3D printing model slice feature compensation method and system and medium

Abstract

The invention relates to the technical field of 3D printing models, in particular to a 3D printing model slice feature compensation method and system and a medium. The method comprises the following steps that a 3D printing model is collected, a model feature skeleton is split, transverse slice features are generated, then a 3D printing environment is collected, virtual simulation is carried out, environment stress analysis is carried out on the skeleton slice features to obtain environment stress data, then deformation scale deduction and thermal deformation prediction are carried out based on the environment stress data, and a thermal deformation model is obtained. On the basis, reverse deformation simulation is carried out, deformation repair requirements are evaluated, a deformation compensation path is planned, finally, compensation simulation of a model feature skeleton is achieved, non-target deformation is analyzed, a compensation path is adjusted, and therefore the feature compensation method is optimized. According to the 3D printing model slice feature compensation method, the accuracy and quality of the 3D printing model are integrally improved.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D printing models, and in particular 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, internal stress release behavior, etc.) and printing process parameters (such as scanning strategy, layer thickness, filling method, etc.), 3D printed 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 the degradation of structural performance, making the final product unable to meet the design requirements, thereby affecting the popularization and development of 3D printing technology. At present, for the deformation problems that occur in the 3D printing process, traditional compensation methods mainly rely on empirical rules or simple deformation compensation models. Most of these methods fail to make accurate predictions based on the physical environment, material stress, and thermal deformation data, resulting in low reliability of compensation strategies. The temperature gradient of the printing environment, the constraint of the support structure, and the dynamic changes in the internal stress of the material may all lead to unpredictable deformation, which is difficult for traditional methods to deal with effectively. 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 purpose, a 3D printing model slice feature compensation method includes the following steps: Step S1: collecting a 3D printed model; splitting a model feature skeleton of the 3D printed model; performing horizontal slice mapping on the model feature skeleton to generate skeleton slice features; Step S2: collecting the 3D printing environment; performing virtual environment field simulation according to the 3D printing environment, and performing environmental stress analysis on the skeleton slice characteristics based on the simulated environment field to generate environmental stress data; Step S3: performing deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; performing thermal deformation prediction on the skeleton slice features based on the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; Step S4: performing reverse deformation simulation based on thermal deformation data, and analyzing deformation repair requirements based on a preset standard printing model and simulated reverse deformation structure; planning a deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; Step S5: performing compensation simulation on the model feature skeleton according to the deformation compensation path to obtain a simulated compensation process; analyzing the non-target deformation in the simulated compensation process, and adjusting the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0005] The present invention can lay a foundation for subsequent analysis and processing by collecting 3D printing models and splitting out model feature skeletons. The skeleton slice features generated by the transverse slice mapping of the model feature skeleton provide structural detail information. The external influencing factors in the printing process can be fully understood by collecting the 3D printing environment and performing virtual environment field simulation. The environmental stress analysis provides key data for the performance evaluation of the skeleton slice features in a specific environment. The deformation scale correlation deduction based on environmental stress data provides theoretical support for understanding the deformation behavior of materials under different stress conditions. The thermal deformation prediction can quantify the thermal impact on the material during the printing process. The generated thermal deformation data provides a basis for model optimization. The implementation of reverse deformation simulation can clarify the deviation between the printer setting and the physical model. The comparative analysis of the standard printing model and the simulated reverse deformation structure is helpful to accurately identify the needs of deformation repair. The planning of the deformation compensation path provides a strategic guide for the repair process. The compensation simulation of the model feature skeleton can be pre-verified before actual printing. The analysis of the non-target deformation in the simulation compensation process provides feedback and adjustment basis for the optimization compensation scheme, which improves the accuracy and quality of the 3D printing model as a whole and provides effective technical support for the subsequent printing process control and finished product quality assurance.

[0006] Preferably, step S1 comprises the following steps: Step S11: collecting a 3D printing model; collecting high-precision point cloud data of the 3D printing model to obtain original point cloud data; Step S12: gridding and reconstructing the original point cloud data, and performing smoothing processing to generate a refined grid structure; Step S13: extracting the center line of the refined grid structure, and performing model feature skeleton extraction on the refined grid structure based on the center line; Step S14: performing multi-directional raster encoding on the model feature skeleton to obtain a skeleton pixel grid; performing inertial principal axis positioning on the skeleton pixel grid, and performing inertial principal axis calibration on the inertial principal axis to obtain a directional slice coordinate system; Step S15: Perform dynamic step-length slicing processing on the oriented slicing coordinate system to generate a slice cross-sectional contour; perform feature key point extraction on the slice cross-sectional contour to generate a skeleton slicing feature.

[0007] The present invention provides an accurate basis for the digital reconstruction of 3D printed models through the collection of high-precision point cloud data. The acquisition of original point cloud data ensures the integrity of model details. The refined grid structure generated by grid reconstruction and smoothing processing improves the operability and visualization effect of the model. The extraction of centerline lays a key foundation for subsequent model feature analysis. Skeleton extraction can effectively simplify complex models, making feature analysis and compensation more efficient. Multi-directional rasterization encoding enhances the structural representation ability of the skeleton. Inertial spindle positioning and calibration ensure the consistency and accuracy of the slicing process. The establishment of a directional slicing coordinate system provides a clear reference frame for slicing processing. Dynamic step slicing processing combined with the generation of cross-sectional contours can achieve more accurate slicing effects. The extraction of feature key points provides important data support for subsequent compensation and optimization, which improves the accuracy and quality of 3D printed models as a whole and provides a solid technical guarantee for subsequent printing processes and model applications.

[0008] Preferably, step S2 comprises the following steps: Step S21: collecting 3D printing environment; identifying environmental parameter distribution according to the 3D printing environment; interpolating the environmental parameter distribution and constructing a three-dimensional environmental gradient field; Step S22: Calculate the fluid dynamics parameters of the three-dimensional environmental gradient field, during which the fluid viscosity is set to 1.85×10-5Pa·s and the density is set to 1.225kg / m³; perform virtual environmental field simulation on the environmental parameter distribution based on the fluid dynamics parameters to obtain a simulated environmental field; Step S23: superimpose and analyze the simulation environment field and skeleton slice features, and construct an interaction influence matrix; Step S24: performing heat flow-material stress transfer simulation based on the interaction influence matrix to generate simulated stress transfer data; and determining environmental stress data based on the simulated stress transfer data.

[0009] The present invention provides a basis for a comprehensive understanding of the distribution of environmental parameters through the ability to collect 3D printing environments. The interpolation processing of environmental parameters ensures the continuity and accuracy of environmental data. The construction of a three-dimensional environmental gradient field provides rich information for subsequent simulation analysis, including the calculation of fluid dynamics parameters, which gives environmental simulation a real physical basis. The realization of virtual environmental field simulation can quickly evaluate the impact under different environmental conditions. The superposition analysis of the simulated environmental field and skeleton slice features strengthens the correlation between the model and the environment. The establishment of an interactive influence matrix provides a systematic data framework, which lays the foundation for analyzing the impact of environmental factors on model deformation. The development of heat flow-material stress transfer simulation enables the stress transfer process of materials in a specific environment to be quantified. The generated simulated stress transfer data provides key parameters for subsequent environmental stress evaluation, which overall improves the ability to identify and compensate for environmental impacts in the 3D printing process, and provides a scientific basis for high-quality printing.

[0010] Preferably, the step S3 of performing deformation scale correlation deduction on skeleton slice features based on environmental stress data includes: Perform stress contact simulation on environmental stress data and skeleton slice features, and perform elastic-plastic tensor mapping based on simulated contact data to generate initial deformation tensor field; The microscopic layer thickness uniformity distribution is fitted to the skeleton slice characteristics to obtain the stable layer thickness data; Conduct heat transfer response simulation on layer thickness stability data, and analyze the thermal inertia of the skeleton material based on the simulated heat transfer response data; Perform material feature matching on material thermal inertia data according to a preset material database to obtain model material data; Assign material property constraints to the initial deformation tensor field based on the model material data and determine the deformation amplitude of the skeleton; Identify the deformation critical point according to the deformation amplitude of the skeleton, and divide the deformation sensitivity of the skeleton based on the deformation critical point; Based on the sensitivity of skeleton deformation, the environmental stress data are correlated to obtain the skeleton stress deformation scale.

[0011] The present invention provides a deep understanding of the performance of the model in the actual environment by performing stress contact simulation on environmental stress data and skeleton slice characteristics. The initial deformation tensor field generated by elastic-plastic tensor mapping lays a mathematical foundation for subsequent deformation analysis. The fitting of the microscopic 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 thermal transfer response simulation provides the response characteristics of the model material to thermal changes, which is helpful for in-depth analysis of the thermal inertia of the skeleton material and improves the ability to predict material behavior. 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 deformation critical points provides a specific reference for optimizing design and production. The segmentation of deformation sensitivity can effectively formulate corresponding compensation strategies for specific areas. Finally, the correlation and integration of environmental stress data and skeleton stress deformation scales form a comprehensive mechanical behavior analysis, which provides definite data support and theoretical basis for improving the accuracy and quality of 3D printed models.

[0012] Preferably, the step S3 of predicting thermal deformation of skeleton slice features according to the skeleton stress deformation scale and environmental stress data includes: Perform heat conduction attenuation simulation based on environmental stress data to obtain temperature gradient change data; Based on the temperature gradient change data, the thermal conduction response of the skeleton stress deformation scale is mapped and the thermal stress distribution matrix is ​​constructed; Fit the thermal field hysteresis effect according to the thermal stress distribution matrix, perform hysteresis compensation, and generate thermal inertia compensation data; Perform interlayer thermal expansion analysis based on thermal inertia compensation data to obtain interlayer expansion data; The deformation trajectory of the skeleton slice features is predicted based on the interlayer expansion data to generate thermal deformation data.

[0013] The present invention can effectively reveal the influence of temperature change on material properties through the thermal conduction attenuation simulation of environmental stress data. The temperature gradient change data provides an important basis for the subsequent thermal stress analysis. The thermal conduction response mapping of the skeleton stress deformation scale can quantify the behavior of the material under different temperature conditions. The construction of the thermal stress distribution matrix deepens the understanding of the internal stress state of the material. The fitting of the thermal field hysteresis effect helps to identify and compensate for the dynamic response caused by temperature change. The generated thermal inertia compensation data provides a reasonable compensation basis for the model under a temperature change environment. The implementation of interlayer thermal expansion fitting ensures that the thermal stress distribution between different layers is more uniform. The acquisition of interlayer expansion data provides necessary information for the accurate prediction of deformation trajectory. The thermal deformation data finally generated provides a scientific quantitative basis for the deformation phenomenon occurring in the 3D printing process, which improves the overall adaptability and prediction accuracy of the printing model to the influence of thermal stress, and provides strong support for high-quality printing.

[0014] Preferably, the reverse deformation simulation based on the thermal deformation data described in step S4, and the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure analysis include: Perform vector inversion processing on thermal deformation data and construct an inverse deformation matrix; Recursively mirror transform the inverse deformation matrix until the transformation error of the inverse deformation matrix is ​​lower than a preset error threshold, so as to generate inverse deformation mapping data; Reversely project the thermal deformation data through the reverse deformation mapping data, and build a simulated reverse deformation structure based on the reverse projection data; Perform structural difference comparison on the preset standard printing model and the simulated reverse deformation structure to obtain structural difference data; Perform deformation repair demand analysis based on the structural difference data to obtain the deformation repair requirements.

[0015] The present invention lays a foundation for reverse deformation simulation through vector inversion processing of thermal deformation data, provides a systematic mathematical model for the construction of the reverse deformation matrix, ensures the precision and accuracy of the matrix transformation by recursive mirror transformation, ensures the reliability of the result by controlling the transformation error within a preset threshold, and the generated reverse deformation mapping data provides a guarantee for the reverse projection of the thermal deformation data. The construction of the reverse 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 the structural changes, and the acquisition of the structural difference data provides a scientific basis for the analysis of the subsequent deformation repair needs. The analysis of the deformation repair needs can formulate compensation plans in a targeted manner, which overall improves the adaptability and precision of the model under different printing conditions, and provides rational theoretical guidance and practical guarantees for high-quality printing.

[0016] Preferably, planning the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements in step S4 includes: Determine the required deformation area according to the deformation repair requirements; Matching the skeleton stress deformation scale corresponding to the region based on the required deformation region; Extract the required deformation amplitude required for deformation repair; Based on the required deformation amplitude, the skeleton stress deformation scale corresponding to the region is converted into a required stress to obtain a deformation required stress; The compensation path is deduced based on the deformation required stress and required deformation area to generate the deformation compensation path.

[0017] The present invention ensures accurate identification of problem areas of the model by locating the deformation repair requirements. The confirmation of the required deformation area provides a clear target 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 the foundation for the formulation of the compensation strategy. The required stress conversion process ensures the scientificity and rationality of the compensation strategy. The compensation path deduction based on the deformation required stress can realize targeted compensation implementation. The generated deformation compensation path provides reliable guidance for actual operation, which improves the accuracy and structural stability of the 3D printed model as a whole, and provides effective data support and decision-making basis for optimizing the printing process.

[0018] Preferably, step S5 comprises the following steps: Step S51: discretizing the deformation compensation path to obtain a set of path control points; simulating deformation compensation on the model feature skeleton based on the set of path control points to generate a simulation compensation process; Step S52: identifying a deformation compensation target area during the compensation process; and analyzing a non-target area during the compensation process based on the deformation compensation target area; Step S53: performing deformation comparison of the non-target area before and after compensation according to the model feature skeleton to obtain the non-target deformation; Step S54: backtracking the compensation process based on the non-target deformation, and determining the initial time point of the non-target deformation based on the backtracking compensation process; analyzing the cause of the non-target deformation according to the initial time point of the non-target deformation; Step S55: simulating the deformation compensation path based on the non-target deformation inducement, and performing conflict detection on the simulated adjustment compensation path to generate a compensation path change conflict collection, wherein the step length of the path adjustment simulation is limited to 0.5 mm, the conflict detection standard is that the part of the compensation path that is not more than 2 mm apart is regarded as the conflict area, and the basis for generating the conflict collection is that the cumulative value of the error between the paths is greater than 0.05 mm; Step S56: adjusting the deformation compensation path according to the non-target deformation inducement and the compensation path change conflict set to obtain an optimized feature compensation method.

[0019] The present invention performs kinematic discretization on the deformation compensation path, making the acquisition of the path control point set operable and accurate. The compensation simulation of the model feature skeleton based on the path control point set generates a credible compensation process. The identification of the compensation target area ensures the pertinence of the compensation work. The analysis of the non-target area provides data support for identifying potential problems. The comparison of the deformation before and after enables a deeper understanding of the non-target deformation. The backtracking of the compensation process provides a basis for determining the initial time point of the non-target deformation. The analysis of the inducement of the non-target deformation provides key clues for optimizing the compensation path. The path adjustment simulation can timely identify the conflicts in the compensation path. The generated collection of compensation path change conflicts lays the foundation for the improvement of the actual compensation scheme. The final compensation path adjustment realizes the optimization of the feature compensation method, which improves the adaptability and accuracy of the 3D printing model as a whole, and provides comprehensive support and guarantee for improving the quality of the printed product.

[0020] The present invention also provides a 3D printing model slicing feature compensation system, which is used to execute the 3D printing model slicing feature compensation method as described above. The 3D printing model slicing feature compensation system includes: The model skeleton extraction module is used to collect the 3D printing model; split the model feature skeleton of the 3D printing model; perform horizontal slice mapping on the model feature skeleton to generate skeleton slice features; Environmental stress simulation module, used to collect 3D printing environment; simulate virtual environment field according to 3D printing environment, and perform environmental stress analysis on skeleton slice features based on simulated environment field to generate environmental stress data; The deformation scale deduction module 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; perform thermal deformation prediction on the skeleton slice features based on the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; The reverse deformation repair module is used to simulate the reverse deformation 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; and plan the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; The deformation compensation optimization module is used to simulate the compensation of 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 deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0021] The present invention can systematically collect and split the 3D printing model through the introduction of the model skeleton extraction module, so that the subsequent analysis has detailed structural information. The skeleton slice features generated by the transverse slice mapping provide the application with operable detail data. The function of the environmental stress simulation module realizes a comprehensive scan of the 3D printing environment and effectively identifies the impact of environmental factors on the model during the printing process. The virtual environment field simulation can non-destructively judge the performance under the construction conditions and provide 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 deformation scale deduction module can effectively evaluate the deformation of the model under different conditions based on the correlation deduction of the environmental stress data. 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 the deformation situation and the accurate analysis of the repair requirements. The planning of the deformation compensation path provides a targeted repair strategy. The deformation compensation optimization module innovatively avoids the 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, and improves the finished product quality and production efficiency of the 3D printed model as a whole.

[0022] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed, the 3D printing model slicing feature compensation method as described in any one of the above items is implemented.

[0023] The present invention can provide an efficient storage and operating environment for the implementation of the 3D printing model slice feature compensation method through the introduction of computer-readable storage media. The stored computer program ensures the reusability and efficient calling of the method. The programmed execution process improves the accuracy and consistency of the overall operation and can quickly respond to the processing requirements of different printing models. The optimized computer program realizes the rapid processing of complex calculations and supports the analysis and compensation of various 3D printing model features, saving users a lot of time and energy. The flexible program architecture makes it easy to expand and upgrade functions in the future. The systematic and orderly programming logic reduces the risk of human operation errors and ensures that the model is accurately verified and optimized before printing, thereby improving the quality and stability of the final product. It provides strong technical support and guarantee for the popularization and application of 3D printing technology, and overall enhances the intelligence level and automation level of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of the steps of a 3D printing model slice feature compensation method; Figure 2 Detailed implementation flow chart of step S2; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0025] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying 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 implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. 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.

[0028] To achieve this, please refer to Figure 1 to Figure 2 , a 3D printing model slice feature compensation method, comprising the following steps: Step S1: collecting a 3D printed model; splitting a model feature skeleton of the 3D printed model; performing horizontal slice mapping on the model feature skeleton to generate skeleton slice features; Step S2: collecting the 3D printing environment; performing virtual environment field simulation according to the 3D printing environment, and performing environmental stress analysis on the skeleton slice characteristics based on the simulated environment field to generate environmental stress data; Step S3: performing deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; performing thermal deformation prediction on the skeleton slice features based on the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; Step S4: performing reverse deformation simulation based on thermal deformation data, and analyzing deformation repair requirements based on a preset standard printing model and simulated reverse deformation structure; planning a deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; Step S5: performing compensation simulation on the model feature skeleton according to the deformation compensation path to obtain a simulated compensation process; analyzing the non-target deformation in the simulated compensation process, and adjusting the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0029] The present invention can lay a foundation for subsequent analysis and processing by collecting 3D printing models and splitting out model feature skeletons. The skeleton slice features generated by the transverse slice mapping of the model feature skeleton provide structural detail information. The external influencing factors in the printing process can be fully understood by collecting the 3D printing environment and performing virtual environment field simulation. The environmental stress analysis provides key data for the performance evaluation of the skeleton slice features in a specific environment. The deformation scale correlation deduction based on environmental stress data provides theoretical support for understanding the deformation behavior of materials under different stress conditions. The thermal deformation prediction can quantify the thermal impact on the material during the printing process. The generated thermal deformation data provides a basis for model optimization. The implementation of reverse deformation simulation can clarify the deviation between the printer setting and the physical model. The comparative analysis of the standard printing model and the simulated reverse deformation structure is helpful to accurately identify the needs of deformation repair. The planning of the deformation compensation path provides a strategic guide for the repair process. The compensation simulation of the model feature skeleton can be pre-verified before actual printing. The analysis of the non-target deformation in the simulation compensation process provides feedback and adjustment basis for the optimization compensation scheme, which improves the accuracy and quality of the 3D printing model as a whole and provides effective technical support for the subsequent printing process control and finished product quality assurance.

[0030] In an embodiment of the present invention, the 3D printing model slice feature compensation method comprises the following steps: Step S1: collecting a 3D printed model; splitting a model feature skeleton of the 3D printed model; performing horizontal slice mapping on the model feature skeleton to generate skeleton slice features; In this embodiment, data collection is performed on the 3D printed model, and 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, 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 Poisson Surface Reconstruction algorithm is used to generate a high-precision triangular mesh. The mesh density is set to 2000 facets per square millimeter to generate a model feature skeleton. The model skeleton data is extracted based on the Skeletonization method, and the Thinning Algorithm is used to perform topology preservation processing to obtain the model skeleton topology structure. The two-dimensional slice mapping method is used for transverse slicing, and the slice spacing is set to 0.05 mm. The Contour Detection method is used to extract the boundary curve of each slice, and the Bézier Curve Fitting is used to optimize the boundary accuracy to generate skeleton slice feature data.

[0031] Step S2: collecting the 3D printing environment; performing virtual environment field simulation according to the 3D printing environment, and performing environmental stress analysis on the skeleton slice characteristics based on the simulated environment field to generate environmental stress data; In this embodiment, a high-resolution laser scanner is used to collect data from the 3D printing environment to obtain parameters such as the printing platform, nozzle temperature, air flow state, and support structure. The nozzle temperature range is set at 180°C to 260°C, and the wind speed detection range is set at 0m / s to 5m / s. Computational Fluid Dynamics (CFD) is used to simulate air flow characteristics, and the boundary conditions are set to an open flow field, the fluid viscosity is set to 0.001Pa·s, and k- The turbulence model solves the influence of air disturbance and generates printing environment flow field data. The environmental stress is loaded on the skeleton slice feature data based on the finite element method (FEM). Constraints imposed include fixed support constraints, material shrinkage constraints and temperature gradient constraints. The temperature gradient range is set to 5℃ / mm to 20℃ / mm. The von Mises Stress criterion is used to calculate the equivalent stress of each slice feature to obtain the environmental stress data.

[0032] Step S3: performing deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; performing thermal deformation prediction on the skeleton slice features based on the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; In this embodiment, the environmental stress data is used to perform deformation scale correlation deduction on the skeleton slice feature data, and a discrete deformation model is constructed based on the Material Point Method (MPM). The material yield strength is set to 50 MPa, the elastic modulus is set to 2500 MPa, and the Poisson's ratio is set to 0.35. The anisotropic deformation gradient under stress is calculated, and the local deformation error is corrected by the Shape Matching method to obtain 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. The thermal expansion coefficient is set to 75×10 -6 / K, the thermal conductivity of the material was set to 0.25W / (m·K), the Heat Transfer Simulation method was used to calculate the temperature gradient influence of each slice, and the thermal deformation process was simulated based on the Kelvin-Voigt Model to obtain the thermal deformation data.

[0033] Step S4: performing reverse deformation simulation based on thermal deformation data, and analyzing deformation repair requirements based on a preset standard printing model and simulated reverse deformation structure; planning a deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; In this embodiment, vector reversal processing is performed on the thermal deformation data, and the thermal deformation field is reversely corrected by using the Inverse Finite Element Method (iFEM), the deformation compensation threshold is set to 0.02 mm, and an inverse deformation matrix is ​​constructed. The inverse deformation matrix is ​​iteratively optimized by using the Recursive Mirror Transformation method, and the convergence threshold is set to 0.001 mm. When the maximum residual is less than the threshold, the iteration is stopped to obtain an inverse deformation mapping function, and the preset standard printing model is fitted by using the inverse deformation mapping function, wherein the standard printing model refers to a theoretical optimal model derived according to the design parameters under ideal environmental conditions, that is, a 3D printing target model that is not affected by environmental stress, thermal deformation, etc., and ensures that the key dimensional error is controlled within ±0.05 mm, the surface roughness Ra≤5μm, the contour accuracy deviation does not exceed 0.1%, and the mechanical property error does not exceed 3%. Calculate the model correction deviation, set the upper limit of deformation correction to 0.5mm, and 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 grid cell cost, the better the path. The location of the starting point and the target point of the path is clear, and the adjacent grid cells that can be reached when the path moves are defined (for example, 4 neighborhoods, 8 neighborhoods, or 26 neighborhoods). With the starting point as the center, expand the adjacent feasible cells layer by layer, and give priority to the adjacent cells with the lowest cost to join the path candidate queue until it is extended to the target point. If there are multiple equal-cost paths, select the path with the shortest path length or the least number of turns, and trace back from the target point to the starting point along the direction of the minimum cost to form a complete deformation compensation path sequence. While avoiding obstacles and areas with large deformation, the path tries to pass through areas with low cost and small deformation to ensure the rationality of compensation and path optimization effect. The obtained path sequence is smoothed to remove unnecessary turning points to ensure the continuity of the path and the feasibility of the compensation motion trajectory to plan the deformation compensation path. The path search step size is set to 0.1 mm to obtain the deformation compensation path.

[0034] Step S5: performing compensation simulation on the model feature skeleton according to the deformation compensation path to obtain a simulated compensation process; analyzing the non-target deformation in the simulated compensation process, and adjusting the deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0035] In this embodiment, the deformation compensation path is used to simulate the compensation of the model feature skeleton, the IncrementalDeformationSuperposition method is used to perform the compensation operation in layers, the compensation step size of each layer is set to 0.05mm, the Adaptive Compensation Filtering method is used to smooth the non-uniform compensation area to obtain a simulated compensation process, the local error analysis of the simulated compensation process is performed, the non-target deformation detection threshold is set to 0.02mm, the Difference Mapping method is used to calculate the non-target deformation area, the local compensation parameters are optimized based on the Deformation Constraint Adjustment method, the deformation compensation path is adjusted, the compensation path curvature is optimized using the Nonlinear Compensation Path Refinement method, and the optimized feature compensation method is generated.

[0036] Preferably, step S1 comprises the following steps: Step S11: collecting a 3D printing model; collecting high-precision point cloud data of the 3D printing model to obtain original point cloud data; Step S12: gridding and reconstructing the original point cloud data, and performing smoothing processing to generate a refined grid structure; Step S13: extracting the center line of the refined grid structure, and performing model feature skeleton extraction on the refined grid structure based on the center line; Step S14: performing multi-directional raster encoding on the model feature skeleton to obtain a skeleton pixel grid; performing inertial principal axis positioning on the skeleton pixel grid, and performing inertial principal axis calibration on the inertial principal axis to obtain a directional slice coordinate system; Step S15: Perform dynamic step-length slicing processing on the oriented slicing coordinate system to generate a slice cross-sectional contour; perform feature key point extraction on the slice cross-sectional contour to generate a skeleton slicing feature.

[0037] In this embodiment, a structured light scanning device is used to collect data from the 3D printed model, the projection grating frequency is set to 150Hz, a 3D camera is used to record the fringe distortion at multiple angles, a phase unwrapping algorithm is used to calculate the 3D coordinate information of the model surface, the data acquisition accuracy is controlled within 0.01mm, the original point cloud data is generated, a polarization filter is used to reduce the interference of mirror reflection in the highly reflective area, the point cloud data is converted into a coordinate format (x, y, z), the KinectFusion algorithm is used to align the multi-frame point cloud data, and the ICP is set. The convergence threshold of the (iterative closest point) algorithm is 0.0005mm. In order to improve the accuracy of point cloud stitching, the statistical filtering method is used to remove noise points, and the filter window size is set to 10 neighborhood points. The abnormal points with a standard deviation exceeding 2 times the mean are removed to obtain the optimized original point cloud data. The Delaunay triangulation method is used to grid the original point cloud data. The constrained Delaunay triangulation algorithm is used to generate a non-uniform triangular mesh. The minimum side length of the mesh is set to 0.05mm and the maximum side length is set to 0.2mm. The Laplacian The Smoothing algorithm is used to optimize the mesh surface. The number of smoothing iterations is set to 5. The coordinate position of each vertex is adjusted to minimize the weight mean of its neighborhood points. The Bilateral Mesh Denoising method is used to retain the mesh details. The weight functions σc=0.1 and σs=0.2 are set. The Curvature Flow Smoothing method is used for high curvature areas to avoid the loss of mesh geometric features. Finally, a refined mesh structure is generated. The center line of the refined mesh structure is extracted using a method based on principal curvature analysis. The principal curvature value of each vertex is calculated. The curvature threshold is set to 0.15, and the curvature peak point is screened. The center line is optimized based on the Shortest Path Skeletonization method. The shortest path from the skeleton point to the mesh surface is used as the skeleton optimization criterion. The skeleton distribution density is adjusted using an adaptive step size algorithm. The step size range is set to 0.02mm to 0.1mm. Geodesic-based The Pruning method is used to remove redundant skeleton branches, and finally the model feature skeleton is generated. The model feature skeleton is multi-directionally rasterized and encoded. The Octree Decomposition method is used to spatially divide the skeleton points. The minimum voxel size of the octree is set to 0.1 mm. The skeleton points are mapped to a 3D voxel grid to generate a skeleton volume pixel grid. The inertial principal axis direction of the skeleton point cloud is calculated based on the PCA method, and the eigenvalue threshold of the covariance matrix decomposition is set to 0.01, in order to screen the main inertial direction, the Iterative Axis Refinement method was used to calibrate the inertial principal axis, and the iteration step was set to 0.005mm until the inertial principal axis deviation was less than 0.001mm. The directional slice coordinate system was generated, and dynamic step-size slicing was performed based on the directional slice coordinate system. The Adaptive Slicing method was used to adjust the slicing step according to the local curvature, and the step range was set to 0.02mm to 0.2mm. The Marching Squares Algorithm was used to calculate the cross-sectional profile of each slice. The Douglas-Peucker Simplification method was used to reduce the redundant points of the contour, and the simplification error threshold was set to 0.005mm. The key points of the simplified contour were extracted, and the Scale-Space Feature Detection method was used to locate the extreme points of curvature. The Gaussian scale parameter σ was set to 1.5mm, and the low signal-to-noise ratio points were eliminated. Finally, the skeleton slice features were generated. .

[0038] It is particularly important that the inertial principal axis positioning of the skeleton volume pixel grid and the inertial principal axis calibration of the inertial principal axis described in step S14 include: Perform spatial distribution analysis on the skeleton pixel grid to obtain density distribution characteristics; Fit the main inertial direction to the density distribution characteristic data to generate the initial inertial principal axis; Perform spatial alignment optimization on the initial inertia principal axis to obtain the initial orientation coordinate system; Perform global error evaluation on the initial orientation coordinate system, and perform local calibration fitting on the initial inertial principal axis based on the global error to obtain the optimized inertial principal axis; The coordinate system calibration transformation is performed based on the optimized inertial principal axis to obtain the oriented slice coordinate system.

[0039] In this embodiment, when performing spatial distribution analysis on the skeleton pixel grid, the three-dimensional coordinates of each pixel point in the grid need to be accurately collected first. After obtaining the point cloud data set, the spatial distribution analysis is performed based on the geometric shape of the grid and the distribution of the point cloud data. First, the grid is preliminarily partitioned by the centroid (geometric center), and then the local density of the point cloud data in each area is calculated. The density calculation is based on the distance relationship between each grid point and its neighborhood. The voxel grid method (Voxel Grid Method) divides the point cloud into multiple small voxel blocks. The number of point clouds in each voxel is the density value of the area. The spatial density distribution characteristics of the overall grid are obtained by counting the density of each voxel block. The area with a larger density value corresponds to a more concentrated or complex part of the grid. This method obtains the spatial density characteristics of the grid, and finally obtains a grid density data set based on spatial distribution. When fitting the main inertia direction of the density distribution characteristic data, the inertia matrix of each grid voxel is first calculated. The inertia matrix reflects the distribution characteristics of the voxel in three-dimensional space. In the calculation process, the mass of the voxel is regarded as the volume or mass of the point cloud it contains. The geometric center of the voxel is determined by weighted averaging. The calculation of the inertia matrix involves the coordinates of the points in each voxel and its density. The calculated inertia matrix can be used to obtain the direction of the principal axis of inertia through eigenvalue decomposition. The direction of the principal axis of inertia is determined by the eigenvector corresponding to the maximum eigenvalue. Through this method, based on the density The initial inertial axis direction of the grid is obtained by the degree distribution data. The inertial axis obtained by fitting the main inertial direction is compared with the reference coordinate system, and the direction of the axis is adjusted by minimizing the deviation. The iterative optimization method is used in the adjustment process. The angle difference between the initial inertial axis and the reference coordinate system is calculated in each iteration, and the inertial axis is spatially aligned through the rotation matrix. The calculation of the rotation matrix is ​​based on the rotation angle between the known axis direction and the target axis direction. The least square method is used as the optimization target in the calculation process, and the rotation matrix is ​​continuously adjusted until the alignment error reaches the predetermined threshold. When the rotation angle error is less than 0.01 degrees, the optimization process is stopped, and finally an initial oriented coordinate system that is highly aligned with the reference coordinate system is obtained. By comparing the known calibration data with the point cloud data in the current oriented coordinate system, the global accuracy of the current coordinate system is evaluated. The error evaluation method includes calculating the Euclidean distance difference between the coordinate points, and accumulating all error differences to obtain the total error value. In the error evaluation process, the error threshold is set to 0.05mm. When the global error exceeds this threshold, it indicates that the accuracy of the initial orientation coordinate system is insufficient and further local calibration and fitting is required. When the initial inertial principal axis is locally calibrated and fitted based on the global error, the local weighted least squares method is used to refine the error area. By focusing on the area with large errors and combining the point cloud distribution in the local area, the inertia 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 0.1mm. The goal of local calibration is to control the error within 0.05mm. Through multiple iterative optimizations, the local error is gradually reduced. Until the global error meets the preset requirements, the optimized inertial principal axis is finally obtained. When the coordinate system calibration transformation is performed based on the optimized inertial principal axis, the optimized inertial principal axis direction is rotated and transformed, and the coordinate axis is transformed using the rotation matrix to transform the data of the original coordinate system into the new oriented slice coordinate system. During the transformation process, the rotation matrix is ​​first calculated. The rotation matrix is ​​determined based on the angular difference between the optimized inertial principal axis and the principal axis direction of the original coordinate system. By performing a rotation matrix transformation on each point cloud data, all point cloud data are finally aligned to the new oriented slice coordinate system. After the coordinate system calibration transformation, the oriented slice coordinate system is finally obtained. .

[0040] Preferably, step S2 comprises the following steps: Step S21: collecting 3D printing environment; identifying environmental parameter distribution according to the 3D printing environment; interpolating the environmental parameter distribution and constructing a three-dimensional environmental gradient field; Step S22: Calculate the fluid dynamics parameters of the three-dimensional environmental gradient field, during which the fluid viscosity is set to 1.85×10-5Pa·s and the density is set to 1.225kg / m³; perform virtual environmental field simulation on the environmental parameter distribution based on the fluid dynamics parameters to obtain a simulated environmental field; Step S23: superimpose and analyze the simulation environment field and skeleton slice features, and construct an interaction influence matrix; Step S24: performing heat flow-material stress transfer simulation based on the interaction influence matrix to generate simulated stress transfer data; and determining environmental stress data based on the simulated stress transfer data.

[0041] In this embodiment, a high-precision environmental sensor array is used to collect data from the 3D printing environment. The environmental sensors used include temperature sensors, humidity sensors, air flow rate sensors, air pressure sensors, and radiation heat sensors. 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. Environmental parameters at different spatial locations are collected and interpolated using the Kriging Interpolation method to construct a spatial continuity distribution model to improve the spatial resolution of the data. The semivariogram function of the interpolation calculation is set to an exponential model. The Laplace equation is used to calculate the semivariogram. ; Calculate the three-dimensional temperature gradient field, set the boundary conditions as the 3D printer workbench temperature is maintained at 90 ° C, the ambient temperature is 25 ° C, and the finite element method is used to numerically solve the ambient temperature change trend to obtain the temperature distribution curve. For the airflow parameters, the Navier-Stokes equation is used to calculate the flow velocity distribution, where the Navier-Stokes equation is: ,in, represents the flow velocity vector, For pressure, is the fluid density, is the kinematic viscosity, is the external force term, For time, Indicates flow rate The rate of change over time describes the dynamic changes of the fluid over time. The fluid viscosity is set to 1.85×10 -5 Pa·s, the density is set to 1.225kg / m³, and the velocity gradient in the airflow field is calculated. and pressure gradient The airflow gradient field is constructed, and finally the temperature gradient, humidity gradient, airflow gradient and pressure gradient data are combined to generate a three-dimensional environmental gradient field using a three-dimensional interpolation algorithm. Based on the constructed three-dimensional environmental gradient field, the fluid dynamics parameters are calculated, and the large eddy simulation (LES) method is used to solve the eddy structure in the flow field. The mesh is divided using an unstructured tetrahedral mesh, the mesh size is set to 2 mm, and the fluid time step is set to 0.001 s. The Reynolds number of the air flow is calculated: , where the fluid density 1.225kg / m³, speed Set to 0.5m / s, characteristic length Set to 20mm, dynamic viscosity Set to 1.85×10 −5 Pa·s, solve the turbulent motion in the flow field, use k- Turbulence model calculates turbulent energy k and turbulent dissipation rate , set the turbulent Prandtl number , combined with the thermal radiation model to analyze the heat transfer process in the high temperature area, the discrete coordinate method (DOM) is used to calculate the thermal radiation transfer, and the radiation divergence equation is set as ,in is the absorption coefficient, is the radiation flux, is the blackbody radiation intensity, The net transfer rate of radiation energy per unit area per unit time is calculated by combining the flow field, temperature field and thermal radiation field data to simulate the virtual environment field distribution of environmental parameters to obtain the simulated environment field. Based on the simulated environment field, the skeleton slice characteristics are superimposed and analyzed. The Euler method is used to calculate the influence of the simulated environment field on the skeleton slices. The skeleton slice characteristics are discretized in space, and the size of each slice grid unit is set to 1mm. The temperature gradient ∇T, stress gradient ∇σ and airflow gradient ∇u in each grid unit are calculated. The matrix transformation method is used to construct the interaction influence matrix. The matrix elements Representative The skeleton slice feature points are in the simulation environment field The influence of dimension parameters is calculated as follows: ;in are the physical state parameters of the skeleton slice characteristics, including displacement, stress, and deformation. In order to simulate the environmental field parameters, including temperature, flow rate, humidity, etc., the eigenvalue decomposition (EVD) method is used to perform characteristic analysis on the interaction matrix, extract the main influencing factors, and generate the interaction matrix. Based on the interaction matrix, the heat flow-material stress transfer simulation is carried out using the Fourier heat conduction equation. Calculate the temperature transfer inside the material, material density Set to 1.4g / cm³, specific heat Set to 900J / (kg·K), thermal conductivity Set to 0.25W / (m·K), The finite element method is used to numerically solve the heat transfer path inside the model, and Newton's cooling law is used Calculate the surface heat exchange, where the heat transfer coefficient Set to 10W / (m²·K), surface area The value is set to 0.02m². Based on the simulated heat transfer data, the material stress distribution is calculated using the finite element analysis method and Hooke's law is used. Calculate the elastic stress, where the elastic modulus E is set to 3 GPa and the strain Calculated from material deformation, combined with the Von Mises criterion ,in is an equivalent uniaxial stress value, so that the material deformation under multiaxial stress state is equivalent to the deformation under uniaxial tension state. , as well as They represent the normal stress acting along the x-axis, the normal stress acting along the y-axis, and the normal stress acting along the z-axis respectively. The maximum equivalent stress is calculated, the calculation area is adjusted using the boundary condition control method, the boundary constraint is set to a fixed support, and finally the environmental stress data is calculated based on the simulated stress transfer data.

[0042] Preferably, the step S3 of performing deformation scale correlation deduction on skeleton slice features based on environmental stress data includes: Perform stress contact simulation on environmental stress data and skeleton slice features, and perform elastic-plastic tensor mapping based on simulated contact data to generate initial deformation tensor field; The microscopic layer thickness uniformity distribution is fitted to the skeleton slice characteristics to obtain the stable layer thickness data; Conduct heat transfer response simulation on layer thickness stability data, and analyze the thermal inertia of the skeleton material based on the simulated heat transfer response data; Perform material feature matching on material thermal inertia data according to a preset material database to obtain model material data; Assign material property constraints to the initial deformation tensor field based on the model material data and determine the deformation amplitude of the skeleton; Identify the deformation critical point according to the deformation amplitude of the skeleton, and divide the deformation sensitivity of the skeleton based on the deformation critical point; Based on the sensitivity of skeleton deformation, the environmental stress data are correlated to obtain the skeleton stress deformation scale.

[0043] In this embodiment, stress contact simulation is performed on environmental stress data and skeleton slice features, and elastic-plastic tensor mapping is performed based on the simulated contact data to generate an initial deformation tensor field. The environmental stress data is discretized by using a finite element analysis method (FEM) to decompose it into multiple micro-units, and material physical property parameters are applied so that each micro-unit has the elastic-plastic response characteristics of the real material. The skeleton slice feature data is imported into a stress contact calculation model, and the node stress distribution on the skeleton surface is numerically fitted. By setting different contact pressure levels, the contact deformation of the skeleton slice feature under different stresses is calculated, and a stress contact relationship matrix is ​​established. The stress contact relationship matrix is ​​subjected to singular value decomposition (SVD) to extract the principal stress components, and the stress-induced elastic-plastic deformation tensor is calculated through the stress-strain relationship. The tensor interpolation method is used to expand it to the entire skeleton slice area to obtain an initial deformation tensor field, and the microscopic layer thickness uniformity distribution of the skeleton slice feature is fitted to obtain layer thickness stability data. The microscopic layer thickness of the skeleton slice is measured by an optical interferometer (OCT) Tomography is used to perform microscopic scanning on the slice surface to obtain layer thickness distribution data, and a layer thickness distribution model is reconstructed through a three-dimensional reconstruction algorithm. The layer thickness data is compared and analyzed with the theoretical uniform layer thickness standard, and the layer thickness deviation distribution is calculated. The layer thickness distribution is surface fitted by the local mean square error (LMS) optimization method to reduce the influence of measurement error on 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. The thermal transfer response of the layer thickness stability data is simulated, and the thermal inertia of the skeleton material is analyzed based on the simulated thermal transfer response data. The thermal finite element analysis method is used to perform heat transfer modeling on the layer thickness stability data, and the thermal physical properties such as thermal conductivity, specific heat capacity, and density of the material are set. The heat flow of the skeleton slice area is calculated according to the heat conduction equation. Based on the calculated heat flux density distribution, the temperature evolution process of the skeleton material is solved in combination with the time stepping method, and the thermal Inertia parameters, including density, specific heat capacity, thermal conductivity and characteristic size combination parameters, perform material feature matching on the material thermal inertia data according to the preset material database to obtain model material data, use the set of thermal physical property parameters in the material database, including density, thermal conductivity, specific heat capacity and other characteristic parameters, perform material matching through data similarity calculation method, set the error threshold range of material matching, use numerical calculation method to calculate the similarity between the thermal inertia data matrix and the sample data in the material database, select the material parameters with the smallest error as the model material data, and record the matched material property parameters, assign material property constraints to the initial deformation tensor field based on the model material data,And determine the deformation amplitude of the skeleton, use the elastic modulus, yield stress, Poisson's ratio and other parameters obtained by matching the material database to constrain the initial deformation tensor field with material properties, calculate the deformation amplitude through the stress-strain relationship, and solve the skeleton deformation amplitude based on the initial deformation tensor field of the skeleton slice in combination with the material properties. Record the deformation amplitude data, identify the deformation critical point according to the skeleton deformation amplitude, and divide the skeleton deformation sensitivity based on the deformation critical point. Perform local gradient analysis on the deformation amplitude data, calculate the deformation gradient distribution, extract the deformation gradient extreme point, set the critical value of the deformation gradient, screen the deformation gradient mutation area as the deformation critical point, partition the skeleton slice area based on the deformation critical point, classify the high deformation gradient area as the high deformation sensitive area, and classify the low deformation gradient area as the low deformation sensitive area. Establish a skeleton deformation sensitivity matrix, and associate the environmental stress data based on the skeleton deformation sensitivity to obtain the skeleton stress deformation scale. Perform a one-to-one mapping between the skeleton deformation sensitivity matrix and the environmental stress data matrix, use the interpolation method to fit the environmental stress data of different deformation sensitive areas, calculate the skeleton stress deformation scale, and finally output the skeleton stress deformation scale data matrix.

[0044] Preferably, the step S3 of predicting thermal deformation of skeleton slice features according to the skeleton stress deformation scale and environmental stress data includes: Perform heat conduction attenuation simulation based on environmental stress data to obtain temperature gradient change data; Based on the temperature gradient change data, the thermal conduction response of the skeleton stress deformation scale is mapped and the thermal stress distribution matrix is ​​constructed; Fit the thermal field hysteresis effect according to the thermal stress distribution matrix, perform hysteresis compensation, and generate thermal inertia compensation data; Perform interlayer thermal expansion analysis based on thermal inertia compensation data to obtain interlayer expansion data; The deformation trajectory of the skeleton slice features is predicted based on the interlayer expansion data to generate thermal deformation data.

[0045] In this embodiment, for the thermal conduction attenuation simulation of the skeleton slice, the environmental stress data is first obtained and mapped to the finite element grid structure. Assuming that the size of the slice area is 100mm×100mm×5mm, the area is divided into cubic grid units with a size of 1mm×1mm×0.5mm, a total of 50,000 units. The material parameters of each unit include thermal conductivity λ=15W / (m·K), specific heat capacity Cp=500J / (kg·K), density ρ=7800kg / m³. The temperature change of each unit is calculated by time stepping according to Fourier's heat conduction law, and the initial temperature T is set. 0=25℃, a heat source of 300℃ is applied to the unit surface during the simulation, and the temperature field distribution under different time steps Δt=0.01s is calculated. The temperature conduction equation is solved by implicit finite difference method, and the temperature values ​​of each unit at different times are compared. The temperature decay trend over time is calculated, and finally the temperature gradient change data matrix T(i,j,k,t) is formed, where i,j,k represent the spatial index of the grid unit, and t represents the time step. Using the above temperature gradient change data matrix T(i,j,k,t), combined with the thermal expansion coefficient of the material α=1.2×10 -5 K - ¹, calculate the thermal expansion of each grid cell ΔL = α·ΔT·L 0 , where ΔT is the temperature change of the unit, L 0 The initial length is used to solve the thermal stress of the grid unit σ = E α ΔT, where E = 200 GPa is the elastic modulus of the material. The thermal stress of all units is calculated and stored in the thermal stress distribution matrix σ (i, j, k). The matrix is ​​smoothed by the bilinear interpolation method to eliminate the calculation error. Based on the stress concentration area analysis, the units with stress change rate greater than the set threshold are screened out to construct the thermal stress dominant area, and the thermal stress data of all key grids are recorded for subsequent thermal field hysteresis effect calculation. Based on the thermal stress distribution matrix σ (i, j, k), the lag time τ (i, j, k) of the temperature change to the thermal stress response is calculated. The temperature change data T (i, j, k, t) and the stress data σ (i, j, k, t) are cross-correlated by the time correlation analysis method to extract the thermal inertia parameters, including the specific heat capacity Cp = 500 J / (kg·K), the thermal diffusion coefficient D = λ / (ρ·Cp) = 3.85×10 -6 m² / s, heat conduction time constant τc=L² / D, where L is the unit size, according to the calculation τ c =0.26s, combined with the actual test data, the exponential fitting method is used to construct the thermal inertia hysteresis curve, and the hysteresis effect is compensated. The time reversal interpolation method is used to correct the hysteresis stress response to make it synchronized with the temperature change, and finally the thermal inertia compensation data matrix τ is generated. adj(i,j,k) , using the thermal inertia compensation data τadj(i,j,k), combined with the temperature change T(i,j,k,t) and the material thermal expansion coefficient α=1.2×10 -5 K - ¹, calculate the interlayer expansion of each unit ΔL(i,j,k)=α·T(i,j,k,t)·L 0 , where L 0The initial layer thickness is 0.1 mm. The interlayer expansion data is optimized by the surface fitting method to eliminate local calculation errors. The interlayer expansion data matrix ΔL(i, j, k) is constructed by the quadratic interpolation method. Finally, the interlayer expansion trend at different time steps is recorded. Based on the interlayer expansion data matrix ΔL(i, j, k), the geometric shape of the skeleton slice is deformed and simulated. The surface model is established by the non-uniform rational B-splines (NURBS) method. The overall deformation trend of the skeleton slice is calculated by the control point deformation analysis method. The deformation vector is Lagrangian interpolated by the grid deformation technology. The trajectory data of the skeleton deformation is recorded and finally the thermal deformation data matrix ΔD(i, j, k) is generated.

[0046] Preferably, the reverse deformation simulation based on the thermal deformation data described in step S4, and the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure analysis include: Perform vector inversion processing on thermal deformation data and construct an inverse deformation matrix; Recursively mirror transform the inverse deformation matrix until the transformation error of the inverse deformation matrix is ​​lower than a preset error threshold, so as to generate inverse deformation mapping data; Reversely project the thermal deformation data through the reverse deformation mapping data, and build a simulated reverse deformation structure based on the reverse projection data; Perform structural difference comparison on the preset standard printing model and the simulated reverse deformation structure to obtain structural difference data; Perform deformation repair demand analysis based on the structural difference data to obtain the deformation repair requirements.

[0047] In this embodiment, by extracting the position information, temperature change, time series and other data of each point in the original thermal deformation data, a three-dimensional coordinate system of thermal deformation is constructed, and these data are standardized to obtain the displacement vector of each point. The displacement vector information is used to perform vector reversal processing. 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 convert these processed data into a matrix form. Specifically, a multi-dimensional reverse deformation matrix is ​​constructed by assigning the reverse deformation displacement vector of each deformation point to the corresponding matrix element. Each row of the matrix represents the reverse displacement of a coordinate point in each coordinate axis direction, and each column of the matrix represents the deformation of each point in different directions, thereby forming a preliminary structure of the reverse deformation matrix, determining the initial reverse deformation matrix and setting the error threshold. The operation of the mirror transformation is to reverse the data in the matrix according to the symmetry axis, reversely project the data and recalculate the deformation result. After each mirror transformation, the error value after the current transformation will be calculated by regression analysis method and compared with the preset error threshold. If the error value is greater than the threshold, the next round of recursive mirror transformation will continue until the error value is less than the threshold. The error threshold can be set according to actual needs, such as 0.01 mm, after multiple rounds of transformation, an accurate reverse deformation mapping data is gradually obtained. In 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, and finally reach the preset error standard. Through this recursive transformation method, the reverse deformation mapping data gradually approaches the actual standard deformation. After obtaining the standard reverse deformation data through the reverse deformation mapping data, when the thermal deformation data is reversely projected, the deformation data and the thermal deformation data are first mapped in three-dimensional space, and the reverse deformation mapping data is projected back to the original thermal deformation data space. Through this process, the reverse structure of the thermal deformation is calculated according to the reverse deformation matrix, and these reverse structure points are mapped to the new three-dimensional coordinate system according to the original geometric form. With the help of the reverse projection method, a simulated reverse deformation structure is built. The position and deformation degree of each point in the structure are accurately adjusted according to the projection data. The construction of the simulated reverse deformation structure not only involves the position adjustment of the points, but also needs to deal with the relationship between the points to ensure that the thermal deformation compensation of each area is coherent and avoid local incoordination. When performing differential comparison between the preset standard printing model and the simulated reverse deformation structure, the geometric data of the simulated reverse deformation structure and the standard printing model are first compared point by point to calculate the geometric difference data between the two. The difference data is calculated by performing difference calculation on the spatial coordinates of each corresponding point to obtain the difference in each axis. The difference data is not only the position difference, but also includes the degree of deformation and various dimensional errors. After careful calculation, the structural difference data is obtained. The calculation of the difference data requires special attention to its accuracy. The error calculation must be performed at the micron level to ensure that every subtle geometric deviation can be reflected. When performing deformation repair demand analysis based on the structural difference data, first, the repair priority of each point is generated based on the structural difference data. The evaluation process of the repair demand includes weighted processing of the difference data, giving priority to the area with larger errors, using the known compensation algorithm, and designing the thermal deformation compensation area through calculation, and performing in-depth analysis on these areas 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. .

[0048] Preferably, planning the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements in step S4 includes: Determine the required deformation area according to the deformation repair requirements; Matching the skeleton stress deformation scale corresponding to the region based on the required deformation region; Extract the required deformation amplitude required for deformation repair; Based on the required deformation amplitude, the skeleton stress deformation scale corresponding to the region is converted into a required stress to obtain a deformation required stress; The compensation path is deduced based on the deformation required stress and required deformation area to generate the deformation compensation path.

[0049] In this embodiment, the thermal deformation data and the structural difference data are analyzed, and the deformation area is located in combination with the deformation repair requirements. The determination of the deformation area depends on the accurate division of the model geometry, and the area with a larger error value is calculated based on the difference data. The area with an error value greater than a preset threshold is defined as the required deformation area, and the error threshold is set to 0.02 mm. Furthermore, through the geometric comparison between the thermal deformation data and the printed model, the displacement of each point is evaluated using the three-dimensional space coordinate system, and the geometric boundaries of the deformation area are calculated. These boundaries include parameters such as the maximum length, width, and depth of the deformation range. The deformation area should take into account the uniformity of the overall model structure, and exclude small deformations in smaller areas to ensure The accuracy and range of the repair area meet the actual repair needs. In this process, numerical modeling tools can be used to spatially segment the model, accurately extract deformation data and locate the target area. According to the required deformation area, the skeleton stress deformation scale corresponding to the area is matched. First, the skeleton information of the deformation area in the model is extracted. The skeleton information is three-dimensionally reconstructed through the detailed structure of the 3D printed model to obtain the stress state of each point of the skeleton under the action of external force. The skeleton stress deformation scale reflects the stress distribution of the deformation area under the stress state. By analyzing the stress data of the skeleton nodes, several points with the largest stress values ​​in the deformation area are extracted. Subsequently, these stress points are compared with the geometric positions of the required deformation area. The matching process is achieved through stress calculation and regional geometric analysis. The skeleton stress deformation scale will be expressed as stress value per unit volume, for example, in 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 deformation area is quantified. The specific steps are to use the error calculation method to calculate the error amplitude of each point in the deformation area. For example, the error of a certain deformation point is 0.02 mm, which indicates that the point needs to be repaired by 0.02 mm. The error data of all error points are analyzed to determine the required deformation amplitude. Deformation amplitude, 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 deformation area as the reference value of the required deformation amplitude, and apply the reference value to the entire area. The deformation amplitude is related to the number of points in the deformation area and the error distribution. According to the distribution of the error value, the overall repair magnitude of the deformation area is given, and the deformation amplitude required for the area is calculated. After determining the required deformation amplitude, when performing stress conversion on the required deformation amplitude, firstly, 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 to perform stress conversion. It is assumed that in a specific deformation area, the required deformation amplitude is 0.02 mm, and the skeleton stress deformation scale in this area is 30 MPa. Through the stress conversion formula, the deformation demand stress in this area is 15 MPa. The conversion process depends on the proportional relationship between stress and deformation amplitude. This ratio is obtained by analyzing the physical properties of the material, and ensures that the conversion process meets the material characteristics and structural safety standards. When the compensation path is deduced based on the deformation demand stress and the required deformation area, first, the required repair path for each deformation point is calculated by combining the required stress and the geometric characteristics of the deformation area. The core of the compensation path deduction is to determine how to evenly repair the entire deformation area through the compensation process. The deduction process uses a numerical optimization algorithm to convert the required stress distribution into a specific route of the compensation path. By simulating the mechanical state in the deformation area, the path planning algorithm is used to gradually generate the optimal compensation path. The generation of the compensation path also needs to consider factors such as the fluidity and adhesion of the material and the capacity of the printing equipment. Finally, through these parameters and path planning, a compensation path that can minimize the deformation error is generated. The compensation path specifically includes the compensation amount, direction and compensation order of each point, and finally forms a path that can guide the compensation operation in the actual printing process. .

[0050] It is particularly important that the compensation path deduction based on the deformation requirement stress and the required deformation area includes: Decomposing the deformation demand stress into stress field tensor to obtain multi-dimensional stress components; Carry out grid discrete mapping on the required deformation area to generate regional grid nodes; The multi-dimensional stress components and regional grid nodes are superimposed and fused to obtain the node stress distribution data; Construct a gradient field based on the node stress distribution data, and perform isosurface segmentation based on the gradient field to generate stress hierarchy data; A transfer path is constructed based on stress hierarchy data, and deviation compensation correction is performed based on the transfer path and stress hierarchy data to generate a deformation compensation path.

[0051] In this embodiment, when performing stress field tensor decomposition on the deformation demand stress, the stress condition of the target 3D printed model is first measured, and a method based on finite element analysis (FEA) is selected to simulate the mechanical response of the model. After setting the boundary conditions, material parameters and loading methods, the finite element solver is used to calculate the stress distribution of the model under different stress 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 main axis directions. In order to more intuitively analyze the stress distribution characteristics, the stress tensor is decomposed by eigenvalue (Eigenvalue Decomposition, calculate the eigenvalue and eigenvector of the tensor. The eigenvalue represents the main stress intensity inside the model, and the eigenvector is used to describe the main stress direction in each direction. The stress tensor is decomposed by numerical iteration methods, such as QR decomposition, to extract the main stress components and shear stress components in each direction of the model, and finally obtain a set of multi-dimensional stress component data. When performing grid discrete mapping on the required deformation area, first determine the area where deformation may occur in the 3D printing model, divide these areas into multiple discrete small units, and use triangulation (Delaunay The grid structure is generated by the triangulation method to ensure that the shape of the grid unit is regular and meets the requirements of calculation accuracy. In the process of grid division, the grid density of different areas is adaptively adjusted, and smaller grid units are used in high stress areas to improve the calculation accuracy. The coordinate information of the grid nodes is stored in a three-dimensional coordinate format. Each node records its spatial position (x, y, z). At the same time, a topological relationship table is established to record the connection relationship between each node. The octree data structure is used to store and manage the grid nodes to improve the subsequent calculation efficiency. After the gridding process is completed, a regional grid node data set containing spatial coordinates, topological relationships and preliminary stress information is obtained. When the multi-dimensional stress components and regional grid nodes are superimposed and fused, The multidimensional stress components obtained in the previous step are mapped to the corresponding grid nodes, and the corresponding stress data are assigned to each grid node. The weighted interpolation method is used 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 stress points. The trilinear interpolation method is used for numerical calculation to ensure the smooth transition of the stress data on the grid. After the stress mapping is completed, the data of each grid node is stored, including the node coordinates, local stress values ​​and their direction information, and finally the node stress distribution data is formed. When constructing the gradient field according to the node stress distribution data, the stress gradient between the grid nodes is first calculated. The gradient calculation is based on the rate of change of the stress value in space. The central difference method is used to calculate the gradient values ​​in each direction within the grid.Each grid unit calculates the gradient components of the three main directions and stores the gradient vector data. Subsequently, based on the calculated gradient field data, the Marching Cubes algorithm is used to extract the isosurface, and the stress distribution data is divided into different stress level areas. During the isosurface segmentation process, different stress thresholds are set, each threshold corresponds to a stress level, and the stress distribution inside the model is divided into multiple continuous areas, and stress level data is generated. When constructing the transfer path based on the stress level data, the boundary lines of each stress level are first extracted, and the path of the boundary line is optimized using the graph theory method. The path of the high stress area is connected with the path of the low stress area. The shortest path algorithm (such as the Dijkstra algorithm) is used to determine the transfer path between the levels. At the same time, the directionality of the stress gradient is considered to ensure the smooth transition of the path. After the path is constructed, the deviation compensation correction is performed according to the stress level data, the local deformation deviation of the model is calculated, and the 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 the deformation adjustment is performed along the transfer path. The calculation accuracy of the deformation compensation adjustment is set to 0.02 mm, and the deformation compensation path is finally obtained. ,

[0052] Preferably, step S5 comprises the following steps: Step S51: discretizing the deformation compensation path to obtain a set of path control points; simulating deformation compensation on the model feature skeleton based on the set of path control points to generate a simulation compensation process; Step S52: identifying a deformation compensation target area during the compensation process; and analyzing a non-target area during the compensation process based on the deformation compensation target area; Step S53: performing deformation comparison of the non-target area before and after compensation according to the model feature skeleton to obtain the non-target deformation; Step S54: backtracking the compensation process based on the non-target deformation, and determining the initial time point of the non-target deformation based on the backtracking compensation process; analyzing the cause of the non-target deformation according to the initial time point of the non-target deformation; Step S55: simulating the deformation compensation path based on the non-target deformation inducement, and performing conflict detection on the simulated adjustment compensation path to generate a compensation path change conflict collection, wherein the step length of the path adjustment simulation is limited to 0.5 mm, the conflict detection standard is that the part of the compensation path that is not more than 2 mm apart is regarded as the conflict area, and the basis for generating the conflict collection is that the cumulative value of the error between the paths is greater than 0.05 mm; Step S56: adjusting the deformation compensation path according to the non-target deformation inducement and the compensation path change conflict set to obtain an optimized feature compensation method.

[0053] In this embodiment, when the deformation compensation path is kinematically discretized, the model path needs to be cut first. During the processing, the compensation path is first decomposed into multiple small segments, and the length of each small segment is controlled by a set kinematic step length. In this embodiment, the kinematic step length 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, the deformation compensation simulation of the model feature skeleton is performed based on these control points. In the simulation process, the finite element method (Finite Element The simulation software of 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 result of the entire compensation process. The accuracy of the compensation simulation process is strictly controlled within 0.01 mm. When identifying the deformation compensation target area in the compensation process, the deformation data in the compensation model is first used to determine the threshold of the deformation variable. The recognition standard of the compensation 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 compensation target area. 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 compensation process. The non-target area refers to the area with a deformation amplitude less than 0.5 mm. These areas do not involve compensation and need to be distinguished from the target area for processing. When comparing the deformation before and after the non-target area, firstly, the deformation data before and after compensation are compared according to the model feature skeleton. The deformation data includes the displacement of each control point. When comparing, the accuracy control standard needs to be used. The error tolerance of the deformation amplitude is set to 0.02 mm. Only when the error is greater than this value, it is considered as a valid deformation difference, thereby obtaining the non-target deformation data. When backtracking the compensation process based on the non-target deformation, the time range of the backtracking needs to be determined first. The time period of the backtracking is limited to the first 200 steps of the compensation process, and the error tolerance of the backtracking is 0.02 seconds, to ensure the timeliness of the compensation path. When determining the initial time point of the non-target deformation, the deformation error back-calculation method is used. Through reverse calculation, the occurrence time of the non-target deformation is determined, and combined with the time step of the deformation process, the initial time point of the backtracking is finally determined. When further analyzing the causes of non-target deformation, based on the known deformation data, combined with external factors such as temperature changes, printing material properties and other factors, the deformation inducement is analyzed, and the calculation method adapted to the physical properties of the model is used to infer the cause of the non-target deformation. When simulating the path adjustment of the compensation path based on the non-target deformation inducement, the compensation path is first simulated segment by segment, and the path is adjusted according to the inducement of the 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 Conflict detection is performed. During the conflict detection process, the relative error between the compensation paths is detected. If the distance between the two paths is less than 2 mm, it is considered that a conflict has occurred. The generation standard of the conflict collection is that the cumulative error between the paths is greater than 0.05 mm. The conflict area will be included in the priority processing area for subsequent path adjustment. When optimizing the deformation compensation path, the path adjustment is performed according to the non-target deformation inducement and the compensation path change conflict collection. The goal of path adjustment is to enable the compensation path to avoid the conflict area and minimize the impact of non-target deformation. In the process of optimizing the path adjustment, the adjustment range of the path is set to within 1 mm to ensure the accuracy of the adjusted path. The final generated optimized feature compensation method is based on the adjusted compensation path for actual printing, and finally the optimization of the entire compensation path is achieved. .

[0054] The present invention also provides a 3D printing model slicing feature compensation system, which is used to execute the 3D printing model slicing feature compensation method as described above. The 3D printing model slicing feature compensation system includes: The model skeleton extraction module is used to collect the 3D printing model; split the model feature skeleton of the 3D printing model; perform horizontal slice mapping on the model feature skeleton to generate skeleton slice features; Environmental stress simulation module, used to collect 3D printing environment; simulate virtual environment field according to 3D printing environment, and perform environmental stress analysis on skeleton slice features based on simulated environment field to generate environmental stress data; The deformation scale deduction module 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; perform thermal deformation prediction on the skeleton slice features based on the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; The reverse deformation repair module is used to simulate the reverse deformation 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; and plan the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; The deformation compensation optimization module is used to simulate the compensation of 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 deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

[0055] The present invention can systematically collect and split the 3D printing model through the introduction of the model skeleton extraction module, so that the subsequent analysis has detailed structural information. The skeleton slice features generated by the transverse slice mapping provide the application with operable detail data. The function of the environmental stress simulation module realizes a comprehensive scan of the 3D printing environment and effectively identifies the impact of environmental factors on the model during the printing process. The virtual environment field simulation can non-destructively judge the performance under the construction conditions and provide 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 deformation scale deduction module can effectively evaluate the deformation of the model under different conditions based on the correlation deduction of the environmental stress data. 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 the deformation situation and the accurate analysis of the repair requirements. The planning of the deformation compensation path provides a targeted repair strategy. The deformation compensation optimization module innovatively avoids the 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, and improves the finished product quality and production efficiency of the 3D printed model as a whole.

[0056] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed, the 3D printing model slicing feature compensation method as described in any one of the above items is implemented.

[0057] The present invention can provide an efficient storage and operating environment for the implementation of the 3D printing model slice feature compensation method through the introduction of computer-readable storage media. The stored computer program ensures the reusability and efficient calling of the method. The programmed execution process improves the accuracy and consistency of the overall operation and can quickly respond to the processing requirements of different printing models. The optimized computer program realizes the rapid processing of complex calculations and supports the analysis and compensation of various 3D printing model features, saving users a lot of time and energy. The flexible program architecture makes it easy to expand and upgrade functions in the future. The systematic and orderly programming logic reduces the risk of human operation errors and ensures that the model is accurately verified and optimized before printing, thereby improving the quality and stability of the final product. It provides strong technical support and guarantee for the popularization and application of 3D printing technology, and overall enhances the intelligence level and automation level of the production process.

[0058] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0059] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A 3D printing model slice feature compensation method, characterized in that: The following steps are involved: Step S1: collecting a 3D printed model; splitting a model feature skeleton of the 3D printed model; performing horizontal slice mapping on the model feature skeleton to generate skeleton slice features; Step S2: collecting the 3D printing environment; performing virtual environment field simulation according to the 3D printing environment, and performing environmental stress analysis on the skeleton slice characteristics based on the simulated environment field to generate environmental stress data; Step S3: performing deformation scale correlation deduction on the skeleton slice features based on the environmental stress data to obtain the skeleton stress deformation scale; Thermal deformation prediction is performed on the skeleton slice characteristics according to the skeleton stress deformation scale and environmental stress data to generate thermal deformation data; Step S4: performing reverse deformation simulation based on thermal deformation data, and analyzing deformation repair requirements based on a preset standard printing model and simulated reverse deformation structure; Plan the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; Step S5: performing compensation simulation on the model feature skeleton according to the deformation compensation path to obtain a simulated compensation process; analyzing the non-target deformation in the simulated compensation process, and adjusting 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, characterized in that: Step S1 includes the following steps: Step S11: collecting a 3D printing model; collecting high-precision point cloud data of the 3D printing model to obtain original point cloud data; Step S12: gridding and reconstructing the original point cloud data, and performing smoothing processing to generate a refined grid structure; Step S13: extracting the center line of the refined grid structure, and performing model feature skeleton extraction on the refined grid structure based on the center line; Step S14: performing multi-directional raster encoding on the model feature skeleton to obtain a skeleton pixel grid; performing inertial principal axis positioning on the skeleton pixel grid, and performing inertial principal axis calibration on the inertial principal axis to obtain a directional slice coordinate system; Step S15: Perform dynamic step-length slicing processing on the oriented slicing coordinate system to generate a slice cross-sectional contour; perform feature key point extraction on the slice cross-sectional contour to generate a skeleton slicing feature.

3. The 3D printing model slice feature compensation method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: collecting 3D printing environment; identifying environmental parameter distribution according to the 3D printing environment; interpolating the environmental parameter distribution and constructing a three-dimensional environmental gradient field; Step S22: Calculate the fluid dynamics parameters of the three-dimensional environmental gradient field, during which the fluid viscosity is set to 1.85×10-5Pa·s and the density is set to 1.225kg / m³; perform virtual environmental field simulation on the environmental parameter distribution based on the fluid dynamics parameters to obtain a simulated environmental field; Step S23: superimpose and analyze the simulation environment field and skeleton slice features, and construct an interaction influence matrix; Step S24: performing heat flow-material stress transfer simulation based on the interaction influence matrix to generate simulated stress transfer data; and determining 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 skeleton slice features based on environmental stress data in step S3 includes: Perform stress contact simulation on environmental stress data and skeleton slice features, and perform elastic-plastic tensor mapping based on simulated contact data to generate initial deformation tensor field; The microscopic layer thickness uniformity distribution is fitted to the skeleton slice characteristics to obtain the stable layer thickness data; Conduct heat transfer response simulation on layer thickness stability data, and analyze the thermal inertia of the skeleton material based on the simulated heat transfer response data; Perform material feature matching on material thermal inertia data according to a preset material database to obtain model material data; Assign material property constraints to the initial deformation tensor field based on the model material data and determine the deformation amplitude of the skeleton; Identify the deformation critical point according to the deformation amplitude of the skeleton, and divide the deformation sensitivity of the skeleton based on the deformation critical point; Based on the sensitivity of skeleton deformation, the environmental stress data are correlated to obtain the skeleton stress deformation scale.

5. The 3D printing model slice feature compensation method according to claim 1, characterized in that: The step S3 of predicting thermal deformation of skeleton slice features according to the skeleton stress deformation scale and environmental stress data includes: Perform heat conduction attenuation simulation based on environmental stress data to obtain temperature gradient change data; Based on the temperature gradient change data, the thermal conduction response of the skeleton stress deformation scale is mapped and the thermal stress distribution matrix is ​​constructed; Fit the thermal field hysteresis effect according to the thermal stress distribution matrix, perform hysteresis compensation, and generate thermal inertia compensation data; Perform interlayer thermal expansion analysis based on thermal inertia compensation data to obtain interlayer expansion data; The deformation trajectory of the skeleton slice features is predicted based on the interlayer expansion data to generate thermal deformation data.

6. The 3D printing model slice feature compensation method according to claim 1, characterized in that: The reverse deformation simulation based on the thermal deformation data described in step S4, and the deformation repair requirements based on the preset standard printing model and the simulated reverse deformation structure analysis include: Perform vector inversion processing on thermal deformation data and construct an inverse deformation matrix; Recursively mirror transform the inverse deformation matrix until the transformation error of the inverse deformation matrix is ​​lower than a preset error threshold, so as to generate inverse deformation mapping data; Reversely project the thermal deformation data through the reverse deformation mapping data, and build a simulated reverse deformation structure based on the reverse projection data; Perform structural difference comparison on the preset standard printing model and the simulated reverse deformation structure to obtain structural difference data; Perform deformation repair demand analysis based on the structural difference data to obtain the deformation repair requirements.

7. The 3D printing model slice feature compensation method according to claim 1, characterized in that: Planning the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements in step S4 includes: Determine the required deformation area according to the deformation repair requirements; Matching the skeleton stress deformation scale corresponding to the region based on the required deformation region; Extract the required deformation amplitude required for deformation repair; Based on the required deformation amplitude, the skeleton stress deformation scale corresponding to the region is converted into a required stress to obtain a deformation required stress; The compensation path is deduced based on the deformation required stress and required deformation area to generate the deformation compensation path.

8. The 3D printing model slice feature compensation method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: discretizing the deformation compensation path to obtain a set of path control points; simulating deformation compensation on the model feature skeleton based on the set of path control points to generate a simulation compensation process; Step S52: identifying a deformation compensation target area during the compensation process; and analyzing a non-target area during the compensation process based on the deformation compensation target area; Step S53: performing deformation comparison of the non-target area before and after compensation according to the model feature skeleton to obtain the non-target deformation; Step S54: backtracking the compensation process based on the non-target deformation, and determining the initial time point of the non-target deformation based on the backtracking compensation process; analyzing the cause of the non-target deformation according to the initial time point of the non-target deformation; Step S55: simulating the deformation compensation path based on the non-target deformation inducement, and performing conflict detection on the simulated adjustment compensation path to generate a compensation path change conflict collection, wherein the step length of the path adjustment simulation is limited to 0.5 mm, the conflict detection standard is that the part of the compensation path that is not more than 2 mm apart is regarded as the conflict area, and the basis for generating the conflict collection is that the cumulative value of the error between the paths is greater than 0.05 mm; Step S56: adjusting the deformation compensation path according to the non-target deformation inducement and the compensation path change conflict set to obtain an optimized feature compensation method.

9. A 3D printing model slice feature compensation system, characterized in that: Used to perform the 3D printing model slice feature compensation method according to claim 1, the 3D printing model slice feature compensation system comprises: The model skeleton extraction module is used to collect the 3D printing model; split the model feature skeleton of the 3D printing model; perform horizontal slice mapping on the model feature skeleton to generate skeleton slice features; Environmental stress simulation module, used to collect 3D printing environment; simulate virtual environment field according to 3D printing environment, and perform environmental stress analysis on skeleton slice features based on simulated environment field to generate environmental stress data; The deformation scale deduction module 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; perform thermal deformation prediction on the skeleton slice features based on the skeleton stress deformation scale and the environmental stress data to generate thermal deformation data; The reverse deformation repair module is used to simulate the reverse deformation 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; and plan the deformation compensation path according to the skeleton stress deformation scale and deformation repair requirements; The deformation compensation optimization module is used to simulate the compensation of 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 deformation compensation path based on the non-target deformation to obtain an optimized feature compensation method.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the 3D printing model slicing feature compensation method as described in any one of claims 1 to 8 is implemented.

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