Deformation compensation method for 3D printing model of oral implantation guide plate

Abstract

The invention discloses a deformation compensation method for a 3D printing model of an oral implanting guide plate, and belongs to the technical field of computer-aided design and manufacturing. The method comprises the steps that original three-dimensional model data of the oral implanting guide plate is obtained; performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data, and generating a dynamic stress evolution field for describing the internal stress distribution and change of the model in the virtual printing process; based on the dynamic stress evolution field, generating an adaptive compensation transformation matrix for correcting geometric deformation; and performing compensation transformation on the original three-dimensional model data by using the self-adaptive compensation transformation matrix to generate high-fidelity compensation model data. A self-adaptive compensation transformation matrix is generated through dynamic hierarchical stress accumulation analysis to preprocess three-dimensional model data, complex deformation in the printing process can be accurately predicted and compensated, and therefore the forming precision of a printed piece is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of computer-aided design and manufacturing technology, and in particular to a deformation compensation method for a 3D printed model of a dental implant guide. Background Technology

[0002] Dental implant guides are a key auxiliary tool in modern digital dental implant restoration. Based on the implant position and angle designed by preoperative planning software, they provide precise guidance for implant preparation. With the development of 3D printing technology, also known as additive manufacturing, dental implant guides can be rapidly and individually manufactured using patient oral scan data and 3D model data generated by computer-aided design software, becoming a mainstream trend in clinical application.

[0003] In existing technologies, to address the deformation of models caused by material thermal shrinkage and residual stress during 3D printing, various data processing methods are typically employed for compensation. A common approach is to uniformly scale the original 3D model data before printing to offset the average shrinkage rate of the material. Some methods utilize computers to perform static structural mechanics analysis on the final printed model, predicting its overall stress distribution and making certain geometric adjustments accordingly.

[0004] However, the aforementioned existing technologies have significant drawbacks. Simple overall scaling cannot handle asymmetric structures like dental implant guides, which have complex curved surfaces and uneven wall thicknesses, because their actual deformation is highly uneven across different regions. Furthermore, static stress analysis based on the final shape ignores the dynamic process of layer-by-layer 3D printing, failing to capture the crucial physical phenomena of continuous heat input and dissipation, and the accumulation and interaction of stress layer by layer during printing. Therefore, the predicted deformation morphology deviates significantly from the actual situation, resulting in poor compensation effects, and the printed guides still fall short of the high-precision fitting requirements of clinical practice. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a deformation compensation method for a 3D printed model of an oral implant guide. This method employs dynamic hierarchical stress accumulation analysis to generate an adaptive compensation transformation matrix for preprocessing the 3D model data. This approach accurately predicts and compensates for complex deformations during the printing process, thereby significantly improving the forming accuracy of the printed parts.

[0006] The above objectives can be achieved through the following approach: A deformation compensation method for a 3D printed model of an oral implant guide includes: acquiring original three-dimensional model data of the oral implant guide; performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during virtual printing; generating an adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field; and applying the adaptive compensation transformation matrix to perform compensation transformation on the original three-dimensional model data to generate high-fidelity compensation model data.

[0007] Optionally, obtaining the original three-dimensional model data of the dental implant guide includes: receiving a design file generated from oral scan data and implant planning software; parsing and extracting a three-dimensional mesh or surface representation from the design file; verifying the geometric integrity of the parsed three-dimensional mesh or surface representation; and generating the original three-dimensional model data.

[0008] Optionally, the step of performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during virtual printing includes: performing virtual layering processing on the original three-dimensional model data to obtain layered model data; obtaining the corresponding hierarchical thermo-coupling parameters for each layer in the layered model data; and iteratively calculating the stress distribution of the model based on the stress accumulation results of the processed layers and the hierarchical thermo-coupling parameters of the current layer until all layers have been processed, thereby generating a dynamic stress evolution field.

[0009] Optionally, obtaining the corresponding hierarchical thermo-coupling parameters for each layer in the hierarchical model data includes: analyzing the three-dimensional geometry of the current layer and extracting the corresponding local geometric feature parameters; obtaining the material physical properties and equipment operating parameters related to the 3D printing process and generating process physical parameters; and combining the local geometric feature parameters and the process physical parameters to generate hierarchical thermo-coupling parameters.

[0010] Optionally, generating an adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field includes: calculating the final predicted deformation shape of the model according to the final state of the dynamic stress evolution field after virtual printing; using the original three-dimensional model data as a reference target and the final predicted deformation shape as a registration source, calculating a transformation relationship that can map the final predicted deformation shape back to the original three-dimensional model data, and obtaining the adaptive compensation transformation matrix.

[0011] Optionally, obtaining the adaptive compensation transformation matrix includes: extracting the connection relationships and neighborhood curvature information between vertices based on the original 3D model data; generating local surface topological constraints according to the connection relationships and neighborhood curvature information; using the original 3D model data as a reference target and the final predicted deformation shape as the registration source, applying the local surface topological constraints to maintain surface smoothness, thereby calculating the transformation relationship and obtaining the adaptive compensation transformation matrix.

[0012] Optionally, applying the adaptive compensation transformation matrix to the original 3D model data to generate high-fidelity compensated model data includes: obtaining the geometric control point set of the original 3D model data; applying the adaptive compensation transformation matrix to the geometric control point set to generate a compensated vertex coordinate set; and reconstructing the model surface based on the compensated vertex coordinate set to generate high-fidelity compensated model data.

[0013] Optionally, after generating the high-fidelity compensation model data, the method further includes: performing virtual fitting verification between the high-fidelity compensation model data and preset digital impression data representing the patient's oral cavity structure, and generating a fitting accuracy assessment report; determining whether the high-fidelity compensation model data meets preset clinical accuracy requirements based on the fitting accuracy assessment report; if the high-fidelity compensation model data does not meet the clinical accuracy requirements, extracting deviation data from the fitting accuracy assessment report; correcting the process physical parameters based on the deviation data, and recalculating the high-fidelity compensation model data.

[0014] Optionally, after generating the high-fidelity compensation model data, the method further includes: identifying key structural regions based on the geometric shape of the high-fidelity compensation model data and optimizing the corresponding printing parameters to generate optimized printing path data; and correcting the equipment operating parameters according to the optimized printing path data.

[0015] Based on the same inventive concept, this invention also provides a deformation compensation system for a 3D printed model of an oral implant guide. The system includes: a model input module for acquiring the original three-dimensional model data of the oral implant guide; a stress analysis module for performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during virtual printing; a matrix generation module for generating an adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field; and a compensation transformation module for applying the adaptive compensation transformation matrix to perform compensation transformation on the original three-dimensional model data to generate high-fidelity compensation model data.

[0016] Compared with the prior art, the present invention has the following advantages: 1. This invention generates a dynamic stress evolution field by performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data, and creates an adaptive compensation transformation matrix accordingly, thereby realizing the prediction and compensation of printing deformation. This method surpasses traditional static analysis or simple geometric scaling, and can simulate the complex physical process of thermal stress accumulating layer by layer during the printing process, thereby dealing with non-uniform warping and shrinkage caused by complex geometric shapes, and improving the dimensional accuracy and morphological fidelity of the final printed product. 2. In the process of generating the adaptive compensation transformation matrix, the present invention introduces local surface topological constraints. This collaborative optimization strategy, which combines physical deformation compensation with geometric shape preservation, ensures that the high-fidelity compensation model data after compensation is not only corrected in terms of macroscopic dimensions, but also maintains the original smoothness and geometric features of its surface. This avoids the surface wrinkles or distortions that may be introduced by simple reverse displacement compensation, and ensures the precise fit and biocompatibility of the dental implant guide with the contact surface of the patient's oral soft and hard tissues. 3. This invention compares the compensated model data with digital impression data representing the patient's actual oral structure, and iteratively corrects the process parameters based on deviation feedback. It directly links the accuracy of simulation compensation with the final clinical application goal. Through self-correction, it improves the robustness and reliability of the entire compensation scheme, ensuring that the model has reached the clinical accuracy requirements before physical printing, thereby improving the success rate of the first printing and reducing manufacturing costs.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a deformation compensation method for a 3D printed model of an oral implant guide according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the stress dynamic accumulation process according to an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram illustrating the relationship between the incremental and cumulative layered stress in an embodiment of the present invention.

[0022] Figure 4 This is a comparison of cross-sectional shape changes in embodiments of the present invention.

[0023] Figure 5 This is a schematic diagram of the deformation compensation system of a 3D printed model of an oral implant guide plate according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Reference Figure 1 One embodiment of the present invention proposes a deformation compensation method for a 3D printed model of an oral implant guide. The method uses dynamic hierarchical stress accumulation analysis to generate an adaptive compensation transformation matrix to preprocess the three-dimensional model data, which can accurately predict and compensate for complex deformations during the printing process, thereby significantly improving the forming accuracy of the printed parts.

[0026] The method described in this embodiment specifically includes: Obtain the original 3D model data of the dental implant guide; Dynamic hierarchical stress accumulation analysis is performed on the original three-dimensional model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during the virtual printing process; Based on the dynamic stress evolution field, an adaptive compensation transformation matrix for correcting geometric deformation is generated; The original three-dimensional model data is compensated and transformed using the adaptive compensation transformation matrix to generate high-fidelity compensated model data.

[0027] This invention uses virtual printing simulation to predict and proactively counteract physical deformation during the 3D printing process. First, a dynamic layer-by-layer stress accumulation analysis is performed on the ideal original 3D model data. This analysis does not statically evaluate the final shape but simulates the complete process of material deposition, solidification, and cooling layer by layer, thus constructing a dynamic stress evolution field. This evolution field completely records the spatiotemporal history of stress within the model from nothing to something, continuously accumulating and redistributing. Subsequently, based on the simulation results of this physical process, the system generates a corresponding adaptive compensation transformation matrix. This matrix is ​​essentially a spatially varying, non-rigid inverse displacement field, capable of applying inverse geometric transformations to different regions of the model based on predicted deformation trends and amplitudes. Finally, this compensation transformation matrix is ​​applied to the original 3D model to generate high-fidelity compensated model data that is geometrically pre-distorted.

[0028] This invention improves the molding accuracy and fidelity of 3D printing of dental implant guides. Through profound insight and precise prediction of dynamic stress evolution during the printing process, this method overcomes the limitations of traditional compensation techniques, which can only handle simple, uniform shrinkage. It addresses complex deformation problems such as warping and torsion caused by complex geometries and uneven thermal histories. Ultimately, when using the high-fidelity compensation model data generated by this method for physical printing, the actual deformation during printing cancels out the model's pre-set geometric compensation, ensuring that the final cooled and molded guide closely matches its original design intent. This guarantees excellent fit between the guide and the patient's oral structure, thereby ensuring the precision of implant surgery and clinical success rates, and improving the reliability of the entire digital treatment process.

[0029] Optionally, obtaining the original three-dimensional model data of the dental implant guide includes: Receive design files generated from oral scan data and implant planning software; The 3D mesh or surface representation is parsed and extracted from the design file; Perform geometric integrity verification on the parsed 3D mesh or surface representation to generate the original 3D model data.

[0030] Specifically, the method first receives design files generated from oral scan data and implant planning software via a standard data interface. These design files are typically in 3D mesh formats such as STL and OBJ, or surface representation formats such as STEP and IGES, and contain the geometric information of the guide plate. Next, the method parses the received design file, reads and extracts the core geometric data according to its file format specifications, and constructs a 3D mesh or surface representation in computer memory. For a 3D mesh, this representation consists of a set of vertex coordinates and an index list defining how these vertices connect to form triangular facets; for a surface representation, it is defined by a set of control points, node vectors, and weight coefficients. Finally, to ensure the accuracy and stability of subsequent stress analysis, the geometric integrity of the parsed 3D mesh or surface representation is verified. This verification process mainly includes checking the model's topology to ensure it is a closed, non-self-intersecting manifold entity. Verification includes checking for holes, non-manifold edges, overhanging surfaces, and whether the surface normals consistently face outwards. After the above verification and necessary automatic repair, the generated data is a geometrically complete and topologically correct original 3D model data, which provides a reliable basis for subsequent accurate dynamic hierarchical stress accumulation analysis.

[0031] Optionally, the step of performing dynamic hierarchical stress accumulation analysis on the original three-dimensional model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during virtual printing includes: The original 3D model data is subjected to virtual layering processing to obtain layered model data; For each layer in the hierarchical model data, obtain the corresponding hierarchical thermal coupling parameters; Based on the stress accumulation results of the processed layers and the layer-level thermo-coupling parameters of the current layer, the stress distribution of the model is iteratively calculated until all layers are processed, thereby generating a dynamic stress evolution field.

[0032] Specifically, such as Figure 2The stress dynamic accumulation process shown describes a virtual layering process for performing dynamic hierarchical stress accumulation analysis on the original 3D model data. This process first cuts the original 3D model data along the 3D printing construction direction into a set of two-dimensional cross-sectional contours with a preset layer thickness. This set constitutes the layered model data. Subsequently, the system enters an iterative calculation loop, starting from the first layer and continuing to the last. When processing the i-th layer, the corresponding hierarchical thermo-mechanical coupling parameters are first obtained. These parameters integrate the current layer's geometry, material physical properties, and printing process settings. Based on the stress accumulation results of the processed layers (layers 1 to i-1) and the hierarchical thermo-mechanical coupling parameters of the current i-th layer, a thermo-mechanical coupling analysis is performed. This analysis calculates the thermal stress generated during the solidification and cooling of the newly deposited layer, as well as its thermal impact on the underlying formed structure, such as localized reheating and cooling. The stress accumulation process can be conceptually represented as: , in, The total stress distribution field of the representative model after the i-th layer printing is completed; It is the result of stress accumulation in the treated layers, that is, the stress state at the end of the (i-1)th layer; This represents the incremental stress field generated by the deposition of the i-th layer. This incremental stress field... This is calculated based on the thermal strain of the layer and the constitutive relationship of the material. The thermal strain is driven by the temperature change field determined by the layer-level thermo-mechanical coupling parameters. This iterative calculation process proceeds layer by layer upwards until all layered model data has been processed. Figure 3 The relationship between the incremental and cumulative layered stresses shown, along with the collection of stress distribution states recorded throughout the process, ultimately constitutes a dynamic stress evolution field describing the internal stress distribution and changes of the model during virtual printing.

[0033] Optionally, obtaining the corresponding hierarchical thermal coupling parameters for each layer in the hierarchical model data includes: Analyze the three-dimensional geometry of the current layer and extract the corresponding local geometric feature parameters; Obtain material physical properties and equipment operating parameters related to the 3D printing process, and generate process physical parameters; By combining the local geometric feature parameters and the process physical parameters, hierarchical thermo-mechanical coupling parameters are generated.

[0034] Specifically, to obtain the layered thermal coupling parameters corresponding to each layer in the layered model data, this invention first analyzes the three-dimensional geometry of the current layer. This analysis automatically identifies and quantifies the geometric features within the two-dimensional contour of the layer through algorithms, such as local thickness, radius of curvature, cross-sectional area, and the ratio of surface area to volume. These quantified values ​​constitute local geometric feature parameters, which reflect the heat conduction and heat dissipation capabilities of different regions of the current layer during the printing process. Simultaneously, the system obtains process physical parameters related to the 3D printing process from a preset database or user input. This includes two parts: first, material physical properties, such as the coefficient of thermal expansion, thermal conductivity, specific heat capacity, and melting point of the printing material; and second, equipment operating parameters, such as laser power, scanning speed, printing temperature, or photopolymerization energy density.

[0035] After obtaining the two sets of parameters mentioned above, the system performs coupled calculations to generate hierarchical thermo-mechanical coupling parameters for driving stress analysis. This coupling is not a simple parameter superposition, but rather the establishment of a physical model to describe the thermal effects of process parameters on a specific geometry. For example, a local thermal input intensity parameter Q can be determined by the following relationship: , Here, Q represents a key component of the generated layered thermo-coupling parameters, such as local energy density; G represents the local geometric feature parameters, such as local cross-sectional thickness; and P represents the process physical parameters, such as laser power and scanning speed. The function f reflects the distribution differences of energy input in different geometric regions. For example, in thinner cross-sectional regions, the same laser power and scanning speed will produce higher energy density and temperature gradients. The final generated layered thermo-coupling parameters are a set defining the thermal load and thermal boundary conditions of the current layer. It precisely describes how printing energy is applied to the current layer and how heat is exchanged with the surrounding environment and the formed portion.

[0036] Optionally, generating the adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field includes: Based on the final state of the dynamic stress evolution field after virtual printing is completed, the final predicted deformation shape of the model is calculated. Using the original 3D model data as a reference target and the final predicted deformation shape as the registration source, the transformation relationship that can map the final predicted deformation shape back to the original 3D model data is calculated to obtain the adaptive compensation transformation matrix.

[0037] Specifically, such as Figure 4As shown, to generate an adaptive compensation transformation matrix based on the dynamic stress evolution field, this invention first extracts data from the final state of the evolution field. This final state represents the internal residual stress field after virtual printing is completed and the model cools to room temperature. Using this residual stress field as input, numerical simulation methods such as finite element analysis are used, combined with mechanical constitutive parameters such as the material's elastic modulus and Poisson's ratio, to calculate the displacement vectors of all nodes of the model when it changes from a stress-free state to a state subjected to this residual stress. By adding the initial coordinates of each node in the original 3D model data to its corresponding displacement vector, the geometric shape of the model after virtual printing can be reconstructed; this shape is the final predicted deformation shape.

[0038] Subsequently, the original 3D model data is used as a reference target, and the final predicted deformed shape is used as the registration source. To calculate the transformation relationship that maps the deformed shape back to the original shape, this invention calculates the inverse vector of the displacement vector from the original position to the predicted deformed position for each node in the model. Specifically, if a node moves from its original position... Move to the deformed position The displacement vector is ,Right now: , Then its compensation displacement vector Defined as The set of compensation displacement vectors for all nodes constitutes a non-rigid, spatially varying displacement field. This displacement field is the adaptive compensation transformation matrix, which stores in a computable form all the geometric transformation information required to accurately restore the deformed model to its original ideal shape.

[0039] Optionally, obtaining the adaptive compensation transformation matrix includes: Based on the original 3D model data, the connection relationships between vertices and the neighborhood curvature information are extracted; Based on the connectivity and neighborhood curvature information, local surface topological constraints are generated; Using the original 3D model data as a reference target and the final predicted deformation shape as the registration source, the local surface topological constraints are applied to maintain the surface smoothness, thereby calculating the transformation relationship and obtaining the adaptive compensation transformation matrix.

[0040] Specifically, the system first analyzes the mesh structure of the original 3D model data, extracting two core pieces of information: the connectivity between vertices (i.e., the topology) and the neighborhood curvature information of each vertex. The connectivity is directly read from the mesh data, while the neighborhood curvature information is calculated by analyzing the local surface patches formed by each vertex and its directly connected neighboring vertices, obtaining values ​​such as Gaussian curvature or mean curvature describing the degree of local bending. Next, based on the extracted connectivity and neighborhood curvature information, local surface topological constraints are generated to maintain geometric fidelity. This constraint is mathematically expressed as an energy function or regularization term, the value of which is negatively correlated with the smoothness of the surface. For example, a commonly used constraint is based on the discrete Laplacian operator, which penalizes vertices that deviate from their neighborhood geometric center, thus tending to maintain surface smoothness. Finally, when calculating the transformation relationship, this process is constructed as an optimization problem. The objective function of this optimization problem is not only to minimize the geometric deviation between the final predicted deformed shape after transformation and the original 3D model data, but also to minimize the smoothness energy defined by the local surface topological constraints. This optimization objective can be expressed as: , in, This represents minimizing the total objective energy; The item is a data item used to measure the distance between the transformed vertex and the corresponding vertex in the original model, ensuring the accuracy of the compensation; The term is a smoothing term, namely the local surface topological constraint, used to penalize unsmooth or unnatural geometric shapes produced after the transformation; This is a weighting coefficient used to balance the relationship between compensation accuracy and surface smoothness. This coefficient can be preset according to the clinical requirements for the smoothness of different areas of the guide plate. By solving this optimization problem, the resulting transformation relationship is the final adaptive compensation transformation matrix, which actively preserves the surface features of the original design while correcting deformation.

[0041] Optionally, applying the adaptive compensation transformation matrix to the original 3D model data to generate high-fidelity compensated model data includes: Obtain the geometric control point set of the original 3D model data; The adaptive compensation transformation matrix is ​​applied to the geometric control point set to generate a compensated vertex coordinate set. Based on the compensated vertex coordinate set, the model surface is reconstructed to generate high-fidelity compensated model data.

[0042] Specifically, to apply the adaptive compensation transformation matrix to the original 3D model data, the system first obtains the geometric control point set of the original 3D model data. For a commonly used triangular mesh model, this geometric control point set is the coordinate set of all its vertices. Next, the adaptive compensation transformation matrix is ​​applied to this geometric control point set. Here, the adaptive compensation transformation matrix is ​​a spatial displacement field containing a compensation displacement vector corresponding to each vertex. For each original vertex coordinate... The system extracts the corresponding compensation displacement vector from the transformation matrix. The new, compensated coordinates can be generated using the following vector addition operation. : , in, These are the compensated vertex coordinates. These are the coordinates of the i-th vertex in the original 3D model data. This is the compensation displacement vector corresponding to the vertex in the adaptive compensation transformation matrix. After traversing all vertices and completing the calculations, the resulting set is the compensated vertex coordinate set. Finally, the model surface is reconstructed based on this compensated vertex coordinate set. This step preserves the topological connections of the original 3D model data, only replacing the original vertex coordinates with the compensated vertex coordinate set, thereby generating a new 3D model that is geometrically pre-deformed. This model is the final high-fidelity compensated model data, which can be directly used for slicing in a 3D printer.

[0043] Optionally, after generating the high-fidelity compensation model data, the following steps are also included: The high-fidelity compensation model data is virtually fitted and verified with preset digital impression data representing the patient's oral cavity structure, and a fitting accuracy evaluation report is generated. Based on the fitting accuracy evaluation report, determine whether the high-fidelity compensation model data meets the preset clinical accuracy requirements; If the high-fidelity compensation model data does not meet the clinical accuracy requirements, then the deviation data is extracted from the fitting accuracy assessment report; The process physical parameters are corrected based on the deviation data, and the high-fidelity compensation model data is recalculated.

[0044] Specifically, the high-fidelity compensation model data is first aligned with pre-set digital impression data representing the patient's oral cavity structure in a virtual three-dimensional space. This digital impression data is typically acquired directly through an intraoral scanner and is an accurate digital copy of the patient's oral anatomy. The system employs an optimal fitting algorithm to register the two data points, and then calculates the normal distance between the surface of the high-fidelity compensation model and the surface of the digital impression data point by point, thereby generating a fitting accuracy evaluation report. This report typically presents the deviation distribution visually in the form of a color contour plot and includes key statistical indicators such as maximum deviation, average deviation, and root mean square error.

[0045] Next, the system compares the statistical indicators in the fitting accuracy assessment report with the preset clinical accuracy requirements. These clinical accuracy requirements are thresholds pre-set according to the standards of oral implant surgery, such as the maximum acceptable gap value. If the high-fidelity compensation model data meets these requirements, the process ends, and the model can be used for printing. If not, the system extracts deviation data from the fitting accuracy assessment report. This deviation data includes not only the numerical value of the deviation but, more importantly, its spatial distribution characteristics on the model. The system analyzes these deviation characteristics, such as whether it is an overall shrinkage deviation or a local warping deviation, and then, based on preset physical rules or machine learning models, inversely infers and corrects the process physical parameters that cause these specific deviations. For example, if excessive overall shrinkage is found, the system may fine-tune the thermal expansion coefficient of the material; if warping is found in a thin-walled structural region, the scanning speed or energy density parameters of that region may be adjusted. The corrected process physical parameters are then used as new inputs and sent back to the dynamic hierarchical stress accumulation analysis step to restart the entire deformation compensation calculation process, generate new high-fidelity compensation model data, and perform virtual fitting verification again until verification is successful.

[0046] Optionally, after generating the high-fidelity compensation model data, the following steps are also included: Based on the geometric shape of the high-fidelity compensation model data, key structural regions are identified and the corresponding printing parameters are optimized to generate optimized printing path data. Based on the optimized printing path data, the device operating parameters are corrected.

[0047] Specifically, the system first performs a detailed analysis of the geometry of the high-fidelity compensation model data, automatically identifying key structural areas prone to stress concentration or heat accumulation during printing. These areas typically include thin-walled structures, sharp corners, overhanging features, and precision holes in implantation catheter sleeves. For each identified key structural area, the system locally optimizes its corresponding printing parameters based on a pre-set process knowledge base. For example, for thin-walled areas, the energy density can be appropriately reduced to decrease heat input and prevent overheating and warping; for precision holes, a finer contour scanning strategy can be used to ensure dimensional accuracy. These localized parameter adjustments are integrated into the printer's slice file, forming optimized print path data containing non-uniform parameter settings. Finally, based on this optimized print path data, the system dynamically adjusts the equipment operating parameters when executing a printing task. This means that the printer controller will no longer use uniform printing parameters for the entire slice, but will instead, based on the specific location of the print head or laser spot, call upon the specific parameters specified for that location in the optimized print path data in real time for printing.

[0048] Based on the same inventive concept, such as Figure 5 As shown, the present invention also provides a deformation compensation system for a 3D printed model of a dental implant guide, the system comprising: The model input module is used to acquire the original three-dimensional model data of the dental implant guide. The stress analysis module is used to perform dynamic hierarchical stress accumulation analysis on the original three-dimensional model data and generate a dynamic stress evolution field that describes the internal stress distribution and changes of the model during the virtual printing process. The matrix generation module is used to generate an adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field. The compensation transformation module is used to apply the adaptive compensation transformation matrix to perform compensation transformation on the original three-dimensional model data to generate high-fidelity compensated model data.

[0049] To verify the feasibility of this invention in practice, it was applied to a real case at a dental clinic. The clinic needed to fabricate a high-precision implant surgical guide for a patient with multiple missing teeth in the maxillary posterior region. Due to the complex structure of the guide, including multiple precision guide holes, a thin-walled structure, and a curved surface highly conforming to the alveolar ridge morphology, conventional 3D printing methods were prone to warping and deformation due to material shrinkage and internal stress, affecting surgical precision.

[0050] First, the system received an STL format design file generated from oral CBCT data and implant planning software. This file contained complete geometric information of the surgical guide. The system automatically parsed the STL file, extracted its vertex coordinate set and triangular facet index list, and performed geometric integrity verification. It checked and automatically repaired minor holes and non-manifold edges in the model, generating a complete original 3D model data with a topologically sound structure as the basis for subsequent analysis.

[0051] Subsequently, the system performs dynamic hierarchical stress accumulation analysis on the original 3D model data. The model is virtually layered along the Z-axis with a layer thickness of 50 micrometers, resulting in layered model data. The analysis is iterative, starting from the first layer. For example, when processing the 850th layer of a thin-walled region of the guide plate, the system first analyzes the geometric features of the layer's outline, namely a local thickness of 1.2 mm and a large radius of curvature. It also retrieves the thermal expansion coefficient, thermal conductivity, and other physical properties of the printing material (a medical photosensitive resin), as well as the printer's operating parameters, such as laser power (180 mW) and scanning speed (2000 mm / s). The system couples these local geometric feature parameters with the process physical parameters to generate layer-specific thermo-mechanical coupling parameters for that layer, used to accurately simulate the heat input and dissipation of the thin-walled region. Through layer-by-layer iterative calculations, the system ultimately generates a dynamic stress evolution field describing the stress evolution from generation and accumulation to final distribution throughout the entire virtual printing process.

[0052] Based on this dynamic stress evolution field, the system extracted the final residual stress field data after the model cooled to room temperature, and used this as a load to calculate the final predicted deformation shape of the model through finite element analysis. The analysis results show that the flange at the far-mid cantilever end of the guide plate is predicted to produce an upward warping of 0.32 mm, while the area near the implantation hole sleeve is predicted to have a non-uniform shrinkage of about 0.15 mm. The system then uses the original 3D model data as the target to calculate the set of inverse displacement vectors that map the predicted deformation shape back to the original shape. In the calculation process, the system introduces local surface topology constraints generated based on vertex connectivity and neighborhood curvature information. By solving an optimization problem that includes data fidelity terms and smoothness constraint terms, the system maintains the smoothness of the guide plate tissue surface while ensuring compensation accuracy, avoiding surface wrinkles that may be introduced by compensation. The final generated non-rigid displacement field is the adaptive compensation transformation matrix.

[0053] Next, the system applies the adaptive compensation transformation matrix to compensate the original 3D model data. The compensation displacement vector corresponding to each node in the matrix is ​​added to the original vertex coordinates to generate a compensated vertex coordinate set. The system preserves the original topological connections, simply replacing the old coordinate set with the new one, thus reconstructing a high-fidelity compensated model data that has been geometrically distorted in the opposite direction of deformation.

[0054] To ensure clinical applicability, the system underwent closed-loop validation. The predicted morphology of the high-fidelity compensation model data, after virtual printing, was virtually fitted against the patient's intraoral digital impression data. The initial fitting accuracy assessment report showed that the model's maximum deviation in the palatal region was 95 micrometers, exceeding the clinically required threshold of 70 micrometers. Based on the spatial distribution characteristics of this deviation, the system inferred that over-curing was caused by heat accumulation in this area and automatically corrected the process physical parameters, fine-tuning the laser scanning speed in this area from 2000 mm / s to 2200 mm / s. The corrected parameters were then fed back into the dynamic hierarchical stress accumulation analysis step, regenerating a new high-fidelity compensation model. A second virtual fitting validation was performed, reducing the maximum deviation to 48 micrometers, meeting clinical requirements.

[0055] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0056] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for deformation compensation of a 3D printed model of a dental implant guide, characterized in that, The method includes: Obtain the original 3D model data of the dental implant guide; Dynamic hierarchical stress accumulation analysis is performed on the original three-dimensional model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during the virtual printing process; Based on the dynamic stress evolution field, an adaptive compensation transformation matrix for correcting geometric deformation is generated; The original three-dimensional model data is compensated and transformed using the adaptive compensation transformation matrix to generate high-fidelity compensated model data.

2. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 1, characterized in that, The acquisition of the original three-dimensional model data of the dental implant guide includes: Receive design files generated from oral scan data and implant planning software; The 3D mesh or surface representation is parsed and extracted from the design file; Perform geometric integrity verification on the parsed 3D mesh or surface representation to generate the original 3D model data.

3. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 1, characterized in that, The step of performing dynamic hierarchical stress accumulation analysis on the original 3D model data to generate a dynamic stress evolution field describing the internal stress distribution and changes of the model during virtual printing includes: The original 3D model data is subjected to virtual layering processing to obtain layered model data; For each layer in the hierarchical model data, obtain the corresponding hierarchical thermal coupling parameters; Based on the stress accumulation results of the processed layers and the layer-level thermo-coupling parameters of the current layer, the stress distribution of the model is iteratively calculated until all layers are processed, thereby generating a dynamic stress evolution field.

4. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 3, characterized in that, The step of obtaining the corresponding hierarchical thermal coupling parameters for each layer in the hierarchical model data includes: Analyze the three-dimensional geometry of the current layer and extract the corresponding local geometric feature parameters; Obtain material physical properties and equipment operating parameters related to the 3D printing process, and generate process physical parameters; By combining the local geometric feature parameters and the process physical parameters, hierarchical thermo-mechanical coupling parameters are generated.

5. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 4, characterized in that, The step of generating the adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field includes: Based on the final state of the dynamic stress evolution field after virtual printing is completed, the final predicted deformation shape of the model is calculated. Using the original 3D model data as a reference target and the final predicted deformation shape as the registration source, the transformation relationship that can map the final predicted deformation shape back to the original 3D model data is calculated to obtain the adaptive compensation transformation matrix.

6. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 5, characterized in that, The obtained adaptive compensation transformation matrix includes: Based on the original 3D model data, the connection relationships between vertices and the neighborhood curvature information are extracted; Based on the connectivity and neighborhood curvature information, local surface topological constraints are generated; Using the original 3D model data as a reference target and the final predicted deformation shape as the registration source, the local surface topological constraints are applied to maintain the surface smoothness, thereby calculating the transformation relationship and obtaining the adaptive compensation transformation matrix.

7. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 6, characterized in that, The step of applying the adaptive compensation transformation matrix to the original 3D model data to generate high-fidelity compensated model data includes: Obtain the geometric control point set of the original 3D model data; The adaptive compensation transformation matrix is ​​applied to the geometric control point set to generate a compensated vertex coordinate set. Based on the compensated vertex coordinate set, the model surface is reconstructed to generate high-fidelity compensated model data.

8. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 7, characterized in that, After generating the high-fidelity compensation model data, the following is also included: The high-fidelity compensation model data is virtually fitted and verified with preset digital impression data representing the patient's oral cavity structure, and a fitting accuracy evaluation report is generated. Based on the fitting accuracy evaluation report, determine whether the high-fidelity compensation model data meets the preset clinical accuracy requirements; If the high-fidelity compensation model data does not meet the clinical accuracy requirements, then the deviation data is extracted from the fitting accuracy assessment report; The process physical parameters are corrected based on the deviation data, and the high-fidelity compensation model data is recalculated.

9. The deformation compensation method for a 3D printed model of a dental implant guide according to claim 7, characterized in that, After generating the high-fidelity compensation model data, the following is also included: Based on the geometric shape of the high-fidelity compensation model data, key structural regions are identified and the corresponding printing parameters are optimized to generate optimized printing path data. Based on the optimized printing path data, the device operating parameters are corrected.

10. A deformation compensation system for a 3D-printed model of a dental implant guide, characterized in that, The system includes: The model input module is used to acquire the original three-dimensional model data of the dental implant guide. The stress analysis module is used to perform dynamic hierarchical stress accumulation analysis on the original three-dimensional model data and generate a dynamic stress evolution field that describes the internal stress distribution and changes of the model during the virtual printing process. The matrix generation module is used to generate an adaptive compensation transformation matrix for correcting geometric deformation based on the dynamic stress evolution field. The compensation transformation module is used to apply the adaptive compensation transformation matrix to perform compensation transformation on the original three-dimensional model data to generate high-fidelity compensated model data.

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