A stereomodelling method based on additive printing

Abstract

The application discloses a kind of stereoscopic model production methods based on additive printing, comprising: obtaining standard model data and production requirement data;Based on production requirement data, the standard model data is biomechanically simulated and optimized, and the optimization data of soft tissue deformation law of human body under target posture is generated;Based on optimization data, the standard model data is designed by lattice filling, and the internal support structure with gradient density distribution is generated, to obtain target model data;The multi-material 3D printing slicing processing is carried out to the target model data, to plan the printing strategy corresponding to different body parts in target model data;Based on printing strategy, drive multi-material 3D printing equipment to print the multiple body part models corresponding to target model data, and the multiple body part models are used to assemble to form stereoscopic model finished product. Eliminate the parting line residue and flash existing in traditional process, and give consideration to structural stability and light weight.

Description

Technical Field

[0001] This application relates to the field of human body model manufacturing technology, and in particular to a method for making three-dimensional models based on additive printing. Background Technology

[0002] Mannequins, as crucial props for displaying clothing in shopping mall windows, have their manufacturing process directly impacting the display effect. Traditional mainstream methods employ hollow blow molding or injection molding. Hollow blow molding involves heating a plastic preform and injecting compressed air to expand it and fit it into the mold cavity, forming a hollow model after cooling. While this process allows for mass production, high mold costs and long development cycles prevent personalized customization and rapid iteration. Furthermore, models produced using this method often have flash or residual parting lines, requiring manual cleaning and trimming. Additionally, hollow blow-molded mannequins are prone to deformation and lack mechanical realism, while injection-molded solid mannequins are heavy and consume more material. Clearly, traditional mannequin manufacturing processes do not meet current production demands. Summary of the Invention

[0003] This application provides a method for producing 3D mannequins based on additive printing, in order to solve the technical problem that the production process of traditional mannequins does not meet current production needs.

[0004] Firstly, this application provides a method for creating a three-dimensional model based on additive printing, comprising:

[0005] Obtain standard model data and production requirement data;

[0006] Based on the aforementioned production requirements data, the standard model data is optimized through biomechanical simulation to generate optimized data on the soft tissue deformation law of the human body under the target posture.

[0007] Based on the optimized data, lattice filling design is performed on the standard model data to generate an internal support structure with gradient density distribution, thereby obtaining the target model data;

[0008] The target model data is sliced ​​using multi-material 3D printing to plan printing strategies for different body parts in the target model data.

[0009] Based on the printing strategy, a multi-material 3D printing device is driven to print multiple body part models corresponding to the target model data. The multiple body part models are then assembled to form a three-dimensional model.

[0010] In some embodiments, the step of performing biomechanical simulation optimization on the standard model data based on the manufacturing requirement data to generate optimized data on the soft tissue deformation law of the human body under the target posture includes:

[0011] Call the skeletal muscle multibody dynamics model corresponding to the standard model data;

[0012] Based on the target posture parameters in the production requirement data, the skeletal muscle multibody dynamics model is driven, and the activation state of each muscle group in the skeletal muscle multibody dynamics model under the target posture, as well as the distribution of mechanical loads acting on the bone and skin boundaries, are calculated to obtain muscle activation and joint force data.

[0013] Using the muscle activation and joint force data as boundary conditions, finite element mechanical simulation is performed on the soft tissue region of the standard model data to obtain the deformation mesh of the soft tissue region under the target posture.

[0014] The vertex displacement field of the deformed mesh is correlated with the internal stress field, and a three-dimensional density distribution field is generated as the optimization data.

[0015] In some embodiments, the step of using the muscle activation and joint force data as boundary conditions to perform finite element mechanical simulation on the soft tissue region of the standard model data to obtain the deformation mesh of the soft tissue region under the target posture includes:

[0016] The muscle activation and joint force data are converted into boundary loads and displacement constraints applied to the soft tissue region.

[0017] Based on the boundary load and displacement constraints, the soft tissue region is solved by finite element method to calculate the displacement field and internal stress field of the soft tissue region under static equilibrium state.

[0018] Based on the displacement field, update the skin mesh vertex coordinates in the standard model data to generate the deformed mesh.

[0019] In some embodiments, the step of designing a lattice filling structure for the standard model data based on the optimized data to generate an internal support structure with a gradient density distribution, thereby obtaining the target model data, includes:

[0020] The target density field of the lattice structure is determined based on the preset mapping function between the mechanical distribution and lattice density characterized by the optimized data.

[0021] Based on the target density field, the internal space of the model corresponding to the standard model data is filled with lattice, wherein the cell type, lattice size or lattice rod diameter is continuously gradient-changed according to the target density value of the spatial location to form the internal support structure.

[0022] The internal support structure is subjected to manufacturability verification and geometric optimization to obtain an optimized internal structure.

[0023] Based on the internal optimization structure, the standard model data is updated to obtain the target model data.

[0024] In some embodiments, the process of performing manufacturability verification and geometric optimization on the internal support structure to obtain an optimized internal structure includes:

[0025] Verify whether the minimum diameter of the lattice rods of the internal support structure is greater than the minimum printable feature size of the multi-material 3D printing equipment, and optimize the lattice orientation so that the angle between the lattice rods and the printing substrate meets the self-supporting printing requirements.

[0026] In some embodiments, the multi-material 3D printing slicing process of the target model data to plan printing strategies for different structural parts in the target model data includes:

[0027] The target model data is parsed to extract multiple body part models, and printing materials are assigned to each body part model.

[0028] Based on the material properties of the printing material, a set of process parameters is planned for each body part model, including layer thickness, printing temperature, printing path, and multi-material switching sequence.

[0029] Based on the set of process parameters, multiple body part models are sliced ​​and layered to generate a printing strategy that drives the multi-material 3D printing equipment.

[0030] In some embodiments, the printing path includes a printing path planned using a fill path algorithm adapted to the orientation of the lattice rods, and a printing path that plans a soluble support material for printing on suspended parts inside the lattice that cannot be self-supported.

[0031] Secondly, this application provides a stereoscopic model making system based on additive printing, comprising:

[0032] The acquisition module is used to acquire standard model data and production requirement data;

[0033] The optimization module is used to perform biomechanical simulation optimization on the standard model data based on the production requirement data, and generate optimized data on the soft tissue deformation law of the human body in the target posture.

[0034] The design module is used to perform lattice filling design on the standard model data based on the optimized data, generate an internal support structure with gradient density distribution, and obtain the target model data.

[0035] The slicing module is used to perform multi-material 3D printing slicing processing on the target model data in order to plan the printing strategy corresponding to different body parts in the target model data;

[0036] The printing module is used to drive a multi-material 3D printing device to print multiple body part models corresponding to the target model data based on the printing strategy. The multiple body part models are used to assemble a three-dimensional model.

[0037] Thirdly, this application provides a computer device including a processor and a memory, wherein the memory is used to store a computer program, and the computer program, when executed by the processor, implements the additive printing-based stereoscopic model production method as described in the first aspect above.

[0038] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the additive printing-based stereoscopic model manufacturing method described in the first aspect above.

[0039] Compared with the prior art, this application has the following beneficial effects:

[0040] By acquiring standard model data and manufacturing requirement data, customized needs can be met, eliminating reliance on physical molds. Based on the manufacturing requirement data, biomechanical simulation optimization is performed on the standard model data to generate optimized data on the soft tissue deformation law of the human body in the target posture. This simulates the soft tissue deformation and mechanical distribution of the standard human body model under different postures and other requirements, ensuring the mechanical stability of the model's posture and solving the problems of mechanical distortion and easy deformation of traditional models. Based on the optimized data, lattice filling design is performed on the standard model data to generate an internal support structure with gradient density distribution, obtaining the target model data. This significantly reduces material usage while ensuring the strength of key areas. The overall weight is reduced to achieve lightweighting and structural stability of the model. Multi-material 3D printing slicing is performed on the target model data to plan printing strategies for different body parts. Appropriate hard or flexible materials and printing parameters are allocated to areas with different mechanical properties to ensure the final product possesses both structural strength and local flexibility. Based on the printing strategy, a multi-material 3D printing device is driven to print multiple body part models corresponding to the target model data, achieving solid forming based on digital blueprints. This completely eliminates parting lines and flash inherent in traditional processes, saving subsequent manual trimming. Furthermore, modular production facilitates local adjustments, repairs, and posture reconfiguration. Attached Figure Description

[0041] Figure 1 This is a schematic flowchart illustrating a method for creating a stereoscopic model based on additive printing, as shown in an embodiment of this application.

[0042] Figure 2 This is a schematic diagram of a conventionally blow-molded model product shown in an embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the upper torso model product printed by the method of this application, as shown in an embodiment of this application;

[0044] Figure 4 As shown in the embodiments of this application Figure 3 Another schematic diagram from an outer perspective;

[0045] Figure 5 As shown in the embodiments of this application Figure 3 An internal perspective diagram;

[0046] Figure 6 This is a structural block diagram of an additive printing-based stereoscopic model fabrication system shown in an embodiment of this application;

[0047] Figure 7 This is a structural block diagram of a computer device shown in an embodiment of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0049] As described in the background section, traditional blow molding methods often leave burrs or residual parting lines at the parting line and other areas (e.g., Figure 2 As shown in the image, subsequent manual cleaning and repair are required.

[0050] Therefore, this application provides a method for creating a three-dimensional model based on additive printing. The method optimizes the target model through mechanical simulation, slices the model, and prints out body part models. Compared to... Figure 2 ,like Figure 3 and Figure 4 As shown, the product surface produced by this method has no burrs or parting lines remaining; Figure 5 As shown, the product produced by this method has a lattice (internal support grid), and the printing thickness varies in different areas, thus balancing structural stability and lightweight design.

[0051] Please refer to Figure 1 , Figure 1This application provides a flowchart illustrating a method for creating a stereoscopic model based on additive printing. The method includes steps S101 to S105, detailed below:

[0052] Step S101: Obtain standard model data and production requirement data.

[0053] In this step, the standard model data is a baseline 3D digital human body model without pose bias. This can include male and female standard model data, and each standard model can consist of a head module, upper torso module, lower torso module, left hand module, and right hand module. The production requirement data is user-customized data, including but not limited to pose requirements, body shape requirements (including height, weight, and other dimensions), material cost requirements, and application scenario requirements. Application scenario requirements include, for example, a sports equipment testing scenario, which emphasizes the simulation accuracy of muscle groups; or a shop window display scenario, which focuses on the golden ratio of the model's body and lightweight design. Optionally, the standard model data can be retrieved from a pre-built body shape library, and the production requirement data is input by the user and then converted into computer language.

[0054] Step S102: Based on the production requirement data, perform biomechanical simulation optimization on the standard model data to generate optimized data on the soft tissue deformation law of the human body in the target posture.

[0055] In this step, biomechanical simulation optimization can be performed using software such as AnyBody Modeling System, OpenSim, or LifeMod. Soft tissue is defined as an elastic material (such as Ogden or Mooney-Rivlin models), and the deformation law of soft tissue in the standard model under the target posture is simulated. This allows for the analysis of the deformation law and internal stress distribution of each module of the standard model under compression and tension under the target posture. This facilitates the adjustment of structural characteristics such as model thickness and model lattice according to the stress of each module, thereby meeting the structural stability requirements.

[0056] In some embodiments, step S102 includes:

[0057] Call the skeletal muscle multibody dynamics model corresponding to the standard model data;

[0058] Based on the target posture parameters in the production requirement data, the skeletal muscle multibody dynamics model is driven, and the activation state of each muscle group in the skeletal muscle multibody dynamics model under the target posture, as well as the distribution of mechanical loads acting on the bone and skin boundaries, are calculated to obtain muscle activation and joint force data.

[0059] Using the muscle activation and joint force data as boundary conditions, finite element mechanical simulation is performed on the soft tissue region of the standard model data to obtain the deformation mesh of the soft tissue region under the target posture.

[0060] The vertex displacement field of the deformed mesh is correlated with the internal stress field, and a three-dimensional density distribution field is generated as the optimization data.

[0061] In this embodiment, since subsequent lattice design software (such as nTopology) can directly read such field data and drive the generation of gradient-changing lattice structures, high-stress (high-density value) regions will generate dense lattices to simulate high hardness, while low-stress regions will correspond to sparse lattices to simulate softness. Therefore, the biomechanical simulation results are directly converted into manufacturable engineering instructions.

[0062] Optionally, in biomechanical software such as AnyBody or OpenSim, a multibody dynamics model matching the standard human body model, including skeletal, joint, and muscle paths, is invoked. The target joint angles from the required data are input, and the model is driven into a specified posture. Inverse dynamics analysis is used to calculate the activation levels of each muscle required to maintain this posture, and the joint contact forces and the load distribution of muscles on bones and subcutaneous tissues are solved to obtain the intrinsic mechanical boundary conditions of the human body model. The calculated loads (such as muscle attachment point forces and joint reactions) are used as forced boundary conditions and applied to the soft tissue geometry of the standard model in finite element software (such as Abaqus). In the finite element model, tissue materials such as fat and muscle are defined as hyperelastic models to simulate their large deformation and nonlinear biomechanical characteristics. The accurate displacement and stress field of the soft tissue under actual internal loads are obtained by solving, generating a high-fidelity deformation mesh. The vertex displacement field and internal stress field of the deformed mesh output by the finite element method are correlated and parameterized. That is, the calculated stress value at each location is transformed into a relative density value between 0 and 1 through a mapping function, thereby generating a three-dimensional density distribution field corresponding to the model space. This three-dimensional density distribution field is used as optimization data.

[0063] In some embodiments, the above-described finite element mechanical simulation process includes:

[0064] The muscle activation and joint force data are converted into boundary loads and displacement constraints applied to the soft tissue region.

[0065] Based on the boundary load and displacement constraints, the soft tissue region is solved by finite element method to calculate the displacement field and internal stress field of the soft tissue region under static equilibrium state.

[0066] Based on the displacement field, update the skin mesh vertex coordinates in the standard model data to generate the deformed mesh.

[0067] In this embodiment, the abstract muscle and joint mechanical data are accurately transformed into visual, quantifiable, and usable engineering design parameters through finite element simulation. These parameters serve as direct inputs for subsequent gradient density lattice design, ensuring that the density variations of the internal support structure of the mannequin correspond to the actual mechanical state of the human body in that posture.

[0068] Optionally, muscle activation data is converted into concentrated forces acting on muscle attachment points, joint force data is converted into pressure distribution on the bone contact surface, and the final bone displacement is set as a rigid displacement constraint, thereby accurately reproducing the internal mechanical environment of the human body in the finite element model. In software such as Abaqus, soft tissue materials are defined as hyperelastic constitutive structures such as the Oden model and given nonlinear mechanical properties. Then, the aforementioned boundary conditions are applied to perform static mechanical solutions, calculating the displacement and internal stress distribution of the soft tissue in equilibrium. The obtained displacement field data is then updated in batches to the coordinates of each vertex of the standard model via a script, thereby generating a high-fidelity deformable mesh that matches the mechanical state of the target posture.

[0069] Step S103: Based on the optimized data, perform lattice filling design on the standard model data to generate an internal support structure with gradient density distribution, thereby obtaining the target model data.

[0070] In this step, by making the lattice density, rod diameter and cell type inside the model change continuously with the mechanical data, significant weight reduction and material saving are achieved while ensuring the overall structural stability, thus resolving the contradiction between the traditional solid and bulky or hollow unstable and mechanical distortion of the model.

[0071] In some embodiments, step S103 includes:

[0072] The target density field of the lattice structure is determined based on the preset mapping function between the mechanical distribution and lattice density characterized by the optimized data.

[0073] Based on the target density field, the internal space of the model corresponding to the standard model data is filled with lattice, wherein the cell type, lattice size or lattice rod diameter is continuously gradient-changed according to the target density value of the spatial location to form the internal support structure.

[0074] The internal support structure is subjected to manufacturability verification and geometric optimization to obtain an optimized internal structure.

[0075] Based on the internal optimization structure, the standard model data is updated to obtain the target model data.

[0076] In this embodiment, the lattice is a three-dimensional mesh inside the model, and the cell type is the material type printed from the three-dimensional mesh. Since the optimization data is three-dimensional density distribution field data obtained through model mechanical simulation to characterize the mechanical state of the model structure, it only characterizes the mechanical performance requirements of the model. Therefore, it is necessary to convert these mechanical performance requirements into structural parameters that can be used for manufacturing.

[0077] Optionally, the optimized data (i.e., the three-dimensional density distribution field) is imported into software such as nTopology, and converted into a target density field using a preset mapping function (e.g., linear mapping: lattice relative density ρ = k × σ_normalized + b, where σ_normalized represents the standardized three-dimensional density distribution field, k is the scaling factor of the mapping function, and b is the intercept of the mapping function). This transforms the three-dimensional density distribution field characterizing structural hardness into a target density field characterizing structural density. The software adjusts the cell type (higher density indicates better material performance), lattice size (higher density means smaller lattice units), or rod diameter (higher density means thicker rods) in real time based on the density value at each spatial point in the target density field. The software verifies whether the minimum diameter of the lattice rods in the internal support structure is greater than the minimum printable feature size of the multi-material 3D printing equipment (e.g., 0.5 mm), and optimizes the lattice orientation so that the angle between the lattice rods and the printing substrate meets the self-supporting printing requirements (e.g., the angle between the rods and the printing platform is greater than 45°). The optimized internal lattice structure and the surface shell model of the standard model are combined using a Boolean union operation to form the target model data (such as .STL or .3MF files).

[0078] Step S104: Perform multi-material 3D printing slicing processing on the target model data to plan the printing strategy corresponding to different body parts in the target model data.

[0079] In this step, each body part is printed separately, so a printing strategy for each body part is planned, which includes printing materials, printing paths, slice layer thickness, and printing sequence.

[0080] In some embodiments, step S104 includes:

[0081] The target model data is parsed to extract multiple body part models, and printing materials are assigned to each body part model.

[0082] Based on the material properties of the printing material, a set of process parameters is planned for each body part model, including layer thickness, printing temperature, printing path, and multi-material switching sequence.

[0083] Based on the set of process parameters, multiple body part models are sliced ​​and layered to generate a printing strategy that drives the multi-material 3D printing equipment.

[0084] In this embodiment, the target model data is imported into 3D printing software. The software automatically parses independent body part models by recognizing metadata tags (such as part IDs) in the model data or based on geometric features (such as closed boundaries of lattice regions of different densities). Printing material is allocated to each body part model according to a preset mapping relationship between the printing material and the body part model. A refined set of process parameters is planned based on the characteristics of each material. For example, for rigid material A, a higher printing temperature is used to ensure interlayer fusion, and a faster printing speed is employed; for flexible material B, the temperature and speed are reduced to prevent stringing, and a specific filling path (such as a concentric circle path) is used to enhance its elasticity. The planning of the multi-material switching sequence includes calculating and inserting material switching instructions and nozzle cleaning cycles at the slicing layer at the interface of the two materials, and planning a transition thin layer printed by mixing the two materials in a certain proportion to enhance the interface bonding. Based on the above-described customized material and parameter sets for each part, the slicing engine slices the overall model layer by layer, generating a composite G-code file containing multi-layer, multi-material instructions, which is the final printing strategy. This document contains precise timing logic for motion trajectories, all material switching, temperature adjustments, and speed changes, to drive a multi-material 3D printer to continuously and collaboratively use different materials to construct complex biomimetic parts in a single printing job.

[0085] In some embodiments, the printing path includes a printing path planned using a filling path algorithm adapted to the orientation of the lattice rods, and a printing path for printing soluble support material for suspended parts inside the lattice that cannot be self-supported.

[0086] In this embodiment, the slicing software can employ an axial filling algorithm for the lattice structure, making the movement trajectory of the print head as parallel as possible to the spatial orientation of the lattice rods. This allows for the layer-by-layer deposition of material along the axial direction of the rods, maximizing the continuity of the printed layers and the fiber orientation strength, and significantly improving the bending resistance of the lattice rods.

[0087] Meanwhile, since there are a large number of interconnected suspended structures inside complex crystal lattices, traditional supports are difficult to remove and will permanently damage the integrity of the crystal lattice. Therefore, this embodiment identifies suspended rods in the crystal lattice with an angle to the horizontal plane that is less than a critical value (usually 45°). Water-soluble support materials (such as PVA) are planned for these locations and printed simultaneously. After printing, the water-soluble support material is completely dissolved and removed by soaking in warm water, perfectly preserving the crystal lattice morphology and internal channels. This is a key guarantee for achieving high fidelity and manufacturability of complex biomimetic crystal lattice structures.

[0088] Step S105: Based on the printing strategy, drive the multi-material 3D printing device to print multiple body part models corresponding to the target model data, and use the multiple body part models to assemble into a three-dimensional model finished product.

[0089] In this embodiment, a multi-material 3D printing device (such as a multi-head FDM or PolyJet printer) receives and parses the composite G-code file generated from the slices. Based on the material switching instructions and timing logic embedded in the file, the device automatically switches between multiple printheads or a single-head multi-material channel, printing layer by layer collaboratively.

[0090] To implement the additive printing-based 3D model fabrication method corresponding to the above method embodiments, and to achieve the corresponding functions and technical effects, see [link to documentation]. Figure 6 , Figure 6 This diagram illustrates a structural block diagram of a stereoscopic model fabrication system based on additive printing, according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The stereoscopic model fabrication system based on additive printing provided in this embodiment includes:

[0091] Module 601 is used to acquire standard model data and production requirement data;

[0092] The optimization module 602 is used to perform biomechanical simulation optimization on the standard model data based on the production requirement data, and generate optimized data on the soft tissue deformation law of the human body in the target posture.

[0093] Design module 603 is used to perform lattice filling design on the standard model data based on the optimized data, generate an internal support structure with gradient density distribution, and obtain target model data;

[0094] The slicing module 604 is used to perform multi-material 3D printing slicing processing on the target model data in order to plan the printing strategy corresponding to different body parts in the target model data.

[0095] The printing module 605 is used to drive a multi-material 3D printing device to print multiple body part models corresponding to the target model data based on the printing strategy. The multiple body part models are used to assemble a three-dimensional model product.

[0096] In some embodiments, the optimization module 602 includes:

[0097] The calling unit is used to call the skeletal muscle multibody dynamics model corresponding to the standard model data;

[0098] The driving unit is used to drive the skeletal muscle multibody dynamics model based on the target posture parameters in the production requirement data, and to calculate the activation state of each muscle group of the skeletal muscle multibody dynamics model in the target posture, as well as the distribution of mechanical loads acting on the bone and skin boundaries, to obtain muscle activation and joint force data.

[0099] The simulation unit is used to perform finite element mechanical simulation on the soft tissue region of the standard model data using the muscle activation and joint force data as boundary conditions, and to obtain the deformation mesh of the soft tissue region under the target posture.

[0100] The mapping unit is used to associate the vertex displacement field of the deformed mesh with the internal stress field, and map to generate a three-dimensional density distribution field as the optimization data.

[0101] In some embodiments, the simulation unit is specifically used for:

[0102] The muscle activation and joint force data are converted into boundary loads and displacement constraints applied to the soft tissue region.

[0103] Based on the boundary load and displacement constraints, the soft tissue region is solved by finite element method to calculate the displacement field and internal stress field of the soft tissue region under static equilibrium state.

[0104] Based on the displacement field, update the skin mesh vertex coordinates in the standard model data to generate the deformed mesh.

[0105] In some embodiments, the design module 603 includes:

[0106] The determining unit is used to determine the target density field of the lattice structure based on a preset mapping function between the mechanical distribution and the lattice density characterized by the optimized data.

[0107] A filling unit is used to fill the internal space of the model corresponding to the standard model data with a lattice based on the target density field, wherein the cell type, lattice size or lattice rod diameter is continuously gradient-changed according to the target density value at the spatial location to form the internal support structure.

[0108] An optimization unit is used to perform manufacturability verification and geometric optimization on the internal support structure to obtain an optimized internal structure.

[0109] The update unit is used to update the standard model data based on the internal optimization structure to obtain the target model data.

[0110] In some embodiments, the optimization unit is specifically used for:

[0111] Verify whether the minimum diameter of the lattice rods of the internal support structure is greater than the minimum printable feature size of the multi-material 3D printing equipment, and optimize the lattice orientation so that the angle between the lattice rods and the printing substrate meets the self-supporting printing requirements.

[0112] In some embodiments, the slicing module 604 includes:

[0113] The allocation unit is used to parse multiple body part models in the target model data and allocate printing material for each body part model;

[0114] The planning unit is used to plan a set of process parameters for each body part model, including layer thickness, printing temperature, printing path, and multi-material switching sequence, based on the material properties of the printing material.

[0115] Based on the set of process parameters, the slicing unit slices and layers multiple body part models to generate a printing strategy that drives the multi-material 3D printing equipment.

[0116] In some embodiments, the printing path includes a printing path planned using a filling path algorithm adapted to the orientation of the lattice rods, and a printing path for printing soluble support material for suspended parts inside the lattice that cannot be self-supported.

[0117] The above-described additive printing-based stereoscopic model making system can implement the additive printing-based stereoscopic model making method of the above-described method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining contents of this application embodiment can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment.

[0118] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Figure 7 As shown, the computer device 7 of this embodiment includes: at least one processor 70 ( Figure 7 (Only one is shown in the diagram), memory 71, and computer program 72 stored in said memory 71 and executable on said at least one processor 70, wherein the processor 70 executes said computer program 72 to implement the steps in any of the above method embodiments.

[0119] The computer device 7 can be a tablet computer, desktop computer, cloud server, or other computing device. This computer device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7The computer device 7 is merely an example and does not constitute a limitation on the computer device 7. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0120] The processor 70 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0121] In some embodiments, the memory 71 may be an internal storage unit of the computer device 7, such as a hard disk or memory of the computer device 7. In other embodiments, the memory 71 may be an external storage device of the computer device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 7. Furthermore, the memory 71 may include both internal and external storage units of the computer device 7. The memory 71 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0122] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in any of the above method embodiments.

[0123] This application provides a computer program product that, when run on a computer device, enables the computer device to execute the steps described in the various method embodiments above.

[0124] In the several embodiments provided in this application, it will be understood that each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the figures. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.

[0125] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for manufacturing a three-dimensional model based on additive printing, characterized in that, include: Obtain standard model data and production requirement data; Based on the aforementioned production requirements data, the standard model data is optimized through biomechanical simulation to generate optimized data on the soft tissue deformation law of the human body under the target posture. Based on the optimized data, lattice filling design is performed on the standard model data to generate an internal support structure with gradient density distribution, thereby obtaining the target model data; The target model data is sliced ​​using multi-material 3D printing to plan printing strategies for different body parts in the target model data. Based on the printing strategy, a multi-material 3D printing device is driven to print multiple body part models corresponding to the target model data. The multiple body part models are used to assemble a three-dimensional model product. The step of performing biomechanical simulation optimization on the standard model data based on the manufacturing requirements data to generate optimized data on the soft tissue deformation law of the human body under the target posture includes: Call the skeletal muscle multibody dynamics model corresponding to the standard model data; Based on the target posture parameters in the production requirement data, the skeletal muscle multibody dynamics model is driven, and the activation state of each muscle group in the skeletal muscle multibody dynamics model under the target posture, as well as the distribution of mechanical loads acting on the bone and skin boundaries, are calculated to obtain muscle activation and joint force data. Using the muscle activation and joint force data as boundary conditions, finite element mechanical simulation is performed on the soft tissue region of the standard model data to obtain the deformation mesh of the soft tissue region under the target posture. The vertex displacement field of the deformed mesh is correlated with the internal stress field, and a three-dimensional density distribution field is generated as the optimization data. The step of using the muscle activation and joint force data as boundary conditions to perform finite element mechanical simulation on the soft tissue region of the standard model data to obtain the deformation mesh of the soft tissue region under the target posture includes: The muscle activation and joint force data are converted into boundary loads and displacement constraints applied to the soft tissue region. Based on the boundary load and displacement constraints, the soft tissue region is solved by finite element method to calculate the displacement field and internal stress field of the soft tissue region under static equilibrium state. Based on the displacement field, update the skin mesh vertex coordinates in the standard model data to generate the deformed mesh; The step of designing a lattice filling structure for the standard model data based on the optimized data to generate an internal support structure with a gradient density distribution, thereby obtaining the target model data, includes: The target density field of the lattice structure is determined based on the preset mapping function between the mechanical distribution and lattice density characterized by the optimized data. Based on the target density field, the internal space of the model corresponding to the standard model data is filled with lattice, wherein the cell type, lattice size or lattice rod diameter is continuously gradient-changed according to the target density value of the spatial location to form the internal support structure. The internal support structure is subjected to manufacturability verification and geometric optimization to obtain an optimized internal structure. Based on the internal optimization structure, the standard model data is updated to obtain the target model data.

2. The method for creating a three-dimensional model based on additive printing as described in claim 1, characterized in that, The process of performing manufacturability verification and geometric optimization on the internal support structure to obtain an optimized internal structure includes: Verify whether the minimum diameter of the lattice rods of the internal support structure is greater than the minimum printable feature size of the multi-material 3D printing equipment, and optimize the lattice orientation so that the angle between the lattice rods and the printing substrate meets the self-supporting printing requirements.

3. The method for creating a three-dimensional model based on additive printing as described in claim 1, characterized in that, The step of performing multi-material 3D printing slicing processing on the target model data to plan printing strategies for different structural parts in the target model data includes: The target model data is parsed to extract multiple body part models, and printing materials are assigned to each body part model. Based on the material properties of the printing material, a set of process parameters is planned for each body part model, including layer thickness, printing temperature, printing path, and multi-material switching sequence. Based on the set of process parameters, multiple body part models are sliced ​​and layered to generate a printing strategy that drives a multi-material 3D printing device.

4. The method for creating a three-dimensional model based on additive printing as described in claim 3, characterized in that, The printing path includes a printing path planned using a filling path algorithm adapted to the orientation of the lattice rods, and a printing path for printing soluble support material for suspended parts inside the lattice that cannot be self-supported.

5. A three-dimensional model making system based on additive printing, characterized in that, include: The acquisition module is used to acquire standard model data and production requirement data; The optimization module is used to perform biomechanical simulation optimization on the standard model data based on the production requirement data, and generate optimized data on the soft tissue deformation law of the human body in the target posture. The design module is used to perform lattice filling design on the standard model data based on the optimized data, generate an internal support structure with gradient density distribution, and obtain the target model data. The slicing module is used to perform multi-material 3D printing slicing processing on the target model data in order to plan the printing strategy corresponding to different body parts in the target model data; The printing module is used to drive a multi-material 3D printing device to print multiple body part models corresponding to the target model data based on the printing strategy. The multiple body part models are used to assemble a three-dimensional model product. The optimization module includes: The calling unit is used to call the skeletal muscle multibody dynamics model corresponding to the standard model data; The driving unit is used to drive the skeletal muscle multibody dynamics model based on the target posture parameters in the production requirement data, and to calculate the activation state of each muscle group of the skeletal muscle multibody dynamics model in the target posture, as well as the distribution of mechanical loads acting on the bone and skin boundaries, to obtain muscle activation and joint force data. The simulation unit is used to perform finite element mechanical simulation on the soft tissue region of the standard model data using the muscle activation and joint force data as boundary conditions, and to obtain the deformation mesh of the soft tissue region under the target posture. A mapping unit is used to associate the vertex displacement field of the deformed mesh with the internal stress field and map it to generate a three-dimensional density distribution field as the optimization data; Specifically, the simulation unit is used for: The muscle activation and joint force data are converted into boundary loads and displacement constraints applied to the soft tissue region. Based on the boundary load and displacement constraints, the soft tissue region is solved by finite element method to calculate the displacement field and internal stress field of the soft tissue region under static equilibrium state. Based on the displacement field, update the skin mesh vertex coordinates in the standard model data to generate the deformed mesh; The design module includes: The determining unit is used to determine the target density field of the lattice structure based on a preset mapping function between the mechanical distribution and the lattice density characterized by the optimized data. A filling unit is used to fill the internal space of the model corresponding to the standard model data with a lattice based on the target density field, wherein the cell type, lattice size or lattice rod diameter is continuously gradient-changed according to the target density value at the spatial location to form the internal support structure. An optimization unit is used to perform manufacturability verification and geometric optimization on the internal support structure to obtain an optimized internal structure. The update unit is used to update the standard model data based on the internal optimization structure to obtain the target model data.

6. A computer device, characterized in that, It includes a processor and a memory, the memory being used to store a computer program, which, when executed by the processor, implements the additive printing-based method for creating stereoscopic models as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the additive printing-based method for creating stereoscopic models as described in any one of claims 1 to 4.

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