3d printing data processing method, system, device and medium for porous implant

Abstract

The application provides a 3D printing data processing method, system, device and medium of a porous implant. The method comprises: slicing a three-dimensional model of the porous implant to generate an initial two-dimensional cross section; extracting a solid contour in the cross section and calculating a solid area ratio and a cross section shape complexity parameter thereof; based on the parameters, classifying the solid contour into a first contour representing a junction and a second contour representing a strut; based on the distribution state of the first contour in each cross section, assigning a target slice layer thickness to a corresponding height interval and performing re-slicing; simplifying the first contour and the second contour in the re-sliced cross section respectively, and the simplification accuracy of the first contour is higher than that of the second contour; and generating printing data based on the simplified contour set. The application can reduce the consumption of computing resources and ensure the mechanical forming strength at the junction.

Description

Technical Field

[0001] This disclosure relates to the field of 3D printing data processing technology, and in particular to a 3D printing data processing method, system, device and medium for porous implants. Background Technology

[0002] With the widespread application of 3D printing technology in the medical field, breakthroughs have been achieved in the design and manufacturing of orthopedic implants. To promote osteoblast ingrowth and effectively reduce the difference in elastic modulus between the implant and real human bone to avoid stress shielding effects, medical implants typically employ biomimetic porous structures with extremely high porosity, such as trabecular structures that mimic human cancellous bone. These porous structures exhibit an extremely complex rod-like network topology in three-dimensional space.

[0003] In the design phase of porous implants, to meet the biomechanical requirements of different regions, Chinese patent application publication number CN114186297A discloses a design method for variable-density conformal lattice structures based on topology optimization, which can generate complex lattice models with variable-density characteristics through topology optimization. However, as the complexity of such variable-density porous structures increases, their three-dimensional digital models contain millions of tiny support rods and intersection nodes. When importing such massive geometric data into a 3D printing system for slicing and path planning, traditional data processing methods face huge computational bottlenecks, resulting in extremely high memory consumption and even causing processing equipment to crash due to memory overflow, leading to extremely low data processing efficiency.

[0004] To optimize data processing and reduce data redundancy, existing technologies have proposed several optimization schemes for partitioning. For example, US Patent Application Publication No. US20210150813A1 discloses a mesh generation method for representing 3D printed objects. This method proposes to identify the outer shell boundary and internal infill boundary of the printed object and apply differentiated node snapping and mesh generation operations to different regions. This approach addresses, to some extent, the data processing efficiency problem for conventional macroscopic solid parts with clearly defined external entities and sparse internal infill.

[0005] However, in the specific application scenario of porous medical implants, the aforementioned differentiated processing scheme based on the macroscopic external shell and internal filling characteristics still has limitations. The essence of a porous implant is a spatial network continuously interwoven with microscopic rods and nodes, without a clear boundary between a conventional solid shell and internal filling. In this microscopic topological network, the implant's core load-bearing capacity highly depends on the structural nodes where multiple microscopic rods intersect and merge, while the independent rods shuttling between these nodes mainly serve as spatial transitions. Existing technologies cannot effectively differentiate this complex microscopic topological morphology at the same horizontal slice height. This leads to a dilemma in actual data processing: using uniform high-precision slicing and contour analysis still cannot avoid the problem of massive memory consumption; using uniformly reduced precision or simplified processing similar to ordinary internal filling will cause the microscopic structural nodes bearing critical stresses to lose their geometric fidelity, resulting in localized stress concentration or microscopic fractures after 3D printing, severely weakening the implant's mechanical strength.

[0006] Therefore, how to effectively ensure the molding strength of key microscopic structures inside porous implants while reducing the memory consumption of processing massive amounts of porous slice data is a technical problem that urgently needs to be solved in the field of additive manufacturing of medical implants. Summary of the Invention

[0007] To achieve the above-mentioned objectives and other advantages of the present invention, in a first aspect, this application provides a 3D printing data processing method for porous implants, comprising: acquiring a three-dimensional model of the porous implant; slicing the three-dimensional model of the porous implant to generate a sequence of sections containing multiple initial two-dimensional sections; extracting solid contours within each initial two-dimensional section and calculating the solid area ratio and section morphology complexity parameters of each solid contour; classifying each solid contour into a first contour representing a microscopic cell intersection node and a second contour representing a microscopic cell support based on the solid area ratio and section morphology complexity parameters; adaptively allocating target slice layer thicknesses to the height intervals where each initial two-dimensional section is located based on the distribution state of the first contours within each initial two-dimensional section; re-slicing the three-dimensional model of the porous implant according to the allocated target slice layer thickness within each height interval to generate target two-dimensional sections; simplifying the first contour and the second contour within the target two-dimensional sections respectively and generating a set of target contours; wherein the contour simplification accuracy of the first contour is higher than that of the second contour to reduce computational resource consumption and ensure mechanical forming strength at the intersection nodes; and generating 3D printing-ready data based on the set of target contours.

[0008] In an optional embodiment, calculating the entity area ratio and cross-sectional morphology complexity parameter of each entity contour includes: calculating the reference area of ​​the global outer contour of the initial two-dimensional cross-section; traversing each entity contour within the initial two-dimensional cross-section, calculating the actual internal area of ​​the target entity contour, and the ratio of the actual internal area to the reference area, and determining it as the entity area ratio; and calculating the ratio of the square of the perimeter of the target entity contour to the actual internal area, and determining it as the cross-sectional morphology complexity parameter.

[0009] In an optional embodiment, based on the entity area ratio and cross-sectional morphology complexity parameters, each entity contour is classified into a first contour representing the intersection node of micro-cells and a second contour representing the support column of micro-cells, including: calculating the sum of the internal actual areas of all entity contours within the initial two-dimensional cross-section; calculating the difference between the reference area of ​​the global outer contour of the initial two-dimensional cross-section and the sum of the internal actual areas, and determining the ratio of the difference to the reference area as the equivalent porosity of the initial two-dimensional cross-section; calculating an area ratio threshold and a complexity threshold based on the equivalent porosity using a preset mapping function; wherein the value of the complexity threshold increases with the increase of the equivalent porosity; traversing each entity contour within the initial two-dimensional cross-section and determining the currently traversed entity contour as the contour to be classified; obtaining the entity area ratio and cross-sectional morphology complexity parameters corresponding to the contour to be classified; and determining whether the entity area ratio is greater than or equal to the area ratio threshold and whether the cross-sectional morphology complexity parameters are greater than or equal to the complexity threshold. If the entity area ratio is greater than or equal to the area ratio threshold, or the cross-sectional shape complexity parameter is greater than or equal to the complexity threshold, then the contour to be classified is classified as the first contour; if the entity area ratio is less than the area ratio threshold, and the cross-sectional shape complexity parameter is less than the complexity threshold, then the contour to be classified is classified as the second contour.

[0010] In an optional embodiment, based on the distribution state of the first contour within each initial two-dimensional cross-section, a target slice layer thickness is adaptively allocated to the height interval where each initial two-dimensional cross-section is located, including: determining whether each initial two-dimensional cross-section contains the first contour; if the initial two-dimensional cross-section contains the first contour, then a first slice layer thickness is allocated to the height interval where the initial two-dimensional cross-section is located as the target slice layer thickness; if the initial two-dimensional cross-section does not contain the first contour, then a second slice layer thickness is allocated to the height interval where the initial two-dimensional cross-section is located as the target slice layer thickness; wherein, the first slice layer thickness is less than the second slice layer thickness.

[0011] In an optional embodiment, contour simplification is performed on the first contour and the second contour within the target two-dimensional cross-section to generate a target contour set, including: determining the first contour or the second contour within the target two-dimensional cross-section as the contour to be processed, wherein the contour to be processed consists of multiple contour points arranged in a connected order; if the contour to be processed is the first contour, then the first chord height error threshold is configured as the target chord height error threshold; if the contour to be processed is the second contour, then the target chord height error threshold is determined based on the second chord height error threshold, wherein the first chord height error threshold is less than the second chord height error threshold; establishing a traversal window containing three consecutive contour points along the connected order; calculating the vertical chord height distance from the middle contour point in the middle position within the traversal window to the lines connecting the two side contour points; if the vertical chord height distance is less than the target chord height error threshold, then the middle contour point is deleted, and the next contour point is extracted along the connected order to complete the traversal window, thereby updating the three consecutive contour points calculated in the next iteration; if the vertical chord height distance is greater than or equal to the target chord height error threshold, then the middle contour point is retained, and the traversal window is slid backward along the connected order to update the three consecutive contour points calculated in the next iteration.

[0012] In an optional embodiment, determining the target chord height error threshold based on the second chord height error threshold includes: calculating the shortest plane Euclidean distance between the second contour within the target two-dimensional cross-section and the nearest first contour; determining whether the shortest plane Euclidean distance is less than a preset smooth transition threshold; if the shortest plane Euclidean distance is less than the preset smooth transition threshold, then marking the second contour as a transition contour, and calculating the dynamic transition error threshold of the corresponding transition contour using a preset interpolation function based on the shortest plane Euclidean distance, the first chord height error threshold, and the second chord height error threshold, and determining the dynamic transition error threshold as the target chord height error threshold; if the shortest plane Euclidean distance is greater than or equal to the preset smooth transition threshold, then determining the second chord height error threshold as the target chord height error threshold; wherein, the value of the dynamic transition error threshold is between the first chord height error threshold and the second chord height error threshold, and its value gradually approaches the first chord height error threshold as the shortest plane Euclidean distance decreases.

[0013] In an optional embodiment, within each height interval, the porous implant 3D model is re-sliced ​​according to the assigned target slice thickness to generate a target 2D cross section, including: identifying a first height interval and a second height interval adjacent in the slice normal direction, wherein a first slice thickness is assigned in the first height interval and a second slice thickness is assigned in the second height interval; defining the boundary region between the first height interval and the second height interval as a slice thickness transition zone; generating at least one transition slice elevation plane within the slice thickness transition zone according to a preset step size; assigning a third slice thickness to the transition slice elevation plane, and re-slicing the porous implant 3D model, wherein the value of the third slice thickness is between the first slice thickness and the second slice thickness to achieve a smooth gradient transition of slice thickness in the longitudinal direction.

[0014] Secondly, this application provides a 3D printing data processing system for porous implants, comprising: a model acquisition module configured to acquire a three-dimensional model of the porous implant; an initial slicing module configured to slice the three-dimensional model of the porous implant to generate a sequence of cross-sections containing multiple initial two-dimensional cross-sections; a feature calculation module configured to extract entity contours within each initial two-dimensional cross-section and calculate the entity area ratio and cross-sectional morphological complexity parameters of each entity contour; a contour classification module configured to classify each entity contour into a first contour representing the intersection node of micro-cells and a second contour representing the support column of micro-cells based on the entity area ratio and cross-sectional morphological complexity parameters; a layer thickness allocation module configured to adaptively allocate target slice layer thicknesses for the height interval where each initial two-dimensional cross-section is located based on the distribution state of the first contours within each initial two-dimensional cross-section; and an adaptive slicing module configured to re-slice the three-dimensional model of the porous implant according to the allocated target slice layer thickness within each height interval to generate target two-dimensional cross-sections. The contour simplification module is configured to simplify the first contour and the second contour within the target two-dimensional cross-section and generate a target contour set; wherein the contour simplification accuracy of the first contour is higher than that of the second contour, so as to reduce the consumption of computing resources and ensure the mechanical forming strength at the intersection node; the data generation module is configured to generate 3D printing data based on the target contour set.

[0015] Thirdly, this application provides an electronic device, including: a memory for storing one or more computer instructions; and a processor communicatively connected to the memory; wherein, when the one or more computer instructions are executed by the processor, the 3D printing data processing method for porous implants as described in the first aspect of this application and any optional embodiment thereof is implemented.

[0016] Fourthly, this application provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the 3D printing data processing method for porous implants as described in the first aspect of this application and any optional embodiment thereof.

[0017] Compared with the prior art, the beneficial effects of this application are: First, this application divides the solid contour into a first contour and a second contour by calculating the enclosing area and shape factor of the solid contour within a two-dimensional cross-section. The first contour is processed using a first layer thickness and a first tolerance, while the second contour is processed using a second layer thickness and a second tolerance, which have larger values. This setup, while maintaining the geometric boundary fidelity of the node intersection region represented by the first contour, reduces the number of slice layers and the amount of contour node data in the independent rod region represented by the second contour. This reduces the memory footprint of the porous implant slice data processing while ensuring the structural strength after 3D printing.

[0018] Secondly, this application extracts the entity contours within the initial two-dimensional cross-section after slicing and uses the enclosing area and shape factor—two two-dimensional geometric feature parameters—as the basis for identifying rod-shaped topological network regions. This avoids directly performing three-dimensional curvature calculations or three-dimensional Boolean operations on three-dimensional spatial structures containing massive polygonal meshes. This approach reduces the feature extraction process of three-dimensional spatial topology to algebraic and geometric operations on a two-dimensional plane, thus reducing the computational power and memory overhead on computing devices during data processing.

[0019] Furthermore, this application generates a monotonically decreasing dynamic transition tolerance by calculating the shortest planar Euclidean distance between the second and first contours, and generates a transition slice elevation plane with a third layer thickness allocated according to a step size between the first and second height intervals. This setup establishes a data smoothing transition mechanism between two-dimensional planar contours with varying thinning accuracy and between three-dimensional height intervals with varying slice step sizes, reducing the step-like geometric abrupt changes on the model surface caused by tolerance or layer thickness mutations, thereby reducing the probability of stress concentration at the interface between different structures after the porous implant is formed.

[0020] Finally, this application calculates the Euclidean distance between non-closed endpoints in the same target contour set, and constructs a straight-line connection topology when the Euclidean distance is less than the bridging threshold, and filters out isolated noise data when the distance is greater than or equal to the bridging threshold. This setting uses underlying topology logic to repair local data breaks caused by mesh cutting or tolerance thinning, maintains the continuity and closure of single-layer processing path data, and reduces the probability of filament breakage or forming defects when the 3D printing equipment executes the processing path. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating a 3D printing data processing method for porous implants provided in this application embodiment; Figure 2 A flowchart illustrating the calculation of entity area ratio and cross-sectional morphology complexity parameters provided in this application embodiment; Figure 3 A flowchart illustrating the process of classifying entity contours into a first contour and a second contour, provided for embodiments of this application; Figure 4 A schematic diagram illustrating the process of adaptively allocating target slice layer thickness as provided in an embodiment of this application; Figure 5 A schematic diagram of the process for performing re-slicing to generate a target two-dimensional cross section provided for embodiments of this application; Figure 6 A schematic diagram illustrating the process of simplifying contours and generating a target contour set provided in an embodiment of this application; Figure 7 A schematic diagram of the process for determining the target chord height error threshold based on the second chord height error threshold provided in an embodiment of this application; Figure 8 A spatial topological diagram of a three-dimensional model of a porous implant provided for embodiments of this application, and a photomicrograph of the physical entity prepared using the method of this application; Figure 9 This is a schematic diagram illustrating the structural morphological evolution of the three-dimensional model of the porous implant provided in the embodiments of this application; Figure 10 A structural block diagram of a 3D printing data processing system for porous implants provided in this application embodiment; Figure 11 This is a hardware structure block diagram of an electronic device suitable for implementing a 3D printing data processing method, provided as an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and drawings of this invention are used to distinguish different objects, rather than to limit a specific order.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0026] This application provides a 3D printing data processing method for porous implants, primarily applied to the digital model preprocessing stage before 3D printing of medical implants (such as orthopedic porous pads, lattice acetabular cups, etc.). By intelligently identifying the topological features within the 3D model and combining height ranges for variable layer thickness slicing and differentiated contour simplification, the processing load of 3D slice data can be effectively reduced without sacrificing the strength of core mechanical nodes.

[0027] It should be understood that the 3D printing data processing method for porous implants provided in this application is essentially a computer-implemented method. The executing entity of this method can be any electronic device with data processing and graphics processing capabilities, such as, but not limited to, graphics processing workstations, local servers, cloud computing clusters, or industrial control computers directly integrated inside 3D printing equipment.

[0028] Furthermore, it should be noted that the term "porous implant" mentioned in the embodiments of this application refers to any medical device or biological scaffold with a microscopically interconnected pore structure. Its applications include, but are not limited to, orthopedic implants (such as acetabular cups, interbody fusion cages, and bone defect pads), maxillofacial prostheses, and dental implants. The term "3D printing" encompasses additive manufacturing technologies that construct objects layer by layer based on digital model files, including but not limited to metal or non-metal 3D printing processes such as selective laser melting (SLM), electron beam melting (EBM), or binder jetting.

[0029] To more clearly illustrate the technical solution of this application, the evolution background of porous implant lattice structures is first explained. With the deep integration of additive manufacturing technology and biomechanical research, the structural design of porous implants is evolving from regular periodic lattices to highly biomimetic random trabecular bone topologies with extremely high porosity. (Refer to...) Figure 9 , Figure 9 Image (a) shows a realistic CT scan model of the human skeleton. Using this as a reference, current simulation reconstruction is moving from... Figure 9 The traditional coarse-grained skeleton structure shown in (b) is oriented towards Figure 9 The evolution of the biomimetic lattice model with extremely high porosity is shown in (c). Compared to Figure 9 The structure shown in (b) is Figure 9 The biomimetic trabecular model shown in (c) contains a massive number of delicate microscopic supports and dense intersecting nodes, with its three-dimensional data volume increasing exponentially. In practical applications, if a traditional global isoparametric slicing algorithm is used to process such... Figure 9 The massive amount of microscopic topological data shown in (c) will cause a severe computational load on computer equipment, resulting in a technical deadlock between computing power consumption and forming accuracy.

[0030] This application provides a 3D printing data processing method for porous implants, aiming to address the above-mentioned... Figure 9 The complex biomimetic lattice model shown in (c) employs a feature reduction and adaptive slicing strategy via computer equipment. The core logic of this method lies in using geometric feature indices to decompose the complex microscopic topology into a first contour representing the cell's intersection nodes and a second contour representing the cell's support pillars, and assigning different slice layer thicknesses and geometric tolerances based on the differences in their stress properties. Through this differentiated processing, this application can significantly compress redundant slice coordinate data while ensuring the forming accuracy of the core stress region, thereby breaking through the efficiency bottleneck of traditional slicing algorithms when processing highly complex models.

[0031] Figure 1 This is a schematic flowchart illustrating a 3D printing data processing method for porous implants provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps: S11. Obtain a three-dimensional model of the porous implant; Specifically, the 3D model of the porous implant can be an artificial lattice structure model (such as Gyroid, Diamond, and other porous structures) generated by computer-aided design software, or a trabecular bone model reconstructed through reverse engineering based on the patient's actual medical imaging data (such as CT or MRI data). Optionally, the data storage format of the 3D model of the porous implant includes solid geometric data formats that support slicing, such as STL, OBJ, or 3MF formats.

[0032] S12. Slice the three-dimensional model of the porous implant to generate a sequence of sections containing multiple initial two-dimensional sections; Specifically, the slicing operation refers to using a set of intersecting planes parallel to the preset construction plane to cut the three-dimensional model of the porous implant, thereby obtaining the two-dimensional geometric projection of the model at different height positions. Preferably, in order to ensure the comprehensiveness and accuracy of subsequent feature extraction, when performing step S12, a preset fixed layer thickness can be used to slice the three-dimensional model of the porous implant at equal intervals, thereby generating an initial cross-sectional sequence.

[0033] S13. Extract the solid contours within each initial two-dimensional section, and calculate the solid area ratio and cross-sectional shape complexity parameters of each solid contour. Extracting the solid contour refers to obtaining the closed boundary line representing the material-filled area in the initial two-dimensional cross-section. It should be noted that the solid area ratio characterizes the degree of internal filling of the solid contour; the cross-sectional morphology complexity parameter characterizes the degree of tortuosity and irregularity of the geometric shape of the solid contour edges. By introducing these two feature parameters, complex mechanical topologies in three-dimensional space can be mapped to geometric quantification indicators on a two-dimensional slice, providing an objective data benchmark for subsequent structural identification.

[0034] S14. Based on the entity area ratio and cross-sectional shape complexity parameters, each entity contour is classified into a first contour representing the intersection node of micro-cells and a second contour representing the support of micro-cells. The internal microstructure of porous implants typically consists of multiple rod-shaped struts and nodes formed by their interconnections. Specifically, the first contour corresponds to the microcellular junction nodes, which are the core stress-bearing regions within the implant. These junction nodes typically exhibit a large area and divergent edges (i.e., high cross-sectional morphological complexity) in a two-dimensional cross-section. The second contour corresponds to the microcellular struts, which act as connectors and typically appear as smaller, more regular edges (i.e., lower cross-sectional morphological complexity) and are independent, closed shapes in a two-dimensional cross-section. Through classification operations, the algorithm can accurately distinguish the physical properties of different topological regions within the cross-section.

[0035] S15. Based on the distribution state of the first contour within each initial two-dimensional section, adaptively allocate the target slice layer thickness for the height interval where each initial two-dimensional section is located. A height interval refers to a physical space with a specific height span, defined in the slice normal direction of a 3D model of a porous implant. Furthermore, since the first contour (intersection node) has a greater impact on mechanical strength, a smaller slice thickness is assigned to a height interval when the first contour is determined to exist there; conversely, a larger slice thickness is assigned to a height interval when only the second contour (support) exists there. This adaptive allocation mechanism breaks the limitations of traditional globally uniform slice thickness, preserving features in areas requiring precision and improving processing efficiency in structurally simple areas.

[0036] S16. Within each height range, re-slice the porous implant 3D model according to the assigned target slice thickness to generate the target 2D cross section. Specifically, the system performs variable-thickness slicing on the three-dimensional model of the porous implant based on the different height ranges and corresponding target slice thicknesses determined in step S15, and outputs the final target two-dimensional cross-section for guiding printing.

[0037] S17. Simplify the first contour and the second contour within the target two-dimensional cross section and generate a target contour set; wherein, the contour simplification accuracy of the first contour is higher than that of the second contour, so as to reduce the consumption of computing resources and ensure the mechanical forming strength at the intersection node. Contour simplification refers to the process of removing redundant data points on the contour boundary using mathematical thinning algorithms. It's important to note that "contour simplification accuracy" here refers to the ability to maintain geometric overlap between the simplified contour and the original theoretical contour. Since the first contour represents a load-bearing intersection node, applying higher contour simplification accuracy (i.e., retaining more data points) to it maximizes the restoration of the original design boundary of the intersection node, avoiding stress concentration caused by polygonal effects and thus ensuring forming strength. Conversely, the second contour represents a slender support column; applying lower contour simplification accuracy to it allows for a significant reduction of contour data points within an acceptable error range, thus significantly reducing memory usage and computational resource consumption during the overall path planning process.

[0038] S18. Generate 3D printing-ready data based on the target contour set.

[0039] Specifically, the system extracts the target contour set after dimensionality reduction and simplification, plans the scanning and filling lines of laser or electron beams within the contour, compiles and generates G-code files or other processing path files that can be recognized by specific additive manufacturing equipment, and transmits them to 3D printing equipment to perform solid manufacturing.

[0040] It should be noted that, although Figure 1The flowchart shown numbers and describes the steps sequentially from S11 to S18, but this does not mean that these steps must be executed in strict chronological order. Without departing from the technical logic of this application, those skilled in the art can reasonably adjust the execution order of some of the above steps according to the actual system architecture (e.g., introducing a multi-threaded parallel computing mechanism). For example, after completing the re-slicing of step S16 for a specific height range, the contour simplification of step S17 can be directly handed over to an asynchronous thread without waiting for all sections of the entire 3D model to be sliced. Any adjustment of the order or parallelization of steps that does not violate the inherent causal dependencies between steps (i.e., the output of a preceding step must be used as the input of a subsequent step) should fall within the protection scope of the embodiments of this application.

[0041] Regarding the acquisition of the 3D model of the porous implant in step S11 above, this step aims to provide a basic data source for subsequent slicing and analysis. It should be understood that, depending on the medical application scenario, the source and generation method of the 3D model of the porous implant can encompass various possibilities, specifically implemented through the following two methods: In one optional embodiment, reverse engineering reconstruction based on medical images is employed. Specifically, an electronic device (or a medical image processing workstation communicatively connected to it) first acquires raw medical image data of the patient's lesion area, such as computed tomography (CT) images or magnetic resonance imaging (MRI) data. Subsequently, the contours of bones or tissues are extracted using a preset grayscale threshold segmentation algorithm, and three-dimensional surface reconstruction is performed to generate a solid model shell. Based on this, a topology generation algorithm is used to fill the interior of the solid model shell with preset microporous units (such as trabecular bone structural units), thereby fusing and generating a porous implant three-dimensional model with a complex internal microstructure. This approach ensures that the macroscopic shape of the implant perfectly conforms to the patient's anatomical structure.

[0042] In another alternative embodiment, forward generation based on computer-aided design (CAD) is employed. Specifically, designers directly construct a standard-sized medical device boundary model using 3D modeling software, and then use a lattice generator to array regularly distributed mathematically periodic minimal surfaces (TPMS, such as Gyroid, Diamond, or Schwarz surfaces) or lattice truss structures within the boundary model. The electronic device obtains the 3D model of the porous implant by reading the file exported from the design software.

[0043] Furthermore, to ensure that the model data can be seamlessly parsed by subsequent slicing algorithms, the data format of the porous implant 3D model is preferably a geometric description format based on surface mesh generation. Exemplary examples include, but are not limited to, STL (Stereolithography) format, OBJ format, AMF (Additive Manufacturing File) format, or 3MF (3D Manufacturing Format). In these formats, the 3D surface of the porous implant is discretized into a large set of triangular or polygonal facets, each facet uniquely determined by its spatial vertex coordinates and normal vector.

[0044] Furthermore, since the 3D model of porous implants reconstructed through reverse engineering or exported from CAD may contain data defects (such as reversed normals, zero-thickness patches, or mesh holes), direct slicing would result in non-closed 2D cross-sectional contours, leading to the failure of subsequent area and complexity calculations. Therefore, after step S11 and before step S12, a model preprocessing step is included: the electronic device performs non-manifold topology detection on the acquired 3D model of the porous implant; if defective triangular patches are detected, mesh repair is performed using a preset hole-filling algorithm and normal unification algorithm to ensure that the 3D surface mesh of the porous implant model is in a strictly closed topological manifold state (i.e., without mesh holes or gaps at the data level), thereby enabling the slicing algorithm to accurately distinguish between the internal and external pore spaces of the solid support. This step provides a reliable data foundation for subsequent high-precision slice intersection calculations.

[0045] It should be noted that before executing step S12, the system first needs to establish the relative spatial pose of the porous implant's 3D model in the 3D printing space. Specifically, the system establishes a global construction coordinate system including the X-axis, Y-axis, and the forming normal Z-axis. Based on the clinical stress requirements or minimum support surface principle of the porous implant, the system performs spatial rotation and translation operations on the porous implant's 3D model to fix it within this global construction coordinate system. All subsequent heights, normals, and slicing planes are defined based on this forming normal Z-axis, thereby ensuring absolute consistency between the slicing direction and the actual physical printing direction.

[0046] For step S12 mentioned above, slicing the three-dimensional model of the porous implant is essentially a geometric intersection operation process that reduces the three-dimensional spatial data to two-dimensional planar data. Therefore, in order to provide an accurate reference for subsequent feature extraction (step S13) and adaptive layer thickness allocation (step S15), the initial two-dimensional cross-section generated at this stage should have sufficient data sampling density.

[0047] Specifically, the execution process of step S12 includes: First, the system determines the orientation of the porous implant 3D model within the virtual construction chamber and extracts the lowest and highest point heights of the model along the forming normal (usually the Z-axis direction).

[0048] Secondly, the system acquires a preset initial slice thickness. It should be noted that, in order to accurately capture the morphological changes of microscopic intersection nodes and pillars, this initial slice thickness is usually configured to a small global fixed constant (e.g., setting the initial slice thickness between 0.01 mm and 0.02 mm) to ensure extremely high sampling resolution in the normal direction.

[0049] Subsequently, based on the lowest point height, highest point height, and initial slice layer thickness, the system generates a series of equally spaced virtual cutting planes parallel to the XY plane. Finally, the system-driven algorithm calculates the geometric intersection of each virtual cutting plane with the surface mesh (i.e., numerous triangular facets) of the porous implant's 3D model. Specifically, the algorithm traverses all triangular facets in the model, determining whether the three vertices of each facet are distributed on both sides of the virtual cutting plane. If they are, linear interpolation is used to calculate the intersection points of the facet edges with the cutting plane, and all intersection points on the same cutting plane are sequentially connected end-to-end to form closed polygonal boundaries. These closed polygonal boundaries constitute the initial two-dimensional cross-section at the current cutting height.

[0050] Through the above slicing operations, the 3D model of the porous implant is completely converted into a sequence of sections containing multiple initial 2D sections. This sequence is arranged sequentially according to height coordinates, accurately and realistically recording the topological morphology of the porous implant at each height level.

[0051] For step S13 mentioned above, the solid contours within each initial two-dimensional cross-section are extracted, and the solid area ratio and cross-sectional morphological complexity parameters of each solid contour are calculated. It should be noted that the purpose of this step is to transform the microscopic topological features of the three-dimensional porous structure into quantifiable geometric indicators on a two-dimensional plane. In specific implementation, the initial two-dimensional cross-section typically contains multiple discrete closed shapes, which represent the physical cross-section of the porous implant's solid material after being truncated by a cutting plane. The electronic device uses a contour tracing algorithm (e.g., a scanline boundary tracing algorithm) to traverse the current cross-section, extracting the coordinate point set of all closed solid boundaries to form multiple independent solid contours. Subsequently, by calculating the geometric features of each solid contour, its solid area ratio and cross-sectional morphological complexity parameters are obtained, providing objective data support for subsequent identification of stress-bearing intersections and connecting supports.

[0052] Furthermore, to ensure the accuracy of subsequent area geometry calculations, the electronic device needs to perform topological sorting on the extracted coordinate point set when forming independent entity contours. Specifically, the electronic device uses Depth-First Search (DFS) or based on the connectivity of adjacent line segments to rearrange the disordered set of intersection points into an ordered sequence with a unique orientation (e.g., uniformly counterclockwise), and checks whether the coordinates of the first and last nodes of this sequence coincide. If they do not coincide, forced closure is performed using linear interpolation or a nearest-neighbor closure algorithm. This preprocessing step ensures that the extracted entity contours mathematically constitute standard simple polygons, thereby avoiding the failure of area integral formulas (such as the shoelace formula) or the area cancellation error caused by self-intersecting polygons due to the disorder of the point set.

[0053] In an optional embodiment, refer to Figure 2 Step S13, which calculates the proportion of the entity area and the cross-sectional shape complexity parameters of each entity contour, includes the following steps: S131. Calculate the reference area of ​​the global bounding contour of the initial two-dimensional section.

[0054] Specifically, the global bounding contour is used to characterize the macroscopic physical boundary of the porous implant at the current cutting height. The electronic device acquires the coordinates of the point set of all solid contours within the initial two-dimensional cross-section, and uses the Convex Hull Algorithm to generate either the smallest convex polygon or the smallest bounding rectangle that encloses all solid contours, defining it as the global bounding contour. Subsequently, the total area of ​​the plane enclosed by this global bounding contour is calculated and denoted as the reference area. This reference area serves as the reference denominator for evaluating the fullness of the local microstructure.

[0055] S132. Traverse the contours of each entity within the initial two-dimensional section, calculate the actual internal area of ​​the target entity contour, and the ratio of the actual internal area to the reference area, and determine it as the entity area ratio.

[0056] Specifically, the electronic device sequentially reads the contours of each entity according to a preset traversal rule, and takes the currently read contour as the target entity contour. For the target entity contour composed of discrete coordinate points, the electronic device calculates the absolute physical area enclosed by it using a polygon area calculation model, which is recorded as the actual internal area. The specific calculation formula is as follows: in, Indicates the actual internal area. This represents the total number of vertices contained in the outline of the target entity. Indicates the first The coordinates of each vertex in the initial two-dimensional cross-sectional coordinate system are defined as follows: To form a closed loop calculation.

[0057] After calculating the actual internal area, the electronic device divides it by the reference area, and the resulting ratio is determined as the proportion of the target entity's area within the overall outline. This parameter objectively reflects the proportion of area occupied by a single microstructure in the current macroscopic cross-section.

[0058] Furthermore, when the porous implant contains internally porous tubular structures or nested topologies, the target entity profile appears as a multi-connected region containing at least two discrete closed coordinate loops in the initial two-dimensional cross-section. For the area calculation of this multi-connected region, the electronic device employs a signed area summation mechanism based on the vertex wrapping direction.

[0059] Specifically, based on the normal properties of the surface mesh of the 3D model, each closed coordinate loop generated by slicing naturally has a definite vertex wrapping direction. Among them, the coordinate loop representing the outer boundary of the entity and the coordinate loop representing the internal holes exhibit opposite wrapping directions (i.e., clockwise and counterclockwise).

[0060] The electronic device substitutes the vertex sequence of each closed coordinate ring into the aforementioned polygon area calculation formula, but does not perform the absolute value operation, thereby obtaining the signed area of ​​each closed coordinate ring. ( (This refers to the numbering of the closed coordinate loops). Due to the opposite directions of encirclement, the area of ​​the outer boundary loop of the entity has opposite signs to the area of ​​the inner hole loop (i.e., one positive and one negative). Subsequently, the actual internal area of ​​the target entity contour is... The calculation is the absolute value of the algebraic sum of the signed areas, i.e.: This computational mechanism utilizes the vector direction attribute of the coordinate sequence to automatically cancel out the area values ​​of the hole region through algebraic addition, thereby obtaining the net physical entity area of ​​the target entity outline.

[0061] S133. Calculate the ratio of the square of the perimeter of the target entity's outline to its actual internal area, and determine it as the cross-sectional morphology complexity parameter.

[0062] Specifically, the electronic device calculates the total boundary length of the target entity contour by accumulating the linear Euclidean distances between adjacent vertices of the target entity contour, which is denoted as the perimeter. Subsequently, the actual internal area was calculated using the aforementioned methods. The cross-sectional shape complexity parameter is calculated using a preset shape quantization function. The morphological quantization function is: It should be understood that, based on the isoperimetric theorem, the ratio of the square of the circumference of a circle to its area is the smallest among all planar figures. When the micro-pillars of a porous structure pass approximately perpendicularly through the cutting plane, their cross-sectional shape is close to a circle or a regular polygon, and the value of the cross-sectional shape complexity parameter is relatively small. When multiple micro-pillars intersect to form connecting nodes, their cross-sectional shape exhibits multi-directional branches or irregular polygons, causing the increase in the perimeter to be greater than the increase in the area, thus increasing the cross-sectional shape complexity parameter. By calculating this cross-sectional shape complexity parameter, the algorithm can map the three-dimensional topological connection state to a two-dimensional algebraic scalar, providing a deterministic input index for the classification of nodes and pillars.

[0063] For step S14, based on the entity area ratio and cross-sectional morphology complexity parameters, each entity contour is classified into a first contour representing the intersection node of micro-cells and a second contour representing the support column of micro-cells.

[0064] It should be noted that the purpose of this step is to infer the topological structure of three-dimensional space from the geometric features of a two-dimensional plane, thereby accurately defining the intersection nodes of stress-bearing structures and the connecting struts that transmit stress within the implant. In this process, the computer equipment uses the proportion of solid area to characterize the local fullness of the structure and the cross-sectional morphological complexity parameter to characterize the edge divergence and multi-directional branching characteristics of the structure. By establishing the above-mentioned comprehensive judgment logic based on two parameters, the algorithm can achieve deterministic output of the classification of microscopic stress units based on pure two-dimensional slice data, even in the absence of a three-dimensional voxel continuity context, providing a benchmark label for subsequent dimensionality reduction of differentiated data.

[0065] In an optional embodiment, refer to Figure 3 Based on the parameters of entity area ratio and cross-sectional morphology complexity, step S14, which classifies each entity contour into a first contour representing the intersection node of micro-cells and a second contour representing the support column of micro-cells, specifically includes the following steps: S141. Calculate the sum of the actual internal areas of all solid contours within the initial two-dimensional cross-section. Specifically, the computer device reads the feature data matrix output in step S13, which contains the actual internal areas of each solid contour within the current initial two-dimensional cross-section. Proportion of physical area and cross-sectional shape complexity parameters .

[0066] Computer equipment calculates the actual internal area of ​​each entity's outline. By performing scalar accumulation, the sum of the actual internal areas of the initial two-dimensional cross-section is obtained. .

[0067] S142. Calculate the difference between the reference area of ​​the global outer contour of the initial two-dimensional cross section and the sum of the actual internal areas, and determine the ratio of the difference to the reference area as the equivalent porosity of the initial two-dimensional cross section.

[0068] Specifically, the computer device extracts the reference area of ​​the global bounding contour determined in step S13. Computer equipment uses formulas Calculate equivalent porosity The equivalent porosity, as a global state parameter, objectively quantifies the macroscopic structural density of the porous implant at a specific cross-sectional height.

[0069] S143. Based on the equivalent porosity, calculate the area proportion threshold and complexity threshold through a preset mapping function; wherein, the value of the complexity threshold increases with the increase of the equivalent porosity.

[0070] Specifically, computer equipment is based on equivalent porosity The area percentage threshold is calculated using a pre-defined linear algebraic mapping model. With complexity threshold This linear algebraic mapping model contains the following operational formulas: The formula for the area percentage threshold is: ; The formula for the complexity threshold is: .

[0071] in, As a baseline area percentage constant, It serves as the baseline complexity constant; This is the area compensation coefficient. This refers to the complexity expansion coefficient. Those skilled in the art can pre-calibrate and store the specific values ​​of the above constants and coefficients in a computer device based on the results of a pre-analysis of standard porous specimens in three-dimensional mechanical finite element mode or on prior data from historical slice tests.

[0072] It should be understood that, with the equivalent porosity As the size increases, the overall structure of the porous implant becomes sparser, and the absolute volume of a single junction node decreases accordingly, thus affecting the area ratio threshold. The mapping function needs to be linearly reduced accordingly; similarly, structural sparsity leads to more pronounced edge divergence of supports at multi-directional intersections, resulting in an objective increase in the geometric complexity of slice projection, hence the complexity threshold... A positive compensation term must be introduced and linearly increased accordingly. This mechanism avoids the failure of static threshold determination due to changes in structural density.

[0073] To more clearly illustrate the execution logic of the dynamic threshold mapping function, the following example uses a specific set of industrial slice calibration data: Assuming a finite element calibration based on a lattice structure of a specific titanium alloy (such as Ti-6Al-4V), and a pre-stored reference area ratio constant within the computer device... The baseline complexity is 0.8, which is a constant. The area compensation coefficient is 20. The complexity inflation factor is 0.5. The value is 10. When slicing the dense sublayer of a porous implant, if the equivalent porosity of the current initial two-dimensional section is calculated... If the porosity is 0.2 (i.e., 20% porosity), then the computer device calculates it using a mapping function: Area percentage threshold ; Complexity threshold .

[0074] When slicing the sparse hollow layer of a porous implant, if the equivalent porosity of the current initial two-dimensional cross-section is calculated... If the porosity is increased to 0.8 (i.e., 80%), the computer equipment calculates that: Area percentage threshold ; Complexity threshold .

[0075] As shown in the above calculations, when the cross-section transitions from dense (equivalent porosity 0.2) to sparse (equivalent porosity 0.8), the system automatically lowers the area ratio threshold from 0.72 to 0.48, ensuring that nodes with smaller absolute volumes in sparse regions can still be accurately captured. At the same time, the system automatically raises the complexity threshold from 22 to 28, effectively shielding slender pillars from false high complexity caused by edge divergence (for example, a slender pillar with a cross-sectional complexity of 25 would be misjudged as a node in the dense layer, but would be accurately intercepted and classified as a pillar in the sparse layer).

[0076] S144. Traverse the contours of each entity within the initial two-dimensional section and determine the currently traversed entity contour as the contour to be classified.

[0077] Specifically, the computer device reads the contours of each entity in the feature data matrix in a preset array index order, establishes a single entity determination loop, and defines the currently read entity contour as the contour to be classified.

[0078] S145. Obtain the entity area ratio and cross-sectional shape complexity parameters corresponding to the contour to be classified.

[0079] The computer device extracts the proportion of the entity area corresponding to the contour to be classified from the feature data matrix. With cross-sectional shape complexity parameters .

[0080] S146. Determine whether the proportion of the entity area is greater than or equal to the area proportion threshold. If the proportion of the entity area is greater than or equal to the area proportion threshold, proceed to step S148; if the proportion of the entity area is less than the area proportion threshold, proceed to step S147.

[0081] Specifically, computer equipment will account for a certain percentage of the physical area. Area percentage threshold Perform numerical comparison. If... This indicates that the contour to be classified has the physical characteristic of being filled with a large volume of solids, and the computer device directly jumps to step S148; if If so, proceed to step S147.

[0082] S147. Determine whether the cross-sectional shape complexity parameter is greater than or equal to the complexity threshold; if the cross-sectional shape complexity parameter is greater than or equal to the complexity threshold, proceed to step S148; if the cross-sectional shape complexity parameter is less than the complexity threshold, proceed to step S149.

[0083] Specifically, for the unclassified contours that do not meet the area determination criteria, the computer equipment further analyzes their cross-sectional shape complexity parameters. With complexity threshold Perform numerical comparison. If... This indicates that the contour to be classified exhibits a multi-branched shape, and the computer device jumps to execute step S148; if If so, proceed to step S149.

[0084] S148. Classify the contour to be classified as the first contour. The computer device defines the contour to be classified as the first contour and assigns a first category label representing the microscopic cell body intersection node to the contour in the feature data matrix.

[0085] S149. Classify the contour to be classified as a second contour. The computer device defines the contour to be classified as a second contour and assigns a second category label representing the microscopic cell support structure to this contour in the feature data matrix. After completing the traversal and classification of all contours within the initial two-dimensional cross-section, the computer device writes the updated feature data matrix, along with the normal height coordinates of the initial two-dimensional cross-section, into the slice attribute map. This slice attribute map provides the data retrieval basis for the subsequent step S15 to allocate the target slice layer thickness based on the height distribution of the first contour.

[0086] For step S15, refer to Figure 4Based on the distribution of the first contour within each initial two-dimensional cross section, the target slice layer thickness is adaptively allocated to the height range of each initial two-dimensional cross section.

[0087] It should be noted that in the three-dimensional solid of porous implants, different topological regions have different requirements for forming accuracy. Regions containing microcellular junction nodes exhibit multi-directional topological connections and significant changes in surface curvature; while regions containing single microcellular pillars typically extend along a specific direction, with a relatively uniform cross-sectional shape in the height direction. This step builds upon the classification results of the previous steps, constructing a layer thickness adaptive mapping mechanism based on structural feature attributes. Using the initial two-dimensional cross-section as the retrieval benchmark, it dynamically adjusts the target slice layer thickness of the printing equipment in different height ranges based on whether each height profile contains the first contour representing the microcellular junction node. This step provides deterministic Z-axis layering parameters for subsequent variable-thickness slice manufacturing, thereby optimizing overall processing efficiency while ensuring the forming strength of the core mechanical structure.

[0088] In an optional embodiment, step S15, which adaptively allocates the target slice layer thickness to the height interval of each initial two-dimensional cross-section based on the distribution state of the first contour within each initial two-dimensional cross-section, specifically includes the following steps: S151. Determine whether each initial two-dimensional cross section contains the first contour; if the initial two-dimensional cross section contains the first contour, then proceed to step S152; if the initial two-dimensional cross section does not contain the first contour, then proceed to step S153.

[0089] Specifically, the computer device sequentially traverses each initial two-dimensional section in the section sequence according to the Z-axis height coordinate. For the currently traversed initial two-dimensional section, the computer device retrieves the category attributes of each entity contour within that section that were configured in the previous steps.

[0090] If at least one entity contour classified as the first contour is found within the current initial two-dimensional cross section, it is determined that the initial two-dimensional cross section contains the first contour. The computer device then determines that the current height range has captured the microscopic cell intersection node and triggers the execution step S152. If all entity contours within the current initial two-dimensional cross section are classified as second contours, it is determined that the initial two-dimensional cross section does not contain the first contour. The computer device then determines that the current height range only contains microscopic cell pillars and triggers the execution step S153.

[0091] S152. Assign the first slice layer thickness as the target slice layer thickness to the height range where the initial two-dimensional cross section is located.

[0092] Specifically, when the judgment result is that the first contour is included, the computer device sets the preset first slice layer thickness to the target slice layer thickness in the height range of the initial two-dimensional cross section.

[0093] It should be understood that the first slice thickness is a relatively small slice thickness value. Since the microscopic cell intersection nodes bear the main multi-directional mechanical loads, the first slice thickness is allocated to the height range in which they are located. This aims to improve the Z-axis forming resolution of this range, so that the solid processing layer can accurately reproduce the three-dimensional morphology of the intersection nodes and reduce the step effect of the overhanging surface in the intersection area.

[0094] S153. Assign a second slice layer thickness as the target slice layer thickness to the height range where the initial two-dimensional cross section is located; wherein, the first slice layer thickness is less than the second slice layer thickness.

[0095] Specifically, when the judgment result is that the first contour is not included, the computer device sets the preset second slice layer thickness to the target slice layer thickness in the height range of the initial two-dimensional cross section.

[0096] It should be understood that since the cross-section of the microcellular pillar has a small shape change during the continuous extension in the Z-axis direction, using a second slice thickness greater than the first slice thickness within the height range that only includes the second contour can reduce the total number of manufacturing slices in this height range while meeting the forming accuracy of the microcellular pillar, thereby reducing the powder spreading and scanning time and data processing load of the 3D printing equipment.

[0097] After completing the traversal and layer thickness assignment of all initial two-dimensional sections in the section sequence, the computer device outputs an adaptive layered dataset containing height interval coordinates and their corresponding target slice layer thicknesses. This adaptive layered dataset provides direct input parameters for the subsequent step S16 to perform re-slicing.

[0098] For step S16 above, within each height range, the porous implant 3D model is re-sliced ​​according to the assigned target slice thickness to generate the target 2D cross section.

[0099] It should be noted that step S16, based on the adaptive layered dataset output in step S15, drives the geometric intersection algorithm to perform manufacturing-level physical layering of the porous implant 3D model. Unlike the initial probe slices that use a fixed micro-layer thickness for full model scanning, step S16 aims to call differentiated layer height parameters in different height domains based on the stress requirements of local topological features. The computer device generates a series of non-equidistant cutting planes along the forming normal direction, according to the allocated target slice layer thickness, and calculates the geometric intersection lines between each cutting plane and the surface mesh of the porous implant 3D model, thereby generating the final target 2D cross-section used for machining instruction planning. This step achieves a comprehensive balance between the core node forming accuracy and the overall data processing scale in the overall height dimension of the 3D model.

[0100] In an optional embodiment, considering that there are abrupt changes in slice layer thickness in adjacent height intervals, stress concentration defects or surface step distortion are likely to occur at the interlayer junctions of the physically printed entity.

[0101] To solve this problem, refer to Figure 5 Step S16 specifically includes the following steps: S161. Identify a first height interval and a second height interval that are adjacent in the slice normal direction, wherein the first height interval is allocated with a first slice layer thickness and the second height interval is allocated with a second slice layer thickness.

[0102] Specifically, the computer device reads the adaptive hierarchical dataset, traverses each height interval along the Z-axis normal direction, and compares the target slice layer thickness values ​​of adjacent intervals in turn. If the layer thickness values ​​assigned to two adjacent height intervals are not equal, the computer device defines these two intervals as the first height interval and the second height interval, respectively, and extracts the specific values ​​of the corresponding first slice layer thickness and second slice layer thickness.

[0103] S162. The boundary area between the first height interval and the second height interval is defined as the layer thickness transition zone.

[0104] Specifically, the computer device extracts a spatial region with a specific Z-axis span on both sides of the physical interface between the first height range and the second height range based on a preset transition height threshold or a set number of transition slice layers, and defines this spatial region as the layer thickness transition zone.

[0105] S163. Within the layer thickness transition zone, at least one transition slice elevation plane is generated according to the preset step size. S164. Assign a third slice thickness to the transition slice elevation plane, and re-slice the 3D model of the porous implant. The value of the third slice thickness is between the first slice thickness and the second slice thickness to achieve a smooth gradient transition of the slice thickness in the longitudinal direction.

[0106] To automate the execution of the computer equipment, a numerical interpolation model is used to calculate the aforementioned step size and the thickness of the third slice layer. Specifically, taking a linear gradient transition mechanism as an example, the computer equipment calculates the thickness of the first slice layer... Second slice thickness and the pre-defined number of transition slice layers Calculate the step size of the ladder The formula for calculating the step length of the ladder is: Subsequently, the computer equipment sequentially generates [the material] along the Z-axis within the layer thickness transition zone. The transition slice elevation plane. For the first... A transition slice elevation plane is assigned a corresponding third slice layer thickness by computer equipment. For scenarios where the slice transitions from a thin to a thick layer along the slicing direction, the formula for calculating the thickness of the third slice layer is: in, The value ranges from 1 to A positive integer. For scenarios transitioning from thick to thin layers, the corresponding layer-by-layer decreasing operation is performed.

[0107] Based on the third slice thickness sequence calculated by the interpolation above, the computer equipment drives the intersection operation between the plane and the surface mesh of the 3D model to generate the corresponding transitional 2D cross section at the height elevation. This operation mechanism uses a deterministic mathematical interpolation function to generate the third slice thickness with an intermediate gradient in the spatial normal direction, thereby eliminating the interlayer bonding force fault defect caused by the numerical step of the slice thickness during the solid forming process, and ensuring the smooth evolution of the slice thickness in the vertical dimension from the physical manufacturing level.

[0108] For step S17, the first contour and the second contour within the target two-dimensional cross section are simplified respectively to generate a target contour set.

[0109] It should be noted that after the intersection operation of the 3D model slices, the generated original 2D cross-sectional contour usually contains a massive number of redundant coordinate points. This redundant data will greatly consume the memory resources of the 3D printing equipment and slow down the path planning efficiency. Step S17 builds upon the previous feature classification results and introduces a differentiated contour thinning mechanism for the target 2D cross-section. The computer equipment uses different levels of tolerance parameters to geometrically filter the contour point set based on the stress properties of the contour (intersection nodes or connecting pillars). By eliminating redundant coordinate points that have little impact on the macroscopic shape, the algorithm achieves lightweight dimensionality reduction of the slice data while ensuring the fidelity of the core topology formation, and finally outputs a target contour set suitable for direct parsing by the equipment's underlying layer.

[0110] In an optional embodiment, refer to Figure 6 Step S17, which involves simplifying the first and second contours within the target two-dimensional cross-section and generating a target contour set, specifically includes the following steps: S171. Determine the first or second contour within the target two-dimensional cross section as the contour to be processed, wherein the contour to be processed consists of multiple contour points arranged in a connected order.

[0111] Specifically, the computer device extracts the target two-dimensional cross-section output in step S16. For any entity contour within this cross-section, the computer device obtains an ordered set of coordinate points constituting the boundary of the entity, and defines this set of coordinate points as the contour to be processed.

[0112] S172. Determine whether the contour to be processed is the first contour or the second contour. If the contour to be processed is the first contour, proceed to step S173; if the contour to be processed is the second contour, proceed to step S174.

[0113] Specifically, the computer device reads the slice attribute map and queries the category label configured in the previous steps for the contour to be processed. If the label represents a microscopic cell intersection node, it is determined to be the first contour, and the program execution path jumps to step S173; if the label represents a microscopic cell support, it is determined to be the second contour, and the program execution path jumps to step S174.

[0114] S173. Configure the first chord height error threshold as the target chord height error threshold.

[0115] Specifically, for the first contour, the computer device calls a preset first chord height error threshold and assigns it to the target chord height error threshold parameter. It should be understood that the first chord height error threshold is a very small physical tolerance (e.g., set to 0.005 mm), which is intended to perform extremely conservative data culling on multi-directionally stressed node regions, preserving their original complex topological curvature to the greatest extent possible, and preventing the introduction of artificial stress concentration points at physical intersections due to oversimplification of polygons.

[0116] S174. Determine the target chord height error threshold based on the second chord height error threshold, wherein the first chord height error threshold is less than the second chord height error threshold.

[0117] Specifically, for the second contour, the computer device calls a preset second chord height error threshold and assigns it to the target chord height error threshold parameter. It should be understood that the value of the second chord height error threshold is strictly greater than the first chord height error threshold (e.g., set to 0.03 mm). Since the support area corresponding to the second contour has the geometric characteristic of extending straight along a specific axis, using a larger chord height error threshold can eliminate collinear or nearly collinear redundant coordinate points by hundreds of times within an acceptable range without causing macroscopic shape distortion, thereby significantly freeing up system memory in non-core load-bearing areas.

[0118] S175. Along the connected sequence, establish a traversal window containing three consecutive contour points.

[0119] Specifically, the computer device initializes a set of sliding pointers in the ordered set of coordinate points of the contour to be processed, extracts three consecutive adjacent contour points into the memory computing unit, and forms the initial traversal window. For ease of description, these three contour points are sequentially defined as the first contour point. Intermediate contour points and tail end contour points .

[0120] S176. Calculate the vertical chord distance between the middle contour point in the middle position within the traversal window and the lines connecting the two side contour points.

[0121] Specifically, the computer equipment uses the point-to-line distance formula to calculate the geometric error. Let the coordinates of the initial contour point be... The coordinates of the middle contour point are The coordinates of the tail contour point are Computer equipment calculates intermediate contour points. to the straight line vertical chord height distance Its mathematical formula is: This distance Objectively characterizes the situation if intermediate contour points are deleted. And directly connected The resulting maximum deviation in geometry.

[0122] S177. Determine whether the vertical chord height distance is less than the target chord height error threshold; if the vertical chord height distance is less than the target chord height error threshold, proceed to step S178; if the vertical chord height distance is greater than or equal to the target chord height error threshold, proceed to step S179.

[0123] The computer equipment will calculate the vertical chord height distance. The data is numerically compared with the target chord height error threshold allocated in step S173 or S174 to determine the subsequent data discard logic. S178: Delete intermediate contour points and extract the next contour point along the connected sequence to complete the traversal window, thereby updating the three consecutive contour points for the next calculation. Specifically, if the vertical chord height distance... The error is less than the target chord height threshold, indicating that the intermediate contour point... Located on a gentle straight line segment, this is redundant data. The computer will delete this intermediate contour point from the point set. Then, maintain the initial outline point. With tail end contour point The position within the traversal window remains unchanged, and points are extracted from the point set. The next contour point Introduce a traversal window. At this point, the updated three consecutive contour points become... The system then returns to step S176 to execute the next calculation.

[0124] S179. Retain the intermediate contour points and slide the traversal window backward along the connected order to update the three consecutive contour points for the next calculation.

[0125] Specifically, if the vertical chord height distance A value greater than or equal to the target chord height error threshold indicates that the intermediate contour point... Located at geometric corners or abrupt changes in curvature, these are key feature data points. Computer equipment retains these intermediate contour points in the point set. Then, the system slides backward through the window, retrieving the original center contour points. As the new beginning contour point, the original end contour point As the new intermediate contour point, and extract the next contour point from the point set. This serves as the new tail contour point. The updated three consecutive contour points then become... The system then returns to step S176 to execute the next calculation.

[0126] The computer device repeatedly executes the above-mentioned discrimination and update operations for traversing the window until a global traversal of the current contour to be processed is completed. After all the first and second contours have been processed, the computer device outputs a set of target contours consisting of a simplified set of coordinate points, providing a final data source for the subsequent step S18 to compile the entity printing path file.

[0127] In an optional embodiment, refer to Figure 7 Considering the significant stress concentration sensitivity in the region transitioning from microcellular intersection nodes to microcellular pillars, and to avoid introducing geometric fault defects due to abrupt changes in simplification accuracy between adjacent contours, step S174, which determines the target chord height error threshold based on the second chord height error threshold, specifically includes the following steps: S1741. Calculate the shortest plane Euclidean distance between the second contour within the target two-dimensional cross section and the first contour closest to it.

[0128] Specifically, the computer device extracts the coordinates of the point set of the current second contour to be processed, as well as the coordinates of the point set of all first contours within the same target two-dimensional cross-section. The computer device uses a spatial distance traversal algorithm or a spatial acceleration partitioning structure (such as a KD-tree) to calculate the minimum linear Euclidean distance between any coordinate point on the current second contour and any coordinate point on any first contour within the cross-section, and defines this minimum value as the shortest planar Euclidean distance. This parameter objectively quantifies the degree of geometric deviation of the current support section from the core stress node.

[0129] S1742. Determine whether the shortest plane Euclidean distance is less than the preset smooth transition threshold; if the shortest plane Euclidean distance is less than the preset smooth transition threshold, then execute steps S1743-S1745; if the shortest plane Euclidean distance is greater than or equal to the preset smooth transition threshold, then execute step S1746.

[0130] Specifically, the computer device invokes a smooth transition threshold pre-stored in memory. This smooth transition threshold characterizes the physical radius limit of stress propagation outward from the junctions of microscopic cells. The computer device will calculate the obtained... and Perform numerical comparison. If... This indicates that the current second profile is within the stress influence buffer of the node, and the computer device triggers a dynamic interpolation branch (steps S1743 to S1745); if This indicates that the current second contour belongs to a pure connection pillar region far from the node, and the computer device triggers a regular assignment branch (step S1746).

[0131] S1743. Mark the second contour as a transition contour; S1744. Based on the shortest plane Euclidean distance, the first chord height error threshold and the second chord height error threshold, the dynamic transition error threshold of the corresponding transition profile is calculated through a preset interpolation function. S1745. The dynamic transition error threshold is determined as the target chord height error threshold.

[0132] For the second profile located within the stress influence buffer zone, the computer device assigns a transition category attribute label and activates a preset mathematical interpolation model. Specifically, the computer device uses the obtained shortest planar Euclidean distance... Smooth transition threshold First chord height error threshold and the second chord height error threshold Calculate the dynamic transition error threshold The mathematical interpolation model is as follows: After the calculation is completed, the computer device will set the dynamic transition error threshold. The value is assigned to the target chord height error threshold parameter for subsequent data removal by the thinning window function.

[0133] S1746. The second chord height error threshold is determined as the target chord height error threshold; wherein, the value of the dynamic transition error threshold is between the first chord height error threshold and the second chord height error threshold, and its value gradually approaches the first chord height error threshold as the Euclidean distance of the shortest plane decreases.

[0134] For the second profile far from the node, the computer device directly applies the conventional second chord height error threshold. Assigning a value to the target chord height error threshold parameter allows for maximum contour simplification.

[0135] It should be understood that, using the above linear spatial interpolation model, when the second contour is extremely close to the first contour (i.e.... (approaching 0), its assigned error threshold Forced approximation of the minimum first chord high error threshold As the second contour gradually moves away from the node (i.e. From 0 (growth), its error threshold Linear relaxation to the second chord height error threshold This computational mechanism uses deterministic coordinate distance as a driving variable to construct a dimensionality-reduced field that smoothly evolves from dense to sparse within the same height section, eliminating physical seams in the data from its source.

[0136] To further illustrate the feasibility of this spatial gradient smoothing model, the following example parameters are set: First chord height error threshold. The second chord height error threshold is 0.005 mm (corresponding to high node precision). The threshold for smooth transition is 0.025 mm (corresponding to low precision for the support column). It is 1.0 mm.

[0137] If a second profile is close to a node, its shortest planar Euclidean distance is calculated. The value is 0.2 mm. The computer device is substituted into the interpolation model to obtain its dynamic transition error threshold. Millimeters.

[0138] If the other second contour is far from the node, the calculation is as follows It is 0.8 mm. Substituting this into the model yields... Millimeters.

[0139] It should be noted that the target contour set output in step S17 only represents the two-dimensional geometric boundaries of the porous implant at various height sections. To drive the additive manufacturing equipment (such as selective laser melting or electron beam melting equipment) to complete the layer-by-layer melting and stacking of solid materials, the motion trajectory of the energy source within these geometric boundaries must be planned. In step S18, the computer device receives the target contour set after differential error thinning and generates closed boundary scanning paths and internal filling scanning paths in combination with preset process parameters; subsequently, the coordinate data of the above geometric paths, together with the Z-axis layer thickness parameters determined in step S15, are syntactically encapsulated and compiled to generate a numerical control execution file that can be directly parsed by the bottom-level controller of the additive manufacturing equipment, thereby completing the final leap from digital virtual model to physical manufacturing instructions.

[0140] In an optional embodiment, step S18, which generates 3D printing-ready data based on the target contour set, specifically includes the following steps: S181. The computer device reads the target slice layer thickness and target contour set corresponding to the two-dimensional cross section of the target.

[0141] Specifically, the computer device extracts the target contour set (containing the simplified coordinate point set of the first contour and the second contour) generated after the filtering process in step S17 from the system cache or array, taking the cross section as the unit, and simultaneously retrieves the target slice layer thickness (as the step increment reference amount in the Z-axis direction) allocated for the height interval of the target two-dimensional cross section in step S15.

[0142] S182. The computer device generates a filling scan path within the closed boundary defined by the target contour set using a geometric intersection algorithm.

[0143] Specifically, since the spot diameter or molten pool width of the 3D printing energy source has fixed physical dimensions, the computer device uses a scanline fill algorithm to generate the internal forming trajectory. The computer device generates a set of parallel virtual ray arrays based on a preset hatch spacing; subsequently, it calculates the geometric intersections of this parallel virtual ray array with the boundaries of each polygon in the target contour set. According to the odd-even rule, the computer device retains ray segments located inside the closed solid boundaries and transforms them into interconnected fill scan paths. Further, for the first contour (microscopic cell intersection node) labeled with the first category label and the second contour (microscopic cell pillar) labeled with the second category label, the computer device calls different fill spacings or scanline deflection angles that match their structural characteristics to optimize the local thermal stress distribution during the forming process.

[0144] S183. The computer device converts the target contour set into a boundary scan path and integrates it with the filling scan path into a single-layer path map.

[0145] Specifically, the computer equipment directly transforms the ordered set of points in the target contour set into a closed boundary scan path according to a preset process sequence (e.g., "contour first, then fill" or "fill first, then contour"). Subsequently, the endpoint coordinates of the boundary scan path and the fill scan path are vectorized to establish a single-layer path map that includes action states such as laser on, laser off, jump, and mark.

[0146] S184. The computer device compiles the single-layer path map and the target slice layer thickness into an executable 3D printing data file.

[0147] Specifically, the computer device uses a post-processor to process the two-dimensional Cartesian plane coordinates recorded in the single-layer path graph. This is converted into absolute physical coordinates of the galvanometer system or linear motor in the 3D printing equipment; simultaneously, the target slice layer thickness parameter is accumulated to the current global height elevation of the system to generate three-dimensional spatial coordinates. Finally, the computer equipment encapsulates the motion trajectory data with spatial coordinates, laser power control parameters, and scanning speed parameters into a machine-readable code format (such as standard G-code files, CLI universal layer interface files, or SLC slice files) according to the syntax protocol supported by the target 3D printing equipment. This data file serves as the final driving source for the physical manufacturing equipment.

[0148] It should be understood that through the complete compilation in step S18, the dimensionality reduction effect of the adaptive slicing algorithm of the present invention is intuitively reflected in the generated data file. Taking the microscopic cellular support pillar (second contour) contained in a certain height section as an example, due to the significant thinning based on the second chord height error threshold in step S17, the number of coordinate points of its contour boundary is sharply reduced; in the scan line intersection calculation (step S182) and data file encapsulation (step S184) in step S18, the number of floating-point operations required by the computer device to process the pillar contour and the number of generated code lines decrease geometrically. Compared with the traditional constant slicing strategy with equal layer thickness and global minimum chord height, the executable 3D printing data file generated by the present invention achieves a significant reduction in total capacity, effectively avoiding memory overflow or communication transmission delay of the underlying industrial control computer of the 3D printing equipment caused by excessively large files, thereby achieving a comprehensive improvement from algorithm logic to hardware performance.

[0149] In summary, the method provided in this application extracts the geometric features of each entity contour from the initial two-dimensional cross-section and constructs a dynamic threshold mapping model based on equivalent porosity, achieving accurate identification of intersection nodes and connecting supports. In the subsequent adaptive planning process, the computer device uses the distribution state of the first contour to guide the Z-axis layer thickness allocation and performs gradient thinning of the second contour in the lateral dimension based on spatial distance weights. This series of operations transforms what was originally difficult to process, such as... Figure 9 The complex biomimetic trabecular bone model shown in (c) is transformed into a set of target contours that retain the core mechanical topological features while possessing a high degree of data lightweighting.

[0150] To further verify the practical industrial application effect of the 3D printing data processing method described in this application, refer to Figure 8 This application embodiment demonstrates the physical entity and microstructure of a porous implant obtained by using the execution data generated by the above method and preparing it through additive manufacturing equipment.

[0151] Specifically, Figure 8 (a) shows a spatial topology diagram of a three-dimensional model of a porous implant, which contains a complex spatial lattice structure. Figure 8 (b) Figure 8 (c) and Figure 8 The images in (d) are scanning electron microscope images of the porous implant physical entity formed using the data processing method of this application, taken in the X, Z and Y directions.

[0152] It should be understood that, through observation Figure 8 Based on the anisotropic microstructure, the following technical verification conclusions, which correspond to the aforementioned algorithm steps, can be drawn: First, in the core region of mechanical load-bearing, namely the intersection of microscopic cells (such as... Figure 8 The robust and multi-directionally intersecting fusion regions shown in the microscopic images (b), (c), and (d) exhibit dense formation quality and complete geometric contours. This demonstrates the effectiveness of the aforementioned steps in accurately identifying the first contour and in using a smaller first chord height error threshold for conservative simplification, ensuring that no topological distortion or stress concentration defects occur at the intersection nodes due to excessive data thinning.

[0153] Second, in the direction of forming height, the key reference is... Figure 8 In the middle (c) image, the microstructure along the Z direction shows a smooth and continuous surface, with no significant step effect or interlayer gaps caused by abrupt changes in layer thickness. This confirms the physical feasibility of the adaptive layer thickness allocation logic and the smooth gradient interpolation mechanism in the layer thickness transition zone, ensuring that the step effect in the overhang region can still be eliminated under variable layer thickness printing conditions, and exhibiting extremely high axial forming accuracy.

[0154] Third, in the connection region that bears a single conductive force, i.e., the microscopic cell pillar (such as... Figure 8 (b), (c), and (d) The slender rod-shaped parts shown in the photomicrographs) have a macroscopic morphology similar to Figure 8 The digital model shown in (a) remains highly consistent. Although this region corresponds to the second contour in the algorithm and has undergone data thinning based on a large second chord height error threshold, microscopic observations indicate that its geometric continuity and surface quality remain within the engineering tolerances for orthopedic medical implants.

[0155] In summary, Figure 8 The physical solid forming results shown in (b), (c), and (d) fully demonstrate that the data processing method provided in this application can effectively ensure the mechanical forming strength of key nodes of porous implants and the forming fidelity of the overall biomimetic structure while significantly compressing the coordinate data of 3D printed slices and reducing the computing power consumption of the underlying computer equipment.

[0156] Reference Figure 8The forming effect shown demonstrates that additive manufacturing using the execution data generated in this application yields a physical entity with excellent geometric fidelity at the microscopic scale. Microscopic photography reveals no step effect caused by data undersampling at the intersection nodes, and the support region maintains good connectivity even after significant data reduction. This fully confirms the industrial applicability and technological advancement of the dimensionality reduction slicing algorithm for distinguishing nodes and supports described in this application when processing cutting-edge complex medical implants.

[0157] Reference Figure 10 This embodiment provides a 3D printing data processing system 20 for porous implants. This system is constructed based on the logical partitioning of a computer program and specifically includes: Model acquisition module 21 is configured to acquire a three-dimensional model of a porous implant; The initial slicing module 22 is configured to slice the three-dimensional model of the porous implant to generate a sequence of sections containing multiple initial two-dimensional sections; The feature calculation module 23 is configured to extract the entity contours within each initial two-dimensional cross section and calculate the entity area ratio and cross section morphology complexity parameters of each entity contour. The contour classification module 24 is configured to classify each entity contour into a first contour representing the intersection node of micro cells and a second contour representing the support of micro cells based on the entity area ratio and cross-sectional shape complexity parameters. The layer thickness allocation module 25 is configured to adaptively allocate the target slice layer thickness to the height range of each initial two-dimensional section based on the distribution state of the first contour within each initial two-dimensional section. The adaptive slicing module 26 is configured to re-slice the porous implant 3D model according to the assigned target slice layer thickness in each height range to generate a target 2D cross section. The contour simplification module 27 is configured to simplify the first contour and the second contour within the target two-dimensional cross section and generate a target contour set; wherein the contour simplification accuracy of the first contour is higher than that of the second contour, so as to reduce the consumption of computing resources and ensure the mechanical forming strength at the intersection node. The data generation module 28 is configured to generate 3D printing-ready data based on a set of target contours.

[0158] It should be understood that the specific operational logic and execution mechanism of each virtual functional module in the 3D printing data processing system 20 for porous implants described above correspond one-to-one with each step (steps S11 to S18) in the 3D printing data processing method for porous implants described in the foregoing embodiments of this application. The specific working process of each module, the calculation method of parameter thresholds, and the technical effects that can be achieved can all be directly referred to in the detailed description in the foregoing method embodiments. To avoid redundancy in the specification, they will not be repeated here.

[0159] Embodiments of the present invention also provide a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a 3D printing data processing method for porous implants according to embodiments of the present invention.

[0160] Embodiments of the present invention also provide a computer program product, including a computer program, wherein the computer program, when executed by a computer processor, is used to cause the computer to perform the 3D printing data processing method for porous implants according to embodiments of the present invention.

[0161] Embodiments of the present invention also provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, which, when executed by the at least one processor, causes the electronic device to perform the 3D printing data processing method for porous implants according to embodiments of the present invention.

[0162] refer to Figure 11 The present invention will now describe a structural block diagram of an electronic device that can serve as an embodiment of the present invention, serving as an example of a hardware device applicable to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0163] like Figure 11As shown, the electronic device includes a computing unit 901, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 902 or a computer program loaded into a random access memory (RAM) 903 from a storage unit 908. The RAM 903 may also store various programs and data required for the operation of the electronic device. The computing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0164] Multiple components in the electronic device are connected to I / O interface 905, including: input unit 906, output unit 907, storage unit 908, and communication unit 909. Input unit 906 can be any type of device capable of inputting information into the electronic device. Input unit 906 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of the electronic device. Output unit 907 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 908 may include, but is not limited to, disks and optical discs. Communication unit 909 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, and / or wireless communication transceivers, such as Bluetooth devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0165] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, CPUs, graphics processing units (GPUs), various special-purpose artificial intelligence (AI) computing units, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention can be implemented as computer programs tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 902 and / or communication unit 909. In some embodiments, the computing unit 901 can be configured to perform the methods described above by any other suitable means (e.g., by means of firmware).

[0166] Computer programs for implementing the methods of embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0167] In the context of embodiments of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, or infrared systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0168] It should be noted that the term "comprising" and its variations used in the embodiments of this invention are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "a plurality" mentioned in the embodiments of this invention are illustrative and not restrictive, and those skilled in the art should understand that unless explicitly indicated otherwise in the context, they should be understood as "one or more".

[0169] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the embodiments of this invention are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0170] The steps described in the method embodiments provided by the present invention can be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0171] The term "embodiment" in this specification refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily imply the same embodiment, nor does it imply independence or alternativeity from other embodiments. The various embodiments in this specification are described in a related manner, with reference to each other for similar or identical parts. In particular, for apparatus, device, and system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, and relevant details are referred to in the description of the method embodiments.

[0172] The above-described embodiments are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. A 3D printing data processing method for porous implants, characterized in that, include: Obtain a 3D model of the porous implant; The porous implant 3D model is sliced ​​to generate a cross-sectional sequence containing multiple initial 2D cross-sections; Extract the solid contours within each of the initial two-dimensional cross sections, and calculate the solid area ratio and cross-sectional shape complexity parameters of each solid contour. Based on the entity area ratio and the cross-sectional shape complexity parameter, each entity contour is classified into a first contour representing the intersection node of micro-cells and a second contour representing the support column of micro-cells. Based on the distribution state of the first contour within each initial two-dimensional cross section, the target slice layer thickness is adaptively allocated to the height interval where each initial two-dimensional cross section is located; Within each of the height ranges, the porous implant 3D model is re-sliced ​​according to the assigned target slice thickness to generate a target 2D cross section; The first contour and the second contour within the target two-dimensional cross section are simplified respectively to generate a target contour set; wherein the contour simplification accuracy of the first contour is higher than that of the second contour, so as to reduce the consumption of computing resources and ensure the mechanical forming strength at the intersection node. Data suitable for 3D printing is generated based on the target contour set.

2. The 3D printing data processing method for porous implants according to claim 1, characterized in that, The calculation of the entity area ratio and cross-sectional shape complexity parameters of each entity contour includes: Calculate the reference area of ​​the global bounding contour of the initial two-dimensional cross section; Traverse each of the entity contours within the initial two-dimensional cross section, calculate the actual internal area of ​​the target entity contour, and the ratio of the actual internal area to the reference area, and determine it as the entity area ratio; The ratio of the square of the perimeter of the target entity's outline to its actual internal area is calculated and determined as the cross-sectional morphology complexity parameter.

3. The 3D printing data processing method for porous implants according to claim 1, characterized in that, Based on the entity area ratio and the cross-sectional morphology complexity parameter, each entity contour is classified into a first contour representing the intersection node of micro-cells and a second contour representing the support column of micro-cells, including: Calculate the sum of the actual internal areas of all solid contours within the initial two-dimensional cross-section; Calculate the difference between the reference area of ​​the global outer contour of the initial two-dimensional cross section and the sum of the actual internal areas, and determine the ratio of the difference to the reference area as the equivalent porosity of the initial two-dimensional cross section; Based on the equivalent porosity, an area proportion threshold and a complexity threshold are calculated using a preset mapping function; wherein, the value of the complexity threshold increases as the equivalent porosity increases; Traverse each of the entity contours within the initial two-dimensional cross section, and determine the currently traversed entity contour as the contour to be classified; Obtain the entity area ratio and cross-sectional shape complexity parameters corresponding to the contour to be classified; Determine whether the proportion of the entity area is greater than or equal to the area proportion threshold, and whether the cross-sectional shape complexity parameter is greater than or equal to the complexity threshold; If the proportion of the entity area is greater than or equal to the area proportion threshold, or the cross-sectional shape complexity parameter is greater than or equal to the complexity threshold, then the contour to be classified is classified as the first contour. If the proportion of the entity area is less than the area proportion threshold, and the cross-sectional shape complexity parameter is less than the complexity threshold, then the contour to be classified is classified as the second contour.

4. The 3D printing data processing method for porous implants according to claim 1, characterized in that, The step of adaptively allocating target slice layer thickness to the height interval of each initial two-dimensional cross-section based on the distribution state of the first contour within each initial two-dimensional cross-section includes: Determine whether each of the initial two-dimensional cross sections contains the first contour; If the initial two-dimensional cross section contains the first contour, then the first slice layer thickness is assigned as the target slice layer thickness for the height interval where the initial two-dimensional cross section is located; If the initial two-dimensional cross section does not include the first contour, then the second slice layer thickness is assigned as the target slice layer thickness for the height interval where the initial two-dimensional cross section is located; The thickness of the first slice layer is less than the thickness of the second slice layer.

5. The 3D printing data processing method for porous implants according to claim 1, characterized in that, The step of simplifying the first contour and the second contour within the target two-dimensional cross-section and generating a target contour set includes: The first contour or the second contour within the target two-dimensional cross section is determined as the contour to be processed, wherein the contour to be processed consists of multiple contour points arranged in a connected order; If the contour to be processed is the first contour, then the first chord height error threshold is configured as the target chord height error threshold; If the contour to be processed is the second contour, then the target chord height error threshold is determined based on the second chord height error threshold, wherein the first chord height error threshold is less than the second chord height error threshold; Along the connectivity sequence, a traversal window containing three consecutive contour points is established; Calculate the vertical chord height distance between the middle contour point in the middle position within the traversal window and the lines connecting the two side contour points. If the vertical chord height distance is less than the target chord height error threshold, then the intermediate contour point is deleted, and the next contour point is extracted along the connected sequence to complete the traversal window, thereby updating the three consecutive contour points calculated in the next iteration. If the vertical chord height distance is greater than or equal to the target chord height error threshold, the intermediate contour point is retained, and the traversal window is slid backward along the connected sequence to update the three consecutive contour points for the next calculation.

6. The 3D printing data processing method for porous implants according to claim 5, characterized in that, Determining the target chord height error threshold based on the second chord height error threshold includes: Calculate the shortest planar Euclidean distance between the second contour within the target two-dimensional cross section and the first contour that is closest to it; Determine whether the shortest plane Euclidean distance is less than a preset smooth transition threshold; If the shortest plane Euclidean distance is less than a preset smooth transition threshold, the second contour is marked as a transition contour, and based on the shortest plane Euclidean distance, the first chord height error threshold and the second chord height error threshold, a dynamic transition error threshold corresponding to the transition contour is calculated by a preset interpolation function, and the dynamic transition error threshold is determined as the target chord height error threshold. If the shortest plane Euclidean distance is greater than or equal to a preset smooth transition threshold, then the second chord height error threshold is determined as the target chord height error threshold. The value of the dynamic transition error threshold is between the first chord height error threshold and the second chord height error threshold, and its value gradually approaches the first chord height error threshold as the Euclidean distance of the shortest plane decreases.

7. The 3D printing data processing method for porous implants according to claim 4, characterized in that, Within each of the stated height intervals, the porous implant 3D model is re-sliced ​​according to the allocated target slice thickness to generate a target 2D cross-section, including: Identify a first height interval and a second height interval that are adjacent in the slice normal direction, wherein the first height interval is allocated with the first slice layer thickness and the second height interval is allocated with the second slice layer thickness; The boundary region between the first height range and the second height range is defined as the layer thickness transition zone; Within the layer thickness transition zone, at least one transition slice elevation plane is generated according to a preset step size. A third slice thickness is assigned to the transition slice elevation plane, and the porous implant 3D model is re-sliced. The value of the third slice thickness is between the first slice thickness and the second slice thickness to achieve a smooth gradient transition of the slice thickness in the longitudinal direction.

8. A 3D printing data processing system for porous implants, characterized in that, include: The model acquisition module is configured to acquire a 3D model of the porous implant. The initial slicing module is configured to slice the three-dimensional model of the porous implant to generate a sequence of cross-sections containing multiple initial two-dimensional cross-sections; The feature calculation module is configured to extract the entity contours within each of the initial two-dimensional cross sections and calculate the entity area ratio and cross section morphology complexity parameters of each entity contour. The contour classification module is configured to classify each of the entity contours into a first contour representing the intersection node of microcells and a second contour representing the support column of microcells based on the entity area ratio and the cross-sectional shape complexity parameter. The layer thickness allocation module is configured to adaptively allocate the target slice layer thickness to the height interval of each initial two-dimensional section based on the distribution state of the first contour within each initial two-dimensional section. An adaptive slicing module is configured to re-slice the porous implant 3D model within each of the height intervals according to the allocated target slice layer thickness to generate a target 2D cross section; The contour simplification module is configured to simplify the first contour and the second contour within the target two-dimensional cross section and generate a target contour set; wherein the contour simplification accuracy of the first contour is higher than that of the second contour, so as to reduce the consumption of computing resources and ensure the mechanical forming strength at the intersection node. The data generation module is configured to generate 3D printing-ready data based on the target contour set.

9. An electronic device, characterized in that, include: Memory, used to store one or more computer instructions; The processor is communicatively connected to the memory; When one or more computer instructions are executed by the processor, the 3D printing data processing method for porous implants as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that: When the computer instructions are executed by the processor, the 3D printing data processing method for porous implants as described in any one of claims 1 to 7 is implemented.

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