collect and split the 3D printing model, enabling subsequent analysis to have detailed structural information. The skeleton slice features generated by horizontal slice mapping provide operable detailed data for applications. The function of the environmental stress simulation module realizes a comprehensive scan of the 3D printing environment, effectively identifying the impact of environmental factors during the printing process on the model. The virtual environment field simulation can non-destructively judge the performance under construction conditions, providing a reliable basis for environmental stress analysis. The generated environmental stress data provides an important reference for the stability evaluation of the model under different conditions. The correlation deduction performed by the deformation scale deduction module based on the environmental stress data can effectively evaluate the deformation of the model under different conditions. The generation of thermal deformation data provides a scientific basis for design optimization. The introduction of the reverse deformation repair module ensures the effective identification of deformation conditions and the accurate analysis of repair requirements. The planning of the deformation compensation path provides a targeted repair strategy. The deformation compensation optimization module innovatively avoids model defects caused by non-target deformation through compensation simulation. The formation of the optimized feature compensation method provides higher accuracy and reliability for subsequent printing, overall improving the finished product quality and production efficiency of the 3D printing model.

[0069] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed, it implements the 3D printing model slice feature compensation method described in any one of the above.

[0070] The present invention can provide an efficient storage and operation environment for the implementation of the 3D printing model slicing feature compensation method through the introduction of a computer-readable storage medium. The stored computer program ensures the reusability and efficient invocation of the method. The programmed execution process improves the accuracy and consistency of the overall operation, can quickly respond to the processing requirements of different printing models. The optimized computer program enables the rapid processing of complex calculations, supports the analysis and compensation of various 3D printing model features, saving a large amount of time and effort for users. The flexible program architecture allows for convenient future functional expansion and upgrade. The systematic and organized programming logic reduces the risk of human operation errors, ensuring that the model is accurately verified and optimized before printing, improving the quality and stability of the final product, providing strong technical support and guarantee for the popularization and application of 3D printing technology, and overall enhancing the intelligent level and automation degree of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic flowchart of the steps of a 3D printing model slicing feature compensation method;

[0072] Figure 2 It is a schematic flowchart of the detailed implementation steps of step S2;

[0073] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED IMPLEMENTATION MANNER

[0074] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that although terms such as "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0077] To achieve the above object, please refer to Figures 1 to 2 , a 3D printing model slice feature compensation method, comprising the following steps:

[0078] Step S1: Acquire a 3D printing model; disassemble the model feature skeleton of the 3D printing model; perform a transverse slice mapping on the model feature skeleton to generate a skeleton slice feature;

[0079] Step S2: Acquire the 3D printing environment; perform a virtual environment field simulation according to the 3D printing environment, and perform an environmental stress analysis on the skeleton slice feature based on the simulated environment field to generate environmental stress data;

[0080] Step S3: Perform a deformation scale correlation deduction on the skeleton slice feature based on the environmental stress data to obtain a skeleton stress deformation scale; perform a thermal deformation prediction on the skeleton slice feature according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data;

[0081] Step S4: Perform a reverse deformation simulation based on the thermal deformation data, and analyze the deformation repair requirements based on a preset standard printing model and the simulated reverse deformation structure; plan a deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements;

[0082] Step S5: Perform a compensation simulation on the model feature skeleton according to the deformation compensation path to obtain a simulated compensation process; analyze the non-target deformation in the simulated compensation process, and adjust the compensation path of the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0083] The present invention can lay a foundation for subsequent analysis and processing by collecting 3D printing models and splitting out the model feature skeletons. The skeleton slice features generated by the horizontal slicing mapping of the model feature skeletons provide structural detail information. Collecting the 3D printing environment and performing virtual environment field simulation can comprehensively understand the external influencing factors during the printing process. Environmental stress analysis provides key data for the performance evaluation of the skeleton slice features under specific environments. The deformation scale correlation deduction based on the environmental stress data provides theoretical support for understanding the deformation behavior of materials under different stress conditions. Thermal deformation prediction can quantify the thermal influence on the materials during the printing process, and the generated thermal deformation data provides a basis for model optimization. The implementation of reverse deformation simulation can clarify the deviation between the printer settings and the physical model. The comparative analysis of the standard printing model and the simulated reverse deformation structure helps to accurately identify the need for deformation repair. The planning of the deformation compensation path provides a strategic guidance for the repair process. The compensation simulation of the model feature skeleton can be pre-verified before actual printing. Analyzing the non-target deformations during the simulation compensation process provides feedback and adjustment basis for optimizing the compensation scheme, overall improving the accuracy and quality of the 3D printing model, and providing effective technical support for subsequent printing process control and finished product quality assurance.

[0084] In an embodiment of the present invention, the 3D printing model slice feature compensation method includes the following steps:

[0085] Step S1: Collect a 3D printing model; split the model feature skeleton of the 3D printing model; perform horizontal slicing mapping on the model feature skeleton to generate skeleton slice features;

[0086] In this embodiment, for data collection of the 3D printing model, a high-precision optical scanning device is used to obtain the three-dimensional point cloud data of the model. The point cloud data is collected by Structured Light 3D Scanning (structured light 3D scanning) method, and the sampling error of each point is controlled within 0.01 mm. The point cloud data is converted into a polyhedral mesh representation, and a high-precision triangular mesh is generated by using the Poisson Surface Reconstruction algorithm. The mesh density is set to 2000 patches per square millimeter. The model feature skeleton is generated, the model skeleton data is extracted based on the Skeletonization method, and the topological preservation process is performed by using the Thinning Algorithm to obtain the model skeleton topological structure. The horizontal slicing is performed by using the two-dimensional slicing mapping method, the slicing interval is set to 0.05 mm, the boundary curve is extracted from each slice by using the Contour Detection method, and the boundary accuracy is optimized by using the Bézier Curve Fitting to generate the skeleton slice feature data.

[0087] Step S2: Collect the 3D printing environment; conduct virtual environment field simulation according to the 3D printing environment, and perform environmental stress analysis on the skeleton slice features based on the simulated environment field to generate environmental stress data;

[0088] In this embodiment, a high-resolution laser scanner is used to collect data on the 3D printing environment, obtaining parameters such as the printing platform, nozzle temperature, air flow state, and support structure. The nozzle temperature range is set between 180°C and 260°C, and the wind speed detection range is set between 0 m / s and 5 m / s. Computational Fluid Dynamics (CFD) is used to simulate the air flow characteristics, with the boundary condition set as an open flow field and the fluid viscosity set to 0.001 Pa·s. The k- turbulence model is used to solve the air disturbance effect, generating the print environment flow field data. Based on the Finite Element Method (FEM), environmental stress loading is applied to the skeleton slice feature data, and the constraint conditions applied include fixed support constraint, material shrinkage constraint, and temperature gradient constraint. The temperature gradient range is set between 5°C / mm and 20°C / mm. The von Mises Stress criterion is used to calculate the equivalent stress of each slice feature, obtaining the environmental stress data.

[0089] Step S3: Conduct deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; perform thermal deformation prediction on the skeleton slice features according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data;

[0090] In this embodiment, the environmental stress data is used to conduct deformation scale correlation deduction on the skeleton slice feature data. A discrete deformation model is constructed based on the Material Point Method (MPM), with the material yield strength set to 50 MPa, the elastic modulus set to 2500 MPa, and the Poisson's ratio set to 0.35. The anisotropic deformation gradient under stress is calculated, and the Shape Matching method is used to correct the local deformation error, obtaining the skeleton stress deformation scale data. The skeleton stress deformation scale data is coupled with the environmental stress data, and the Thermal-Mechanical Coupling method is used to calculate the thermal stress effect, with the thermal expansion coefficient set to 75×10⁻ 6 / K and the material thermal conductivity set to 0.25 W / (m·K). The Heat Transfer Simulation method is used to calculate the temperature gradient effect of each slice, and the Kelvin-Voigt Model is used to simulate the thermal deformation process, obtaining the thermal deformation data.

[0091] Step S4: Perform reverse deformation simulation based on the thermal deformation data, and analyze the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure; Plan the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements;

[0092] In this embodiment, perform vector reversal processing on the thermal deformation data, use the Inverse Finite Element Method (iFEM) to perform reverse correction on the thermal deformation field, set the deformation compensation threshold to 0.02 mm, construct a reverse deformation matrix, and use the Recursive Mirror Transformation method to iteratively optimize the reverse deformation matrix. Set the convergence threshold to 0.001 mm, and stop the iteration when the maximum residual is less than the threshold to obtain the reverse deformation mapping function. Use the reverse deformation mapping function to fit the preset standard printing model, where the standard printing model refers to the theoretical optimal model deduced according to the design parameters under ideal environmental conditions, that is, the 3D printing target model not affected by environmental stress, thermal deformation, etc., and ensure that the key dimension error is controlled within ±0.05 mm, the surface roughness Ra ≤ 5 μm, the profile accuracy deviation does not exceed 0.1%, and the mechanical property error does not exceed 3%. Calculate the model correction deviation, set the deformation correction upper limit to 0.5 mm, use the Gradient-based Compensation Path Optimization method to optimize the compensation path, discretize the deformation area into two-dimensional or three-dimensional grid cells, and assign different cost values according to the deformation amount, compensation cost, and obstacle distribution of each cell. The lower the cost value of the grid cell, the better the path. Define the starting point and target point positions of the path, define the adjacent grid cells that can be reached when the path moves (such as 4-neighborhood, 8-neighborhood, or 26-neighborhood), expand the adjacent feasible cells layer by layer with the starting point as the center, and preferentially select the neighboring cells with the lowest cost to join the path candidate queue until the target point is reached. If there are multiple equal-cost paths, select the path with the shortest length or the fewest turning times, and trace back from the target point along the direction with the minimum cost to the starting point to form a complete deformation compensation path sequence. While avoiding obstacles and large deformation areas, this path tries to pass through areas with low cost and small deformation amounts to ensure the rationality of compensation and the optimization effect of the path. Smooth the obtained path sequence, remove redundant inflection points, and ensure the continuity of the path and the executability of the compensation motion trajectory to plan the deformation compensation path. Set the path search step size to 0.1 mm to obtain the deformation compensation path.

[0093] Step S5: Perform compensation simulation on the model feature skeleton according to the deformation compensation path to obtain the simulation compensation process; analyze the non-target deformation in the simulation compensation process, and adjust the compensation path of the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0094] In this embodiment, the model feature skeleton is compensated and simulated using the deformation compensation path. The Incremental Deformation Superposition method is used to perform the compensation operation layer by layer, with the compensation step size for each layer set to 0.05 mm. The Adaptive Compensation Filtering method is used to smooth the non-uniform compensation area to obtain the simulation compensation process. Local error analysis is performed on the simulation compensation process, with the non-target deformation detection threshold set to 0.02 mm. The Difference Mapping method is used to calculate the non-target deformation area, and the local compensation parameters are optimized based on the Deformation Constraint Adjustment method. The compensation path of the deformation compensation path is adjusted, and the Nonlinear Compensation Path Refinement method is used to optimize the curvature of the compensation path to generate an optimized feature compensation method.

[0095] Preferably, step S1 includes the following steps:

[0096] Step S11: Collect a 3D printed model; perform high-precision point cloud data collection on the 3D printed model to obtain the original point cloud data;

[0097] Step S12: Perform meshing reconstruction on the original point cloud data and perform smoothing processing to generate a refined mesh structure;

[0098] Step S13: Extract the center line of the refined mesh structure, and extract the model feature skeleton based on the center line;

[0099] Step S14: Perform multi-directional rasterization encoding on the model feature skeleton to obtain the skeleton voxel grid; perform inertial principal axis positioning on the skeleton voxel grid and perform inertial principal axis calibration on the inertial principal axis to obtain the oriented slice coordinate system;

[0100] Step S15: Perform dynamic step size slicing processing on the oriented slice coordinate system to generate the slice cross-sectional profile; extract the feature key points from the slice cross-sectional profile to generate the skeleton slice feature.

[0101] In this embodiment, a structured light scanning device is used to collect data of the 3D printing model. The projection grating frequency is set to 150 Hz. A three-dimensional camera is used to record the fringe distortion conditions at multiple angles. The phase unwrapping algorithm is adopted to calculate the three-dimensional coordinate information of the model surface. The data collection accuracy is controlled within 0.01 mm to generate the original point cloud data. For the highly reflective area, a polarization filter is used to reduce the specular reflection interference. The point cloud data is converted into the coordinate format (x, y, z). The KinectFusion algorithm is used to register multiple frames of point cloud data. The convergence threshold of the ICP (Iterative Closest Point) algorithm is set to 0.0005 mm to improve the point cloud stitching accuracy. The statistical filtering method is used to remove the noise points. The filtering window size is set to 10 neighborhood points, and the abnormal points with a standard deviation exceeding twice the mean value are removed to obtain the optimized original point cloud data. The Delaunay triangulation method is used to perform grid reconstruction on the original point cloud data. Based on the constrained Delaunay triangulation algorithm, a non-uniform triangular mesh is generated. The minimum side length of the mesh is set to 0.05 mm, and the maximum side length is set to 0.2 mm. The Laplacian Smoothing algorithm is used to optimize the mesh surface. The number of smoothing iterations is set to 5 times to adjust the coordinate positions of each vertex to minimize the weighted mean of its neighborhood points. The Bilateral Mesh Denoising method is used to retain the mesh details. The weight function σc = 0.1 and σs = 0.2 are set. For the high-curvature area, the Curvature Flow Smoothing method is used to avoid the loss of mesh geometric features. Finally, a refined grid structure is generated. The centerline of the refined grid structure is extracted by using the method based on principal curvature analysis. The principal curvature values of each vertex are calculated. The curvature threshold is set to 0.15, and the curvature peak points are screened. Based on the Shortest Path Skeletonization method, the centerline is optimized. The shortest path from the skeleton points to the mesh surface is used as the skeleton optimization criterion. The adaptive step size algorithm is used to adjust the skeleton distribution density. The step size range is set from 0.02 mm to 0.1 mm. The Geodesic-based Pruning method is used to remove the redundant skeleton branches. Finally, the model feature skeleton is generated. The model feature skeleton is encoded by multi-directional rasterization. The Octree Decomposition method is used to perform spatial division on the skeleton points. The minimum voxel size of the octree is set to 0.1 mm. The skeleton points are mapped to the 3D voxel grid to generate the skeleton voxel grid. Based on the PCA (Principal Component Analysis) method, the inertial principal axis direction of the skeleton point cloud is calculated. The eigenvalue threshold for covariance matrix decomposition is set to 0.01. To screen the main inertial direction, the Iterative Axis Refinement method is used to calibrate the inertial principal axis. The iteration step size is set to 0.005 mm until the deviation of the inertial principal axis is less than 0.001 mm, generating an oriented slice coordinate system. Based on the oriented slice coordinate system, dynamic step size slicing is performed. The Adaptive Slicing method is adopted to adjust the slice step size according to the local curvature, with the step size range set from 0.02 mm to 0.2 mm. The Marching Squares Algorithm is used to calculate the cross-sectional profile of each slice. Based on the Douglas-Peucker Simplification method, redundant points on the profile are reduced, and the simplification error threshold is set to 0.005 mm. Key points are extracted from the simplified profile, and the Scale-Space Feature Detection method is used to locate the curvature extreme points, with the Gaussian scale parameter σ set to 1.5 mm. Low signal-to-noise ratio points are removed, and finally, the skeleton slice features are generated.

[0102] Particularly important is that the inertial principal axis positioning for the skeleton body pixel grid and the inertial principal axis calibration described in step S14 include:

[0103] Analyze the spatial distribution of the skeleton body pixel grid to obtain the density distribution characteristics;

[0104] Fit the main inertial direction to the density distribution characteristic data to generate an initial inertial principal axis;

[0105] Optimize the spatial alignment of the initial inertial principal axis to obtain an initial oriented coordinate system;

[0106] Conduct a global error assessment on the initial oriented coordinate system and perform local calibration fitting on the initial inertial principal axis based on the global error to obtain an optimized inertial principal axis;

[0107] Perform coordinate system calibration transformation based on the optimized inertial principal axis to obtain an oriented slice coordinate system.

[0108] In this embodiment, when analyzing the spatial distribution of the skeleton pixel grid, it is first necessary to accurately collect the three-dimensional coordinates of each pixel point in the grid. After obtaining the point cloud data set, spatial distribution analysis is performed based on the geometric shape of the grid and the distribution of the point cloud data. First, preliminary partitioning is carried out through the centroid (geometric center) of the grid, and then the local density of the point cloud data in each region is calculated. The density calculation is based on the distance relationship between each grid point and its neighborhood. The voxel grid method is used to spatially partition the point cloud into multiple small voxel blocks. The number of point clouds in each voxel is the density value of that region. By statistically analyzing the density of each voxel block, the spatial density distribution characteristics of the overall grid are obtained. The regions with larger density values correspond to the more concentrated or complex parts of the grid. Through this method, the spatial density characteristics of the grid are obtained, and finally a grid density data set based on spatial distribution is obtained. When fitting the principal inertia direction for the density distribution characteristic data, first calculate the inertia matrix for each grid voxel. The inertia matrix reflects the distribution characteristics of the voxel in three-dimensional space. During the calculation process, the mass of the voxel is regarded as the volume or mass of the point cloud it contains, and the geometric center of the voxel is determined by weighted averaging. The calculation of the inertia matrix involves the coordinates and density of the points within each voxel. The calculated inertia matrix can be decomposed by eigenvalue decomposition to obtain the direction of the principal inertia axis. The direction of the principal inertia axis is determined by the eigenvector corresponding to the largest eigenvalue. Through this method, the initial principal inertia axis direction of the grid is obtained based on the density distribution data. Compare the principal inertia axis obtained by principal inertia direction fitting with the reference coordinate system, and adjust the direction of the principal axis by minimizing the deviation. The iterative optimization method is used during the adjustment process. In each iteration, calculate the angle difference between the initial principal inertia axis and the reference coordinate system, and spatially align the principal inertia axis through the rotation matrix. The calculation of the rotation matrix is based on the rotation angle between the known principal axis direction and the target principal axis direction. The least squares method is used as the optimization target during the calculation process, and the rotation matrix is continuously adjusted until the alignment error reaches the predetermined threshold and the rotation angle error is less than 0.01 degrees, then stop the optimization process. Finally, an initial orientation coordinate system highly aligned with the reference coordinate system is obtained. By comparing the known calibration data with the point cloud data in the current orientation coordinate system, evaluate the global accuracy of the current coordinate system. The error evaluation method includes calculating the Euclidean distance difference between coordinate points and accumulating all error differences to obtain the total error value. During the error evaluation process, the set error threshold is 0.0.05 mm. When the global error exceeds this threshold, it indicates that the accuracy of the initial orientation coordinate system is insufficient, and further local calibration fitting is required. When performing local calibration fitting on the initial inertial principal axis based on the global error, the local weighted least squares method is used to refine the error region. By focusing on adjusting the regions with larger errors and combining the point cloud distribution in the local region, the inertial matrix of the local point cloud is calculated, and targeted adjustments are made based on its eigenvalues and eigenvectors. During the calibration process, the step size of each adjustment is limited to 0.1 mm. The goal of local calibration is to control the error within 0.05 mm. Through multiple iterative optimizations, the local error is gradually reduced until the global error meets the preset requirements, and finally the optimized inertial principal axis is obtained. When performing coordinate system calibration transformation based on the optimized inertial principal axis, by performing a rotation transformation on the direction of the optimized inertial principal axis and using the rotation matrix for axis conversion, the data in the original coordinate system is converted into the new orientation slice coordinate system. During the transformation process, the rotation matrix is first calculated, and the rotation matrix is determined based on the angle difference between the optimized inertial principal axis and the principal axis direction of the original coordinate system. By performing the rotation matrix transformation on each point cloud data, all the point cloud data is finally aligned to the new orientation slice coordinate system. After the coordinate system calibration transformation, the orientation slice coordinate system is finally obtained.

[0109] Preferably, step S2 includes the following steps:

[0110] Step S21: Collect the 3D printing environment; identify the distribution of environmental parameters according to the 3D printing environment; perform interpolation processing on the distribution of environmental parameters and construct a three-dimensional environmental gradient field;

[0111] Step S22: Calculate the hydrodynamic parameters of the three-dimensional environmental gradient field. During the calculation process, the fluid viscosity is set to 1.85×10-5 Pa·s, and the density is set to 1.225 kg / m³; perform virtual environment field simulation on the distribution of environmental parameters based on the hydrodynamic parameters to obtain the simulated environment field;

[0112] Step S23: Perform superposition analysis on the simulated environment field and the skeleton slice features, and construct an interaction influence matrix;

[0113] Step S24: Perform heat flow - material stress transfer simulation based on the interaction influence matrix to generate simulated stress transfer data; determine the environmental stress data based on the simulated stress transfer data.

[0114] In this embodiment, a high-precision environmental sensor array is used to collect data on the 3D printing environment. The environmental sensors used include a temperature sensor, a humidity sensor, an air flow rate sensor, a barometric pressure sensor, and a radiant heat sensor. The distribution interval of each sensor is set to 50 mm, the sensor array covers a range of 1.2 m around the 3D printer, the data collection frequency is set to 1 Hz, and the environmental parameters at different spatial positions are collected. The Kriging Interpolation method is used to interpolate the environmental parameters to construct a spatially continuous distribution model to improve the spatial resolution of the data. The semi-variogram for interpolation calculation is set to an exponential model, and the Laplace equation is used. ; Calculate the three-dimensional temperature gradient field. The boundary conditions are set such that the temperature of the 3D printer workbench remains at 90 °C and the ambient temperature is 25 °C. The finite element method is used to numerically solve the trend of environmental temperature change to obtain the temperature distribution curve. For the air flow parameters, the Navier-Stokes equation is used to calculate the flow velocity distribution. Among them, the Navier-Stokes equation is: , where represents the flow velocity vector, is the pressure, is the fluid density, is the kinematic viscosity, is the external force term, is the time, represents the flow velocity represents the rate of change of the flow velocity with time, describing the dynamic change of the fluid over time. The fluid viscosity is set to 1.85×10 -5 Pa·s, and the density is set to 1.225 kg / m³. By calculating the velocity gradient and the pressure gradient in the air flow field, an air flow gradient field is constructed. Finally, by combining the temperature gradient, humidity gradient, air flow gradient, and pressure gradient data, a three-dimensional environmental gradient field is generated using a three-dimensional interpolation algorithm. Based on the constructed three-dimensional environmental gradient field, hydrodynamic parameters are calculated. The large eddy simulation (LES) method is used to solve the eddy structure in the flow field. The mesh division uses unstructured tetrahedral meshes, and the mesh size is set to 2 mm. The fluid time step is set to 0.001 s, and the Reynolds number of the air flow is calculated: , where the fluid density is 1.225 kg / m³, the velocity is set to 0.5 m / s, the characteristic length is set to 20 mm, and the dynamic viscosity is set to 1.85×10 −5 Pa·s. To solve the turbulent motion in the flow field, the k- The turbulence model calculates the turbulence energy k and the turbulence dissipation rate , sets the turbulence Prandtl number , combines with the thermal radiation model to analyze the heat transfer process in the high-temperature area, uses the Discrete Ordinates Method (DOM) to calculate the thermal radiation transfer, and the radiation divergence equation is set as , where is the absorption coefficient, is the radiation flux, is the blackbody radiation intensity, is the net transfer rate of radiation energy per unit time and per unit area. Combining the flow field, temperature field and thermal radiation field data, a virtual environment field simulation is carried out on the environmental parameter distribution to obtain a simulated environment field. Based on the simulated environment field, a superposition analysis of the skeleton slice characteristics is carried out. The Eulerian Method is used to calculate the influence of the simulated environment field on the skeleton slice. The skeleton slice characteristics are discretized in space, and the size of each slice grid unit is set to 1 mm. The temperature gradient ∇T, stress gradient ∇σ and air flow gradient ∇u in each grid unit are calculated. A matrix transformation method is used to construct an interaction influence matrix, and the matrix element represents the influence quantity of the th skeleton slice characteristic point on the -dimensional parameter of the simulated environment field. The calculation method is: ; where is the physical state parameter of the skeleton slice characteristic, including displacement, stress, and deformation amount, is the simulated environment field parameter, including temperature, flow rate, humidity, etc. The Eigenvalue Decomposition (EVD) method is used to perform eigenanalysis on the interaction influence matrix, extract the main influencing factors, and generate an interaction influence matrix. Based on the interaction influence matrix, a heat flow - material stress transfer simulation is carried out. Using the Fourier heat conduction equation calculates the temperature transfer inside the material. The material density is set to 1.4 g / cm³, the specific heat capacity is set to 900 J / (kg·K), and the thermal conductivity is set to 0.25 W / (m·K), is time. The finite element method is used for numerical solution to obtain the heat transfer path inside the model. Using Newton's cooling law calculates the surface heat exchange, where the heat transfer coefficient is set to 10 W / (m²·K), and the surface area is set to 0.02 m². Based on the simulated heat transfer data, the finite element analysis method is used to calculate the material stress distribution. Using Hooke's law Calculate the elastic stress, where the elastic modulus E is set to 3 GPa, and the strain is calculated from the material deformation, combined with the Von Mises criterion , where is an equivalent uniaxial stress value such that the material deformation under a multiaxial stress state is equivalent to that under a uniaxial tensile state, , and respectively represent the normal stress acting in the x-axis direction, the normal stress acting in the y-axis direction, and the normal stress acting in the z-axis direction. Calculate the maximum equivalent stress, adjust the calculation area using the boundary condition control method, set the boundary constraint as a fixed support, and finally calculate the environmental stress data based on the simulated stress transfer data.

[0115] Preferably, the deformation scale correlation deduction of the skeleton slice features based on the environmental stress data in step S3 includes:

[0116] Conduct a stress contact simulation on the environmental stress data and the skeleton slice features, and generate an initial deformation tensor field based on the simulated contact data;

[0117] Fit the micro-layer thickness uniformity distribution of the skeleton slice features to obtain the layer thickness stability data;

[0118] Conduct a heat transfer response simulation on the layer thickness stability data, and analyze the thermal inertia of the skeleton material based on the simulated heat transfer response data;

[0119] Match the material thermal inertia data with the material characteristics according to the preset material database to obtain the model material data;

[0120] Endow the initial deformation tensor field with material property constraints based on the model material data, and determine the skeleton deformation amplitude;

[0121] Identify the deformation critical point according to the skeleton deformation amplitude, and divide the skeleton deformation sensitivity based on the deformation critical point;

[0122] Correlate the environmental stress data based on the skeleton deformation sensitivity to obtain the skeleton stress deformation scale.

[0123] In this embodiment, stress contact simulation is performed on environmental stress data and the characteristics of the skeleton slices, and elastic-plastic tensor mapping is carried out based on the simulated contact data to generate an initial deformation tensor field. The finite element method (FEM) is used to discretize the environmental stress data, decompose it into multiple micro-elements, and apply material physical property parameters to enable each micro-element to possess the elastic-plastic response characteristics of real materials. The skeleton slice feature data is imported into the stress contact calculation model, and numerical fitting is performed on the node stress distribution on the surface of the skeleton. By setting different contact pressure levels, the contact deformation amounts of the skeleton slice features under different stress actions are calculated, a stress contact relationship matrix is established, and singular value decomposition (SVD) is performed on the stress contact relationship matrix to extract the principal stress components. The stress-induced elastic-plastic deformation tensor is calculated through the stress-strain relationship and extended to the entire skeleton slice area using the tensor interpolation method to obtain the initial deformation tensor field. The micro-layer thickness uniformity distribution of the skeleton slice features is fitted to obtain layer thickness stability data. The micro-layer thickness of the skeleton slice is measured, and the surface of the slice is micro-scanned using an optical coherence tomography (OCT) instrument to obtain layer thickness distribution data. The layer thickness distribution model is reconstructed through a three-dimensional reconstruction algorithm, and the layer thickness data is compared and analyzed with the theoretical uniform layer thickness standard to calculate the layer thickness deviation distribution. The layer thickness distribution is surface-fitted using the least mean square (LMS) optimization method to reduce the influence of measurement errors on the fitting accuracy. The layer thickness stability index of each skeleton slice unit is calculated using the deviation distribution surface, and a layer thickness stability data matrix is generated. Thermal transport response simulation is performed on the layer thickness stability data, and the thermal inertia of the skeleton material is analyzed based on the simulated thermal transport response data. The thermal finite element analysis method is used to perform thermal transport modeling on the layer thickness stability data, and thermal physical property parameters such as the thermal conductivity, specific heat capacity, and density of the material are set. Based on the heat conduction equation, the heat flux is calculated for the skeleton slice area. Combining the time-stepping method, the temperature evolution process of the skeleton material is solved, and the thermal inertia parameters of each region are calculated, including the combined parameters of density, specific heat capacity, thermal conductivity, and characteristic size. According to the preset material database, material feature matching is performed on the material thermal inertia data to obtain model material data. Using the set of thermal physical property parameters in the material database, including characteristic parameters such as density, thermal conductivity, and specific heat capacity, material matching is performed through a data similarity calculation method. An error threshold range for material matching is set, and a numerical calculation method is used to calculate the similarity between the thermal inertia data matrix and the sample data in the material database. The material parameters with the smallest error are selected as the model material data, and the matched material physical property parameters are recorded. Based on the model material data, material property constraints are assigned to the initial deformation tensor field.Determine the amplitude of the skeleton deformation, use the parameters such as elastic modulus, yield stress, and Poisson's ratio obtained by matching the material database to impose material property constraints on the initial deformation tensor field, calculate the deformation amplitude through the stress-strain relationship, solve the skeleton deformation amplitude based on the initial deformation tensor field of the skeleton slice and in combination with material properties, record the deformation amplitude data, identify the deformation critical point according to the skeleton deformation amplitude, and based on the deformation critical point, divide the sensitivity of the skeleton deformation. Conduct local gradient analysis on the deformation amplitude data, calculate the deformation gradient distribution, and extract the extreme points of the deformation gradient. Set the critical value of the deformation gradient, screen the regions with sudden changes in the deformation gradient as the deformation critical points, partition the skeleton slice region based on the deformation critical points, classify the high deformation gradient regions as highly deformation-sensitive regions, and classify the low deformation gradient regions as low deformation-sensitive regions. Establish a matrix of the sensitivity of the skeleton deformation, perform corresponding association on the environmental stress data based on the sensitivity of the skeleton deformation to obtain the scale of the skeleton stress deformation, perform a one-to-one mapping between the matrix of the sensitivity of the skeleton deformation and the matrix of the environmental stress data, use the interpolation method to fit and calculate the environmental stress data in different deformation-sensitive regions, calculate the scale of the skeleton stress deformation, and finally output the data matrix of the scale of the skeleton stress deformation.,

[0124] Preferably, the thermal deformation prediction of the skeleton slice features according to the scale of the skeleton stress deformation and the environmental stress data in step S3 includes:

[0125] Conduct a simulation of the attenuation of heat conduction based on the environmental stress data to obtain the data of the temperature gradient change;

[0126] Perform a heat conduction response mapping on the scale of the skeleton stress deformation based on the data of the temperature gradient change and construct a matrix of the thermal stress distribution;

[0127] Conduct a fitting of the thermal field hysteresis effect according to the matrix of the thermal stress distribution and perform hysteresis compensation to generate the data of the thermal inertia compensation;

[0128] Conduct an analysis of the interlayer thermal expansion based on the data of the thermal inertia compensation to obtain the data of the interlayer expansion;

[0129] Predict the deformation trajectory of the skeleton slice features according to the data of the interlayer expansion to generate the thermal deformation data.

[0130] In this embodiment, for the simulation of the thermal conduction attenuation of the skeleton slice, first, environmental stress data is obtained and mapped to the finite element mesh structure. Assuming that the size of the slice area is 100mm×100mm×5mm, this area is divided into cubic mesh elements with a size of 1mm×1mm×0.5mm, totaling 50,000 elements. The material parameters of each element include the thermal conductivity λ = 15W / (m·K), the specific heat capacity Cp = 500J / (kg·K), and the density ρ = 7800kg / m³. Through Fourier's law of heat conduction, the time-stepping calculation of the temperature change of each element is carried out. The initial temperature T0 = 25°C is set, and a heat source of 300°C is applied to the surface of the element during the simulation. The temperature field distribution at different time steps Δt = 0.01s is calculated. The implicit finite difference method is used to solve the temperature conduction equation, the temperature values of each element at different times are compared, and the attenuation trend of the temperature with time is calculated. Finally, a temperature gradient change data matrix T(i, j, k, t) is formed, where i, j, and k represent the spatial indices of the mesh elements, and t represents the time step. Using the aforementioned temperature gradient change data matrix T(i, j, k, t), combined with the thermal expansion coefficient α = 1.2×10⁻ 5 K⁻¹ of the material, the thermal expansion amount ΔL = α·ΔT·L0 of each mesh element is calculated, where ΔT is the temperature change amount of the element and L0 is the initial length. Thus, the thermal stress σ = E·α·ΔT of the mesh element is solved, where E = 200GPa is the elastic modulus of the material. By calculating the thermal stress of all elements and storing it in the thermal stress distribution matrix σ(i, j, k), the bilinear interpolation method is used to smooth this matrix to eliminate calculation errors. Further, based on the analysis of the stress concentration area, the elements with a stress change rate greater than the set threshold are screened out to construct the thermally stressed dominant area, and the thermal stress data of all key meshes are recorded for subsequent calculation of the thermal field hysteresis effect. Based on the thermal stress distribution matrix σ(i, j, k), the hysteresis time τ(i, j, k) of the thermal stress response to the temperature change is calculated. The time correlation analysis method is used to perform cross-correlation analysis on the temperature change data T(i, j, k, t) and the stress data σ(i, j, k, t) to extract the thermal inertia parameters, including the specific heat capacity Cp = 500J / (kg·K), the thermal diffusivity D = λ / (ρ·Cp) = 3.85×10⁻ 6 m² / s, and the thermal conduction time constant τc = L² / D, where L is the element size. According to the calculated τ c = 0.26s, combined with the actual test data, the exponential fitting method is used to construct the thermal inertia hysteresis curve and compensate for the hysteresis effect. The time-reversal interpolation method is used to correct the hysteresis stress response to make it synchronized with the temperature change. Finally, a thermal inertia compensation data matrix τ adj(i,j,k), using the thermo-inertia compensated data τadj(i,j,k), combined with the temperature change T(i,j,k,t) and the coefficient of thermal expansion of the material α = 1.2×10⁻ 5 K⁻¹, calculate the interlayer expansion ΔL(i,j,k) of each unit as ΔL(i,j,k)=α·T(i,j,k,t)·L0, where L0 is the initial layer thickness of 0.1 mm. Optimize the interlayer expansion data by the surface fitting method to eliminate local calculation errors. Use the quadratic interpolation method to construct the interlayer expansion data matrix ΔL(i,j,k). Finally, record the interlayer expansion trend at different time steps. Based on the interlayer expansion data matrix ΔL(i,j,k), perform a deformation simulation on the geometric shape of the skeleton slice. Use the Non-Uniform Rational B-Splines (NURBS) method to establish a surface model. Use the control point deformation analysis method to calculate the overall deformation trend of the skeleton slice. Use the mesh deformation technology to perform Lagrangian interpolation calculation on the deformation vector, and record the trajectory data of the skeleton deformation. Finally, generate the thermal deformation data matrix ΔD(i,j,k).

[0131] Preferably, the reverse deformation simulation based on the thermal deformation data and the analysis of the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure in step S4 include:

[0132] Perform a vector reversal process on the thermal deformation data and construct a reverse deformation matrix;

[0133] Perform a recursive mirror transformation on the reverse deformation matrix until the transformation error of the reverse deformation matrix is lower than the preset error threshold to generate reverse deformation mapping data;

[0134] Perform a reverse projection on the thermal deformation data through the reverse deformation mapping data and construct a simulated reverse deformation structure based on the reverse projection data;

[0135] Perform a structural differential comparison between the preset standard printing model and the simulated reverse deformation structure to obtain structural difference data;

[0136] Analyze the deformation repair requirements based on the structural difference data to obtain the deformation repair requirements.

[0137] In this embodiment, by extracting data such as the position information, temperature change, and time series of each point in the original thermal deformation data, a three-dimensional coordinate system of thermal deformation is constructed. These data are standardized to obtain the displacement vectors of each point. Using this displacement vector information, vector reversal processing is performed. The process of vector reversal involves obtaining a preliminary model of reverse deformation by reversing the sign and correcting the direction of the displacement vector. The construction of the reverse deformation matrix is to further transform these processed data into matrix form. Specifically, by allocating the inverse deformation displacement vectors of each deformation point to the corresponding matrix elements, a multi-dimensional reverse deformation matrix is constructed. Each row of the matrix represents the reverse displacement of a certain coordinate point in each coordinate axis direction, and each column of the matrix represents the deformation amounts of each point in different directions, thus forming the preliminary structure of the reverse deformation matrix. The initial reverse deformation matrix is determined and an error threshold is set. The operation of mirror transformation is to reverse the data in the matrix according to the axis of symmetry, perform reverse projection on the data, and recalculate the deformation result. After each mirror transformation, the error value after the current transformation is calculated through regression analysis and compared with the preset error threshold. If the error value is greater than the threshold, the next round of recursive mirror transformation is continued until the error value is less than the threshold. The error threshold can be set according to actual needs, for example, 0.0.1 mm. After multiple rounds of transformation, an accurate reverse deformation mapping data is gradually obtained. During each round of mirror transformation, the content of the reverse deformation matrix will gradually approach the standard reverse deformation data, and the error will gradually decrease until the preset error standard is reached. Through this recursive transformation method, the reverse deformation mapping data gradually approaches the actual standard deformation situation. After obtaining the standard reverse deformation data from the reverse deformation mapping data, when performing inverse projection on the thermal deformation data, first map the deformation data and the thermal deformation data in three-dimensional space, and project the reverse deformation mapping data back to the original thermal deformation data space. Through this process, based on the reverse deformation matrix, the reverse structure of the thermal deformation is deduced, and these reverse structure points are mapped into a new three-dimensional coordinate system according to the original geometric form. With the help of the inverse projection method, a simulated reverse deformation structure is constructed. In this structure, the position and deformation degree of each point are accurately adjusted according to the projection data. The construction of the simulated reverse deformation structure not only involves the adjustment of the position of points, but also needs to deal with the mutual relationship between points to ensure that the thermal deformation compensation of each area is coherent and avoid local incoordination. When performing differential comparison on the preset standard printing model and the simulated reverse deformation structure, first compare each point according to the geometric data of the simulated reverse deformation structure and the standard printing model to calculate the geometric difference data between the two. The calculation method of the difference data is to calculate the difference of the spatial coordinates of each corresponding point to obtain the difference in each axial direction. The difference data is not only the position difference, but also includes the deformation degree and various dimensional errors. After precise calculation, the structural difference data is obtained. Special attention should be paid to the accuracy of the calculation of the difference data. The error calculation must be carried out at the micron level to ensure that each subtle geometric deviation can be reflected. When analyzing the deformation repair requirements based on the structural difference data, first, generate the repair priority of each point based on the structural difference data. The evaluation process of the repair requirements includes weighting the difference data and giving priority to processing the areas with larger errors. Using the known compensation algorithm, through calculation, the thermal deformation compensation areas are designed and deeply analyzed to determine the repair parameters of each point. The repair parameters include the magnitude, direction, and type of compensation, etc. Based on this analysis result, specific deformation repair requirements are generated.

[0138] Preferably, the planning of the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements in step S4 includes:

[0139] Determine the required deformation area according to the deformation repair requirements;

[0140] Match the skeleton stress deformation scale corresponding to the area based on the required deformation area;

[0141] Extract the required deformation amplitude of the deformation repair requirements;

[0142] Perform demand stress conversion on the skeleton stress deformation scale corresponding to the region based on the demand deformation amplitude to obtain the deformed demand stress;

[0143] Deduce the compensation path based on the deformed demand stress and the demand deformation region to generate the deformation compensation path.

[0144] In this embodiment, the thermal deformation data and the structural difference data are analyzed, and the deformed area is located in combination with the deformation repair requirements. The determination of the deformed area depends on the precise division of the model geometry, and the area with a large error value is calculated according to the difference data. The area where the error value is greater than the preset threshold is defined as the required deformation area, and the error threshold is set to 0.02 mm. Further, through the geometric comparison between the thermal deformation data and the printed model, the displacement of each point is evaluated using a three-dimensional space coordinate system, and the geometric boundaries of the deformed area are calculated. These boundaries include parameters such as the maximum length, width, and depth of the deformation range. The deformed area should consider the uniformity of the overall model structure, exclude minor deformations in small areas, and ensure that the accuracy and range of the repair area meet the actual repair requirements. During this process, numerical modeling tools can be used to divide the model in space, accurately extract the deformation data, and locate the target area. When matching the skeleton stress deformation scale corresponding to the required deformation area, first, the skeleton information of the deformed area in the model is extracted. The skeleton information is obtained by three-dimensional reconstruction of the detailed structure of the 3D printed model, and the stress state of each point on the skeleton under external forces is obtained. The skeleton stress deformation scale reflects the stress distribution in the deformed area under the stress state. By analyzing the stress data of the skeleton nodes, several points with the maximum stress values in the deformed area are extracted. Subsequently, these stress points are corresponded to the geometric positions of the required deformation area to determine the skeleton stress deformation scale of this area. The matching process is achieved through stress calculation and regional geometric analysis. The skeleton stress deformation scale will be expressed as the stress value per unit volume. For example, in units of MPa. After matching, the skeleton stress deformation scale of the required deformation area is obtained. The confirmation of the required deformation amplitude is achieved by analyzing the geometric error of the required deformation area. First, the three-dimensional geometric error of the deformed area is quantified. The specific steps are to use the error calculation method to calculate the error amplitude of each point in the deformed area. For example, the error of a certain deformed point is 0.02 mm, which indicates that this point needs to be repaired with a deformation of 0.02 mm. By analyzing the error data of all error points, the required deformation amplitude is determined. The required deformation amplitude is determined based on the reference value of the maximum error. The specific calculation method is to select the point with the largest error value in the deformed area as the reference value of the required deformation amplitude and apply this reference value to the entire area. The deformation amplitude is related to the number of points and the error distribution in the deformed area. According to the distribution of the error values, the overall repair magnitude of the given deformed area is determined, and the deformation amplitude required for this area is calculated. After determining the required deformation amplitude, when performing stress conversion on the required deformation amplitude, first, according to the matching relationship between the required deformation amplitude and the skeleton stress deformation scale, the stress-deformation relationship formula in material mechanics is used for stress conversion. Assume that in a specific deformed area, the required deformation amplitude is 0.If the deformation scale of the skeleton stress in this area is 30 MPa when the deformation amount is 0.2 mm, then through the stress conversion formula, the required stress for deformation in this area is obtained as 15 MPa. The conversion process depends on the proportional relationship between stress and deformation amplitude, and this proportion is obtained by analyzing the physical properties of the material and ensuring that the conversion process complies with material characteristics and structural safety standards. When deducing the compensation path based on the required stress for deformation and the required deformation area, first, combining the required stress and the geometric characteristics of the deformation area, calculate the repair path required for each deformation point. The core of deducing the compensation path is to determine how to uniformly repair the entire deformation area through the compensation process. The deduction process uses a numerical optimization algorithm to convert the required stress distribution into the specific route of the compensation path. By simulating the mechanical state within the deformation area and adopting a path planning algorithm, gradually generate the optimal compensation path. The generation of the compensation path also needs to consider factors such as the fluidity and adhesiveness of the material and the capabilities of the printing equipment. Finally, through these parameters and path planning, generate a compensation path that can minimize the deformation error to the greatest extent. The compensation path specifically includes the compensation amount, direction, and compensation order for each point, and finally forms a path that can guide the compensation operation in the actual printing process.

[0145] Particularly importantly, the deduction of the compensation path based on the required stress for deformation and the required deformation area includes:

[0146] Perform stress field tensor decomposition on the required stress for deformation to obtain multi-dimensional stress components;

[0147] Perform grid discretization mapping on the required deformation area to generate regional grid nodes;

[0148] Superimpose and fuse the multi-dimensional stress components and the regional grid nodes to obtain node stress distribution data;

[0149] Construct a gradient field based on the node stress distribution data, and perform isosurface slicing based on the gradient field to generate stress level data;

[0150] Construct a transfer path based on the stress level data, and perform deviation compensation correction based on the transfer path and the stress level data to generate a deformation compensation path.

[0151] In this embodiment, when performing stress field tensor decomposition on the deformed demand stress, first measure the force condition of the target 3D printing model, select a method based on Finite Element Analysis (FEA) to simulate the mechanical response of the model. After setting boundary conditions, material parameters, and loading methods, use a finite element solver to calculate the stress distribution of the model under different force conditions to obtain stress tensor data. The stress tensor data includes six independent components, which respectively represent the normal stress and shear stress of the model in the three principal axis directions. To more intuitively analyze the stress distribution characteristics, perform eigenvalue decomposition on the stress tensor, calculate the eigenvalues and eigenvectors of the tensor. The eigenvalues represent the main stress intensity inside the model, and the eigenvectors are used to describe the principal stress directions in each direction. Use a numerical iterative method, such as QR decomposition, to perform decomposition operations on the stress tensor, extract the principal stress components and shear stress components in each direction inside the model, and finally obtain a set of multi-dimensional stress component data. When performing grid discretization mapping on the demand deformation area, first determine the areas in the 3D printing model that may undergo deformation, divide these areas into multiple discrete small cells, and generate a grid structure through the Delaunay Triangulation method to ensure that the shape of the grid cells is regular and meets the calculation accuracy requirements. During the grid division process, adaptively adjust the grid density of different areas, use smaller grid cells in high-stress areas to improve the calculation accuracy. The coordinate information of the grid nodes is stored in a three-dimensional coordinate storage format, and each node records its spatial position (x, y, z). At the same time, establish a topological relationship table to record the connection relationships between the nodes. Use an octree data structure to store and manage the grid nodes to improve the subsequent calculation efficiency. After completing the grid processing, obtain a regional grid node data set containing spatial coordinates, topological relationships, and preliminary stress information. When superimposing and fusing the multi-dimensional stress components and regional grid nodes, map the multi-dimensional stress components obtained in the previous step to the corresponding grid nodes, assign the corresponding stress data to each grid node, use a weighted interpolation method to calculate the final stress value of the grid node. The weight of the weighted interpolation is determined by the distance from the grid node to the surrounding force points, and use the trilinear interpolation method for numerical calculation to ensure the smooth transition of the stress data on the grid. After the stress mapping is completed, store the data for each grid node, including node coordinates, local stress values, and their direction information, and finally form the node stress distribution data. When constructing the gradient field based on the node stress distribution data, first calculate the stress gradient between the grid nodes. The gradient calculation is based on the change rate of the stress value in space, and use the central difference method to calculate the gradient values in each direction inside the grid.Each grid cell calculates the gradient components in three principal directions and stores the gradient vector data. Subsequently, based on the calculated gradient field data, the Marching Cubes algorithm is used for isosurface extraction to divide the stress distribution data into different stress level regions. During the isosurface slicing process, different stress thresholds are set, and each threshold corresponds to a stress level. The stress distribution inside the model is divided into multiple continuous regions, and stress level data is generated. When constructing the transfer path based on the stress level data, first, the boundary lines of each stress level are extracted, and the graph theory method is used to optimize the paths of the boundary lines. The paths in the high-stress regions are connected to the paths in the low-stress regions. The shortest path algorithm (such as the Dijkstra algorithm) is used to determine the transfer paths between each level, and at the same time, the directionality of the stress gradient is considered to ensure a smooth transition of the paths. After the path construction is completed, deviation compensation correction is performed according to the stress level data. The local deformation deviation of the model is calculated, and a deformation compensation algorithm is used for correction. During the correction process, the current transfer path is compared with the theoretical stress distribution, the compensation vector is calculated, and deformation adjustment is performed along the transfer path. The calculation accuracy of the deformation compensation adjustment is set to 0.02 mm, and finally, the deformation compensation path is obtained.

[0152] Preferably, step S5 includes the following steps:

[0153] Step S51: Discretize the deformation compensation path to obtain a set of path control points; perform deformation compensation simulation on the model feature skeleton based on the set of path control points to generate a simulated compensation process;

[0154] Step S52: Identify the deformation compensation target region during the compensation process; analyze the non-target regions during the compensation process based on the deformation compensation target region;

[0155] Step S53: Compare the deformations before and after compensation of the non-target regions according to the model feature skeleton to obtain the non-target deformations;

[0156] Step S54: Trace back the compensation process based on the non-target deformations, and determine the initial time point of the non-target deformations based on the traced-back compensation process; analyze the causes of the non-target deformations according to the initial time point of the non-target deformations;

[0157] Step S55: Perform path adjustment simulation on the deformation compensation path based on the causes of the non-target deformations, and perform conflict detection on the simulated adjusted compensation path to generate a conflict set of compensation path changes. Among them, the step size of the path adjustment simulation is limited to 0.5 mm, and the standard for conflict detection is that the part where the compensation paths are no more than 2 mm apart is regarded as the conflict area. The generation basis of the conflict set is that the cumulative path error value is greater than 0.05 mm;

[0158] Step S56: Adjust the deformation compensation path according to the non-target deformation inducement and the conflict set of compensation path changes, so as to obtain an optimized feature compensation method.

[0159] In this embodiment, when performing kinematic discretization processing on the deformation compensation path, the model path needs to be cut first. During the processing, the compensation path is first decomposed into multiple small segments. The length of each small segment is controlled by a set kinematic step size. In this embodiment, the kinematic step size is set to 0.1 mm to ensure that the distance between each path control point is controlled within a precise range. Then, the joint angle change in each path segment is calculated to generate a set of path control points. The coordinates of the path control points and their transformation matrix are used to form a discrete coordinate system for each control point, and finally a complete set of path control points is obtained. Subsequently, deformation compensation simulation of the model feature skeleton is performed based on these control points. During the simulation process, the finite element method (Finite Element Method) is used. The simulation software of the FEM (Functional Element Method) is used to analyze the stress and deformation of the model. The time step of the simulation compensation process is set to 0.01 seconds. The deformation of the skeleton is updated in each step of the calculation to generate the simulation results of the entire compensation process. The accuracy of the compensation simulation process is strictly controlled within 0.01 mm. When identifying the target area of deformation compensation in the compensation process, the deformation threshold judgment method is first adopted based on the deformation data in the compensation model. The identification standard of the target area is the area with a deformation value exceeding 0.5 mm. When the model is compensated, if the deformation amplitude is greater than this value, the area is considered to be the target area of compensation. Through the deformation data of the area, it can be determined which parts need to be compensated. At the same time, it is necessary to identify the target areas in the compensation process. Non-target areas are defined as areas with deformation amplitudes less than 0.5 mm. These areas are not subject to compensation and must be processed separately from the target areas. When comparing the deformation of non-target areas before and after compensation, the deformation data before and after compensation are first compared based on the model's characteristic skeleton. The deformation data includes the displacement of each control point. When performing this comparison, a precision control standard is applied, with a deformation amplitude error tolerance of 0.02 mm. Only errors greater than this value are considered valid deformation differences, resulting in non-target deformation data. When backtracking the compensation process based on non-target deformation, the time range for backtracking must be determined. The backtracking period is limited to the first 200 steps of the compensation process, and the backtracking error tolerance is 0.0.2 seconds to ensure the timeliness of the compensation path. When determining the initial time point of non-target deformation, the deformation error backtracking method is adopted. Through reverse calculation, the occurrence time of non-target deformation is determined, and combined with the time step of the deformation process, the initial time point of backtracking is finally determined. When further analyzing the cause of non-target deformation, based on the known deformation data and combined with external factors such as temperature change and printing material properties, the analysis of the cause of deformation is carried out. A calculation method suitable for the physical characteristics of the model is used to infer the cause of non-target deformation. When simulating the path adjustment of the compensation path based on the cause of non-target deformation, first, the compensation path is simulated segment by segment, and the path is adjusted according to the cause of non-target deformation. The step size of the path adjustment simulation is limited to 0.5 mm to ensure the accuracy of the path adjustment. The adjusted path is subjected to collision detection. During the collision detection process, by detecting the relative error between the compensation paths, if the distance between two paths is less than 2 mm, it is regarded as a collision. The generation standard of the collision set is that the cumulative value of the error between the paths is greater than 0.05 mm, and the collision area will be included in the priority processing area of the subsequent path adjustment. When optimizing the deformation compensation path, the path is adjusted according to the cause of non-target deformation and the collision set of the compensation path change. The goal of the path adjustment is to make the compensation path avoid the collision area and minimize the impact of non-target deformation. During the optimization of the path adjustment, the adjustment range of the path is set within 1 mm to ensure the accuracy of the adjusted path. The finally generated optimized feature compensation method is based on the adjusted compensation path for actual printing, and finally realizes the optimization of the entire compensation path.

[0160] The present invention also provides a 3D printing model slice feature compensation system for executing the 3D printing model slice feature compensation method as described above. The 3D printing model slice feature compensation system includes:

[0161] A model skeleton extraction module, configured to collect a 3D printing model; split the model feature skeleton of the 3D printing model; perform a horizontal slice mapping on the model feature skeleton to generate a skeleton slice feature;

[0162] An environmental stress simulation module, configured to collect a 3D printing environment; perform a virtual environment field simulation according to the 3D printing environment, and perform an environmental stress analysis on the skeleton slice feature based on the simulated environment field to generate environmental stress data;

[0163] A deformation scale deduction module, configured to perform a deformation scale correlation deduction on the skeleton slice feature based on the environmental stress data to obtain a skeleton stress deformation scale; perform a thermal deformation prediction on the skeleton slice feature according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data;

[0164] The reverse deformation repair module is used to perform reverse deformation simulation based on thermal deformation data, analyze the deformation repair requirements based on a preset standard printed model and the simulated reverse deformation structure; plan the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements;

[0165] The deformation compensation optimization module is used to perform compensation simulation on the model feature skeleton according to the deformation compensation path to obtain the simulated compensation process; analyze the non-target deformation in the simulated compensation process, and adjust the compensation path of the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0166] Through the introduction of the model skeleton extraction module, the present invention can systematically collect and split 3D printing models, enabling subsequent analysis to have detailed structural information. The skeleton slice features generated by horizontal slice mapping provide operable detailed data for applications. The function of the environmental stress simulation module realizes a comprehensive scan of the 3D printing environment, effectively identifying the impact of environmental factors on the model during the printing process. The virtual environment field simulation can non-destructively judge the performance under construction conditions, providing a reliable basis for environmental stress analysis. The generated environmental stress data provides an important reference for the stability evaluation of the model under different conditions. The correlation deduction based on the environmental stress data by the deformation scale deduction module can effectively evaluate the deformation of the model under different conditions. The generation of thermal deformation data provides a scientific basis for design optimization. The introduction of the reverse deformation repair module ensures the effective identification of deformation conditions and the accurate analysis of repair requirements. The planning of the deformation compensation path provides a targeted repair strategy. The deformation compensation optimization module innovatively avoids model defects caused by non-target deformation through compensation simulation. The formation of the optimized feature compensation method provides higher accuracy and reliability for subsequent printing, overall improving the finished product quality and production efficiency of 3D printing models.

[0167] The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed, it implements the 3D printing model slice feature compensation method described in any one of the above.

[0168] The present invention can provide an efficient storage and operation environment for the implementation of the 3D printing model slicing feature compensation method through the introduction of a computer-readable storage medium. The stored computer program ensures the reusability and efficient invocation of the method. The programmed execution process improves the accuracy and consistency of the overall operation, can quickly respond to the processing requirements of different printing models. The optimized computer program enables the rapid processing of complex calculations, supports the analysis and compensation of various 3D printing model features, saving a great deal of time and effort for users. The flexible program architecture allows for convenient future functional expansion and upgrade. The systematic and well-organized programming logic reduces the risk of human operation errors, ensures that the model is accurately verified and optimized before printing, improves the quality and stability of the final product, provides strong technical support and guarantee for the popularization and application of 3D printing technology, and overall enhances the intelligent level and automation degree of the production process.

[0169] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0170] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for compensating slice features of a 3D printed model, characterized in that, Including the following steps: Step S1: Collect a 3D printing model; split the model feature skeleton of the 3D printing model; perform a horizontal slicing mapping on the model feature skeleton to generate skeleton slice features; Step S2: Collect the 3D printing environment; perform virtual environment field simulation according to the 3D printing environment, and perform environmental stress analysis on the skeleton slice features based on the simulated environment field to generate environmental stress data; Step S3: Perform deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; Predict the thermal deformation of the skeleton slice features according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; Step S4: Perform reverse deformation simulation based on the thermal deformation data, and analyze the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure; Plan the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirements; among them, performing reverse deformation simulation based on the thermal deformation data, and analyzing the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure includes: Perform vector reverse processing on the thermal deformation data and construct a reverse deformation matrix; Perform recursive mirror transformation on the reverse deformation matrix until the transformation error of the reverse deformation matrix is lower than the preset error threshold to generate reverse deformation mapping data; among them, the mirror transformation operation is to reverse the data in the matrix according to the axis of symmetry, perform reverse projection on the data and recalculate the deformation result. After each mirror transformation, the error value after the current transformation is calculated through the regression analysis method and compared with the preset error threshold. If the error value is greater than the threshold, continue to perform the next round of recursive mirror transformation until the error value is less than the threshold; Perform reverse projection on the thermal deformation data through the reverse deformation mapping data, and build a simulated reverse deformation structure based on the reverse projection data; including mapping the deformation data and the thermal deformation data in three-dimensional space, projecting the reverse deformation mapping data back to the original thermal deformation data space, calculating the reverse structure of the thermal deformation according to the reverse deformation matrix, and mapping these reverse structure points to a new three-dimensional coordinate system according to the original geometric shape, and building a simulated reverse deformation structure by means of the reverse projection method; Perform structural difference comparison on the preset standard printing model and the simulated reverse deformation structure to obtain structural difference data; Analyze the deformation repair requirements according to the structural difference data to obtain the deformation repair requirements; Step S5: Perform compensation simulation on the model feature skeleton according to the deformation compensation path to obtain the simulated compensation process; analyze the non-target deformation in the simulated compensation process, and adjust the compensation path of the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

2. The 3D printing model slice feature compensation method according to claim 1, wherein Step S1 includes the following steps: Step S11: Collect a 3D printing model; perform high-precision point cloud data collection on the 3D printing model to obtain the original point cloud data; Step S12: Perform grid reconstruction on the original point cloud data and perform smoothing processing to generate a refined grid structure; Step S13: Extract the center line of the refined grid structure, and extract the model feature skeleton based on the center line for the refined grid structure; Step S14: Perform multi-directional rasterization encoding on the model feature skeleton to obtain the skeleton body pixel grid; perform inertial principal axis positioning on the skeleton body pixel grid and calibrate the inertial principal axis to obtain the oriented slice coordinate system; Step S15: Perform dynamic step slicing on the oriented slice coordinate system to generate the slice cross-sectional profile; extract feature key points from the slice cross-sectional profile to generate the skeleton slice features.

3. The 3D printing model slice feature compensation method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Collect the 3D printing environment; identify the environmental parameter distribution according to the 3D printing environment; perform interpolation processing on the environmental parameter distribution and construct a three-dimensional environmental gradient field; Step S22: Calculate the hydrodynamic parameters of the three-dimensional environmental gradient field. During the calculation process, the fluid viscosity is set to 1.85×10-5 Pa·s and the density is set to 1.225 kg / m³; perform virtual environment field simulation on the environmental parameter distribution based on the hydrodynamic parameters to obtain the simulated environment field; Step S23: Perform superposition analysis on the simulated environment field and the skeleton slice features and construct an interaction influence matrix; Step S24: Perform heat flow - material stress transfer simulation based on the interaction influence matrix to generate simulated stress transfer data; determine the environmental stress data based on the simulated stress transfer data.

4. The 3D printing model slice feature compensation method according to claim 1, characterized in that The deformation scale correlation deduction of the skeleton slice features based on the environmental stress data described in Step S3 includes: Perform stress contact simulation on the environmental stress data and the skeleton slice features, and perform elastoplastic tensor mapping based on the simulated contact data to generate the initial deformation tensor field; Fit the micro-layer thickness uniformity distribution of the skeleton slice features to obtain the layer thickness stability data; Perform heat transfer response simulation on the layer thickness stability data, and analyze the thermal inertia of the skeleton material based on the simulated heat transfer response data; Match the material thermal inertia data with the material characteristics according to the preset material database to obtain the model material data; Endow the initial deformation tensor field with material property constraints based on the model material data and determine the skeleton deformation amplitude; Identify the deformation critical point according to the skeleton deformation amplitude, and segment the skeleton deformation sensitivity based on the deformation critical point; Perform corresponding correlation on the environmental stress data based on the skeleton deformation sensitivity to obtain the skeleton stress deformation scale.

5. The method for compensating 3D printing model slicing features according to claim 1, wherein The thermal deformation prediction of the skeleton slice features according to the skeleton stress deformation scale and the environmental stress data described in Step S3 includes: Perform heat conduction attenuation simulation according to the environmental stress data to obtain the temperature gradient change data; Perform heat conduction response mapping on the skeleton stress deformation scale based on the temperature gradient change data and construct a thermal stress distribution matrix; Perform thermal field hysteresis effect fitting according to the thermal stress distribution matrix and perform hysteresis compensation to generate thermal inertia compensation data; Perform interlayer thermal expansion analysis based on the thermal inertia compensation data to obtain the interlayer expansion data; Predict the deformation trajectory of the skeleton slice features according to the interlayer expansion data to generate thermal deformation data.

6. The 3D printing model slice feature compensation method according to claim 1, wherein The deformation compensation path planning according to the skeleton stress deformation scale and the deformation repair requirement in Step S4 includes: Determine the required deformation area according to the deformation repair requirement; Match the corresponding skeleton stress deformation scale of the area based on the required deformation area; Extract the demand deformation amplitude of the deformation repair requirement; Based on the demand deformation amplitude, perform demand stress conversion on the skeleton stress deformation scale corresponding to the region to obtain the deformation demand stress; Based on the deformation demand stress and the demand deformation region, deduce the compensation path to generate the deformation compensation path.

7. The 3D printing model slice feature compensation method according to claim 1, characterized in that, Step S5 includes the following steps: Step S51: Discretize the deformation compensation path to obtain a set of path control points; based on the set of path control points, perform deformation compensation simulation on the model feature skeleton to generate a simulation compensation process; Step S52: Identify the deformation compensation target region in the compensation process; analyze the non-target region in the compensation process based on the deformation compensation target region; Step S53: Compare the deformation before and after compensation of the non-target region according to the model feature skeleton to obtain the non-target deformation; Step S54: Trace back the compensation process based on the non-target deformation, and determine the initial time point of the non-target deformation based on the traced-back compensation process; analyze the cause of the non-target deformation according to the initial time point of the non-target deformation; Step S55: Perform path adjustment simulation on the deformation compensation path based on the cause of the non-target deformation, and perform conflict detection on the simulated adjusted compensation path to generate a compensation path change conflict set. Among them, the step size of the path adjustment simulation is limited to 0.5 mm, and the conflict detection standard is that the part where the compensation paths are no more than 2 mm apart is regarded as the conflict area, and the generation basis of the conflict set is that the cumulative path error value is greater than 0.05 mm; Step S56: Adjust the compensation path of the deformation compensation path according to the cause of the non-target deformation and the compensation path change conflict set to obtain an optimized feature compensation method.

8. A 3D printing model slice feature compensation system, characterized in that, For executing the 3D printing model slice feature compensation method as described in claim 1, the 3D printing model slice feature compensation system includes: A model skeleton extraction module, configured to collect a 3D printing model; split the model feature skeleton of the 3D printing model; perform transverse slice mapping on the model feature skeleton to generate skeleton slice features; An environmental stress simulation module, configured to collect a 3D printing environment; perform virtual environment field simulation according to the 3D printing environment, and perform environmental stress analysis on the skeleton slice features based on the simulated environment field to generate environmental stress data; A deformation scale deduction module, configured to perform deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; perform thermal deformation prediction on the skeleton slice features according to the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; A reverse deformation repair module, configured to perform reverse deformation simulation based on the thermal deformation data, and analyze the deformation repair requirement based on a preset standard printing model and the simulated reverse deformation structure; plan the deformation compensation path according to the skeleton stress deformation scale and the deformation repair requirement; A deformation compensation optimization module, configured to perform compensation simulation on the model feature skeleton according to the deformation compensation path to obtain a simulation compensation process; analyze the non-target deformation in the simulation compensation process, and perform compensation path adjustment on the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the 3D printing model slice feature compensation method described in any one of claims 1 to 7.

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