Support reinforcement method and system of 3D printing model, computer device, medium and computer program product

Abstract

The application discloses a support reinforcement method and system of a 3D printing model, a computer device, a medium and a computer program product. The support reinforcement method of the 3D printing model comprises the following steps: obtaining a to-be-reinforced model, wherein the to-be-reinforced model comprises a 3D printing model of a target part and a to-be-reinforced support of the 3D printing model; the to-be-reinforced support is determined based on a support structure constructed by voxels of the 3D printing model; a region corresponding to the support structure is offset based on a bias parameter to obtain an offset region intersecting with the 3D printing model; support points located in the offset region are determined on a surface of the 3D printing model, and each determined support point is connected to the to-be-reinforced support to obtain a reinforced model.

Description

Technical Field

[0001] This application relates to the field of 3D printing technology, specifically to a method and system for supporting and reinforcing a 3D printed model, computer equipment, media, and computer program products. Background Technology

[0002] In the 3D printing process, to prevent parts from collapsing or deforming, support structures are typically generated during the pre-processing stage below the overhanging areas of the 3D printed model corresponding to the part, or at other locations where support areas need to be created. However, the reliability of the generated support structures is difficult to guarantee, which can lead to a decrease in the load-bearing capacity of the support structures, or even cause parts to fall off during printing, affecting the stability of the printing process and the forming quality of the parts.

[0003] Therefore, improving the reliability of the support structure to avoid support instability and forming defects is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0004] In view of the shortcomings of the above-mentioned related technologies, the purpose of this application is to provide a method and system for supporting and reinforcing 3D printed models, computer equipment, media and computer program products, so as to overcome the technical problems of how to improve the reliability of the support structure to avoid support instability and forming defects in the above-mentioned related technologies.

[0005] To achieve the above and other related objectives, the first aspect of this application discloses a method for supporting and reinforcing a 3D printed model, comprising the following steps: obtaining a model to be reinforced, wherein the model to be reinforced includes a 3D printed model of a target part and a support to be reinforced; the support to be reinforced is determined based on a support structure constructed from voxels of the 3D printed model; biasing the region corresponding to the support structure based on an offset parameter to obtain a biased region intersecting with the 3D printed model; determining support points located within the biased region on the surface of the 3D printed model, and connecting each determined support point to the support to be reinforced to obtain a reinforced model.

[0006] A second aspect of this application discloses a support and reinforcement system for a 3D printed model, comprising: an acquisition module for acquiring a model to be reinforced, wherein the model to be reinforced includes a 3D printed model of a target part and a support to be reinforced; the support to be reinforced is determined based on a support structure constructed from voxels of the 3D printed model; an offset module for offsetting a region corresponding to the support structure based on offset parameters to obtain an offset region intersecting with the 3D printed model; and a reinforcement module for determining support points located within the offset region on the surface of the 3D printed model and connecting each determined support point to the support to be reinforced to obtain a reinforced model.

[0007] A third aspect of this application discloses a computer device, comprising: a storage device for storing at least one program; and a processing device connected to the storage device for calling and executing the at least one program from the storage device to implement the support and reinforcement method for a 3D printed model as described in the first aspect of this application.

[0008] The fourth aspect of this application discloses a computer-readable storage medium storing at least one program, which, when called and executed by a computer processor, implements the support and reinforcement method for a 3D printed model as described in the first aspect of this application.

[0009] The fifth aspect of this application discloses a computer program product that, when run on a computer, causes the computer to perform the support and reinforcement method for a 3D printed model as described in the first aspect of this application.

[0010] In summary, this application discloses a method and system for supporting and reinforcing a 3D printed model, as well as a computer device, medium, and computer program product. The method involves biasing the region corresponding to the support structure constructed from voxels, allowing the biased region to spatially enclose the potential contact zone of the support structure. Subsequently, support points located within the biased region are selected on the surface of the 3D printed model, and each support point is connected to the support to be reinforced. Thus, the connection position between the support structure and the model is determined jointly by geometric bias and support point selection. This allows for a more closely fitting and stable connection between the support structure and the 3D printed model, improving the reliability of the support structure, effectively enhancing the load-bearing capacity of the support, reducing the risk of connection point misalignment or detachment, and significantly reducing collapse, deformation, or forming defects caused by support failure during printing. This improves the stability of the printing process and the forming quality of the printed parts. Attached Figure Description

[0011] The specific features of the invention involved in this application are shown in the appended claims. The features and advantages of the invention can be better understood by referring to the exemplary embodiments and drawings described in detail below. A brief description of the drawings is as follows:

[0012] Figure 1 The diagram shown is a schematic representation of the build volume in computer three-dimensional space in one embodiment of this application.

[0013] Figure 2 The diagram shown is a schematic representation of a 3D printed model and its support structure constructed from voxels in one embodiment of this application.

[0014] Figure 3 The diagram shows a flow chart of a support and reinforcement method for a 3D printed model according to one embodiment of this application.

[0015] Figure 4 The diagram shown is a flowchart of step S100 in one embodiment of this application.

[0016] Figure 5 The diagram shown is a schematic representation of a 3D printed model obtained in one embodiment of this application.

[0017] Figure 6 As shown in one embodiment of this application, the thermal distribution is superimposed and displayed. Figure 5 A schematic diagram of the 3D printed model shown.

[0018] Figure 7 The diagram shown is an interactive interface diagram of the first modified input window in one embodiment of this application.

[0019] Figure 8 The diagram shown is an interactive interface diagram of the second modified input window in one embodiment of this application.

[0020] Figure 9 The diagram shows a process flow of topology optimization to generate a support structure constructed from voxels in one embodiment of this application.

[0021] Figure 10 The diagram shown is a schematic representation of the bounding box and the corresponding support generation area in one embodiment of this application.

[0022] Figure 11 The diagram shown is a schematic representation of a 3D printed model and a spatial domain model in one embodiment of this application.

[0023] Figure 12 The diagram shown is an interactive interface diagram including a task progress window in one embodiment of this application.

[0024] Figure 13 This application is displayed. Figure 5The diagram shows the 3D printed model and the corresponding generated support structure constructed from voxels.

[0025] Figure 14 The diagram shown is a schematic representation of a 3D printed model obtained in another embodiment of this application.

[0026] Figure 15 This application is displayed. Figure 14 The diagram shows the 3D printed model and the corresponding generated support structure constructed from voxels.

[0027] Figure 16 The diagram shown is a suspended 3D printed model and the corresponding generated support structure constructed from voxels in one embodiment of this application.

[0028] Figure 17 The diagram shows a suspended 3D printed model and the corresponding generated support structure constructed from voxels, as shown in another embodiment of this application.

[0029] Figure 18 This application is displayed as being in Figure 15 The diagram shown is a schematic of the area corresponding to the support structure constructed from voxels after basic units have been filled in.

[0030] Figure 19 This application is shown as an embodiment of the present application. Figure 15 A schematic diagram of the area corresponding to the supporting structure in the diagram after being offset outwards.

[0031] Figure 20 This application is shown as an embodiment of the present application. Figure 2 A schematic diagram of the area corresponding to the supporting structure in the diagram after being offset outwards.

[0032] Figure 21 The diagram shows a flowchart illustrating the steps of determining a support point located in the offset region in one embodiment of this application.

[0033] Figure 22 This application is shown as a counterpart to... Figure 18 A schematic diagram of the support to be reinforced after reinforcement.

[0034] Figure 23 This application is shown as a counterpart to... Figure 22 The diagram shown illustrates the 3D printed model after the auxiliary support has been added.

[0035] Figure 24 The diagram shown is a block diagram of a support and reinforcement system for a 3D printed model according to one embodiment of this application.

[0036] Figure 25 The diagram shown is a structural schematic of a computer device according to one embodiment of this application. Detailed Implementation

[0037] The following explanations of some terms and nouns used in the various embodiments of this application are also part of the invention content. Those skilled in the art will understand that, unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0038] The 3D printing process described in this application refers to a manufacturing process that constructs 3D objects by scanning printing material and printing layer by layer. This manufacturing process can be implemented using a 3D printer to produce printed parts required in the real world (i.e., actual physical space) using various methods with the material to be printed (e.g., photopolymerizable resin materials, or sinterable metal powders, or sinterable copolymers or polymer mixtures, or fused deposition plastic filaments, etc.). The printed parts can be any object such as aerospace parts, automotive parts, industrial equipment parts, handicrafts, medical devices, and household goods. In subsequent embodiments, the printed parts may also be referred to as target parts.

[0039] The model to be reinforced or the 3D printed model corresponding to the target part described in the embodiments of this application is a three-dimensional virtual model. The build volume described in the embodiments of this application is a space corresponding to the actual printing space of the 3D printer. The 3D printed model or the model to be reinforced placed in the build volume can be manufactured within the printing space. The build volume is located in a computer three-dimensional space, which has a coordinate system. In the embodiments, a corner of the build volume can be used as the origin to form the coordinate system of the computer three-dimensional space. Please refer to [link to relevant documentation]. Figure 1 The diagram shows a construction volume V in a computer three-dimensional space according to one embodiment of this application. A construction volume V is formed in the computer three-dimensional space R. Figure 1The lower left corner of the construction volume V is taken as the origin O, thus forming a coordinate system XYZ in the computer's three-dimensional space. The surface of the construction volume V on the XOY plane corresponds to the printing surface of the actual printing platform. In the embodiments of this application, the surface of the construction volume V on the XOY plane is also referred to as the construction plane G. In addition, the direction perpendicular to the construction plane in the construction volume V or the direction perpendicular to the printing surface in the printing space is called the vertical direction (also known as the Z-axis direction), and the direction perpendicular to the vertical direction is called the horizontal direction (also known as the horizontal direction). The side of the model in the construction volume closer to the construction plane is called the lower side or bottom, and the side farther away from the construction surface is called the upper side or top.

[0040] The 3D printing process simulation described in the embodiments of this application is a mechanical simulation or a thermo-mechanical coupling simulation. Thermo-mechanical coupling simulation can simultaneously consider the coupling relationship between the temperature field and the stress field, thus obtaining the deformation field, temperature field, and stress field of the model under thermal load after simulating the 3D printing process of the 3D printed model or the reinforced model. The mechanical simulation does not consider the temperature field and can quickly solve the stress field and deformation field of the model, for example, by directly using the inherent strain values. The obtained deformation field includes the deformation results (deformation magnitude and deformation direction) at various locations of the model. In subsequent embodiments, the deformation magnitude is also referred to as the deformation amount.

[0041] As described in the background section, improving the reliability of support structures to avoid support instability and forming defects is a pressing technical problem. Specifically, the generated support structures often struggle to adhere to the surface of the 3D printed model, meaning a tight connection between the support structure and the 3D printed model is difficult to achieve. This is especially true for support structures of 3D printed models automatically generated during topology optimization. Since topology optimization is based on voxel-based support structure construction, regardless of the format used to represent the voxel-constructed support structure or whether basic units are filled within the corresponding region, the edges of the voxel-constructed support structure or the resulting lattice-like support structure are difficult to fit tightly with the 3D printed model. This is particularly true for 3D printed models with smooth contours, such as those described in [reference needed]. Figure 2 The image shown is a schematic diagram of a 3D printed model and its support structure constructed from voxels in one embodiment of this application. Figure 2In the voxel-based support structure N1 shown, the voxels close to the 3D printed model M are not tightly fitted. Therefore, this application discloses a method and system for reinforcing the support of a 3D printed model, as well as computer equipment, media, and computer program products. The method for reinforcing the support of a 3D printed model involves biasing the region corresponding to the voxel-based support structure of the 3D printed model, then determining support points located within the biased region on the surface of the 3D printed model, and connecting each determined support point to a support to be reinforced based on the support structure. This ensures a reliable connection between the support and the 3D printed model in the reinforced model, thereby giving the support sufficient load-bearing capacity and avoiding problems such as support instability and forming defects.

[0042] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. The technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of them. Based on the embodiments in the present application, all other embodiments and technical effects obtained by those skilled in the art without creative effort should fall within the scope of protection of the present application. The terms "an embodiment," "implementation," or similar wording used throughout this specification mean that a specific feature, structure, or characteristic described together with an implementation is included in at least one embodiment of the present application. Therefore, the appearance of the phrases "in an embodiment," "in an embodiment," and similar wording throughout this specification may (but does not necessarily) refer to the same implementation.

[0043] This application provides a method for supporting and reinforcing a 3D printed model in some embodiments. The method can be executed by a computer device equipped with a support and reinforcement system for the 3D printed model. The support and reinforcement system for the 3D printed model is a software tool or software module capable of processing data. It executes the method for supporting and reinforcing the 3D printed model with the help of the hardware devices and / or the operating environment provided by the operating system in the computer device.

[0044] In one embodiment, the computer device may be configured as an electronic device, that is, the electronic device performs the support and reinforcement method of the 3D printed model. For example, the electronic device includes a desktop computer, a laptop computer, a tablet computer, a smart TV, a smartphone, a tablet, an industrial control computer, etc. The electronic device may also be an electronic device consisting of a host with multiple virtual machines and human-computer interaction devices (such as touch screen, keyboard and mouse) corresponding to each virtual machine.

[0045] In one embodiment, the computer device may be configured as a server, that is, the server executes the support and reinforcement method for the 3D printed model. The server can be deployed on one or more physical servers based on factors such as functionality and load. In some examples, the server may be a cloud-based server, referring to a cloud computing platform provided by a cloud computing provider, which can provide services such as IaaS (Infrastructure-as-a-Service), PaaS (Platform-as-a-Service), and SaaS (Software-as-a-Service). The cloud computing platform includes public cloud, private cloud, and hybrid cloud. In some examples, the server may consist of a distributed or centralized server cluster. For example, the server cluster consists of at least one physical server. Each physical server is configured with multiple virtual servers, each virtual server runs at least one functional module of the system, and the virtual servers communicate with each other via a network.

[0046] Please see Figure 3 The figure shows a flowchart illustrating a method for supporting and reinforcing a 3D printed model according to one embodiment of this application. As shown, the method includes steps S110, S120, and S130. The following description uses the example of the method being executed by a computer device to illustrate each embodiment.

[0047] In step S110, the computer device acquires a model to be hardened.

[0048] The model to be reinforced includes a 3D printed model of the target part and its reinforcement support. The target part is the final printed part to be obtained through 3D printing. The 3D printed model of the target part is a virtual model corresponding to the target part. The reinforcement support is a model added to the 3D printed model of the target part to ensure the printing quality of the target part.

[0049] In some embodiments, the 3D printed model is constructed using a modeling method capable of generating smooth contours. A smooth contour refers to a model contour that exhibits a smooth shape, but it is not required to be strictly smooth; the contour can also be approximately smooth. For example, if the angle of change of the normals of adjacent faces in the contour is below an angle threshold, the contour can be considered a smooth contour. Examples of angle thresholds include 1°, 2°, or 3°. Examples of modeling methods capable of generating smooth contours include implicit modeling, parametric modeling within explicit modeling, or patch modeling within explicit modeling. In the following embodiments, patch modeling is used as an example to illustrate the modeling method capable of generating smooth contours.

[0050] In one example, the modeling method that generates a smooth contour is a patch modeling method. Accordingly, the 3D printed model is a model constructed from patch elements. A model constructed from patch elements means that the model is built based on patch elements, thus the 3D printed model can be constructed with a smooth contour. Examples of patch elements include triangular patch elements (also called triangular meshes or triangular patches) or quadrilateral patch elements. It should be noted that this application does not require the 3D printed model to be expressed by patch elements during the intermediate process. For example, in order to perform 3D printing process simulation on the 3D printed model, the 3D printed model can be converted to voxel representation during the 3D printing process simulation, and then the simulation results based on the voxel-represented 3D printed model can obtain the simulation results of each vertex of the patch elements.

[0051] In some embodiments where the 3D printed model is constructed using a continuous modeling method, such as implicit modeling or parametric modeling in explicit modeling, the 3D printed model can be converted into a model expressed by patch elements to facilitate the uniformity of input format during the calculation process (e.g., 3D printing process simulation).

[0052] In one embodiment, the support to be reinforced is determined based on a voxel-based support structure constructed from a 3D printed model.

[0053] In one embodiment, the voxel-based support structure refers to a support structure constructed based on voxels. It should be noted that this application does not limit the expression form of the voxel-based support structure in subsequent embodiments. For example, the voxel-based support structure can be converted into patch units after construction and then expressed by patch units, but the fact that the support structure is expressed by patch units does not affect the fact that the support structure is constructed from voxels. Since voxels are regular cubic units, when multiple voxels are used to approximate a continuous curved surface, the boundaries of the multiple voxels will exhibit a serrated (or stepped) contour. Therefore, the voxel-based support structure has a serrated contour, resulting in a serrated contour regardless of the form in which the voxel-based support structure is expressed. Correspondingly, the region corresponding to the voxel-based support structure also has a serrated contour.

[0054] In one embodiment, the support structure constructed from voxels is automatically generated. In order to automatically generate the support structure constructed from voxels based on the 3D printed model, the computer device further executes step S100 before executing step S110. In step S100, the computer device performs topology optimization in the support generation area of ​​the 3D printed model to obtain the support structure constructed from voxels.

[0055] Please see Figure 4 The figure shows a flowchart of step S100 in one embodiment of this application. As shown, step S100 includes step S1000 and step S1001.

[0056] In step S1000, the computer device acquires the 3D printing model and performs a 3D printing process simulation to obtain the current simulation results of the 3D printing model in real time.

[0057] In one embodiment, a computer device acquires the 3D printing model and performs a 3D printing process simulation on the 3D printing model to obtain the current simulation results of the 3D printing model in real time. Furthermore, the computer device also displays the thermal distribution characterizing the current simulation results in real time. The acquired 3D printing model is an unsupported model corresponding to the target part (i.e., the final printed part). The "thermal distribution" referred to in this application is a visual color map of the current simulation results.

[0058] In one embodiment, a computer device acquires the 3D printing model based on a user's import operation and performs a 3D printing process simulation on the acquired 3D printing model. The import operation includes, but is not limited to, clicking (e.g., clicking using an input device such as a mouse, or touching a touchscreen with a finger), long pressing, or repeated touches. For example, please refer to... Figure 5The figure shows a schematic diagram of a 3D printed model obtained in one embodiment of this application. As shown, the imported 3D printed model M is located on the construction plane G of the construction volume.

[0059] In another embodiment, when the 3D printed model is located outside the build volume or in an undesired position within the build volume, the computer device places the 3D printed model into the build volume and simulates the printing process. For example, the computer device may configure the 3D printed model in the build volume with a corresponding posture based on posture configuration parameters. These posture configuration parameters can be user-inputted or pre-configured, and include parameters indicating the position and posture of the 3D printed model within the build volume. This application does not limit the method of placing the 3D printed model into the build volume. For example, the imported 3D printed model can also be placed into the build volume in a posture manually by the user.

[0060] In one embodiment, the 3D printing process simulation can be either a mechanical simulation or a thermo-mechanical coupling simulation. For example, the computer device may only provide mechanical simulation, or it may only provide thermo-mechanical coupling simulation, or it may provide both mechanical and thermo-mechanical coupling simulations. The computer device performs mechanical or thermo-mechanical coupling simulations on the 3D printed model or the model to be optimized in subsequent embodiments based on the user's trigger operation in the simulation type selection interface. The simulation type selection interface displays either a mechanical simulation option or a thermo-mechanical coupling simulation option. In the following embodiments, thermo-mechanical coupling simulation is used as an example for illustration.

[0061] In one embodiment, the computer device solves the 3D printing process layer by layer, starting from the construction plane, according to the input processing parameters and the property parameters of the printing material. This simulates the 3D printing process gradually from the bottom of the 3D printed model, obtaining in real time the deformation results (deformation amount and direction) of each position of the currently simulated (i.e., currently simulated and printed) model, as well as the stress results of each position of the model in the simulated region. The deformation results (deformation amount and direction) of each position of the model in the simulated region are used as the current simulation result. In other examples, the stress results (stress magnitude and stress direction) of each position of the model in the simulated region can also be used as the current simulation result. For ease of explanation, in subsequent embodiments, the current simulation result is described as the deformation results of each position of the model in the simulated region. When the current simulation result is the stress results of each position of the model in the simulated region, those skilled in the art can adaptively adjust the preset triggering conditions in subsequent embodiments.

[0062] In one embodiment, the computer device maps the obtained current simulation results to thermal values ​​(color values) to obtain a thermal distribution characterizing the current simulation results, which can then be displayed in real time. For example, the deformation amount in the deformation results at each location is converted into thermal values. In one embodiment, the computer device overlays the thermal distribution onto the 3D printed model in real time, allowing the user to visually view the deformation distribution. For example, please refer to... Figure 6 and combined Figure 5 , Figure 6 As shown in one embodiment of this application, the thermal distribution is superimposed and displayed. Figure 5 The diagram shown is an illustration of a 3D printed model. Figure 5 and Figure 6 As shown, the computer device will characterize Figure 5 The thermal distribution A of the current simulation results of the 3D printed model shown is superimposed in real time on... Figure 5 The results are displayed on the 3D printed model M, allowing users to intuitively see the current simulation results.

[0063] In step S1001, when the current simulation result meets the preset triggering condition, the computer device triggers the generation calculation of the support structure.

[0064] In one embodiment, the preset triggering condition is that the maximum deformation value in the current simulation result is greater than or equal to a preset maximum deformation. Specifically, when the maximum deformation value in the current simulation result is greater than or equal to the preset maximum deformation, the current simulation result is determined to meet the preset triggering condition. For example, if the preset maximum deformation is 0.5 mm, and the deformation value at a certain location in the current simulation result is 0.6 mm, then the computer device determines that the current simulation result meets the preset triggering condition. This ensures that support structures are added to the 3D printed model when significant deformation is possible, avoiding the addition of support structures to 3D printed models that do not require them, thus ensuring the rationality of support structure addition.

[0065] In another embodiment, the preset triggering condition includes a first triggering condition and a second triggering condition. The current simulation result is determined to satisfy the preset triggering condition when either the first or second triggering condition is met. For example, when the current simulation result satisfies the second triggering condition, the computer device determines that the current simulation result satisfies the preset triggering condition. Similarly, when the current simulation result satisfies the first triggering condition, the computer device determines that the current simulation result satisfies the preset triggering condition. The first triggering condition is that the maximum deformation value in the current simulation result is above a preset maximum deformation. The specific implementation of satisfying the first triggering condition is the same as or similar to that described above and will not be repeated here. The second triggering condition is that the minimum deformation value in the current simulation result is above a preset minimum deformation and the entire 3D printed model is in a suspended state. The entire 3D printed model being in a suspended state means that the minimum distance (also called vertical distance) between each point in the 3D printed model and the building plane is greater than 0. In other words, the entire 3D printed model being in a suspended state means that there are no points in the 3D printed model in contact with the building plane. For example, if the preset minimum deformation is 0mm, the computer determines that the current simulation result meets the second trigger condition as long as the 3D printed model starts the 3D printing process simulation (i.e., when a current simulation result exists) and the 3D printed model is in a suspended state. It should be noted that if the 3D printed model starts the 3D printing process simulation but is not in a suspended state, the computer determines that the current simulation result does not meet the second trigger condition. By setting the second trigger condition in the preset trigger conditions, support structures can be automatically added to the suspended 3D printed model at the start of the simulation. This ensures that support structures are automatically added to support the suspended parts while avoiding the unreasonable addition of subsequent support structures due to the 3D printed model being suspended, which would affect the simulation results.

[0066] In one embodiment, the preset maximum deformation is input by the user. The 3D printed model support and reinforcement method further includes providing a first deformation input window for the user to input the preset maximum deformation. See also... Figure 7 The diagram shows an interactive interface of the first deformation input window in one embodiment of this application. The first deformation input window provides an input box for a preset maximum deformation, where the user can input data to complete the input of the preset maximum deformation. It should be understood that... Figure 7 This is merely an example. Those skilled in the art can also make adaptive adjustments to the content and interface layout that the first deformable input window can display under the guidance of this application, and this application does not impose any limitations on this.

[0067] In one embodiment, the preset minimum deformation is input by the user. The 3D printed model support and reinforcement method provides a second deformation input window for the user to input the steps of the preset minimum deformation. Please refer to [link to relevant documentation]. Figure 8 The diagram shows an interactive interface of the second deformation input window in one embodiment of this application. The second deformation input window provides an input box with a preset minimum deformation, where the user can input data to complete the preset minimum deformation. It should be understood that... Figure 8 This is merely an example. Those skilled in the art can also make adaptive adjustments to the content and interface layout that the second modified input window can display under the guidance of this application, and this application does not impose any limitations on this.

[0068] Although the preset minimum deformation and preset maximum deformation are input in different input windows in the above embodiments, in other implementations, the preset minimum deformation and preset maximum deformation can also be input in the same input window; or, the preset minimum deformation and preset maximum deformation can also be automatically given by the system from the process library according to the material, printing process and target accuracy.

[0069] In embodiments of this application, the calculation of generating the support structure performed by the computer device includes: performing topology optimization within the support generation region based on dimensional constraints and / or upper surface constraints, with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model, to generate a support structure constructed from voxels so that the structural stiffness of the 3D printed model is optimal.

[0070] In one embodiment, please refer to Figure 9 The figure shows a flowchart of topology optimization to generate a support structure constructed from voxels in one embodiment of this application. As shown in the figure, topology optimization is performed in the support generation area according to preset size constraints and / or upper surface constraints, with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model, to generate a support structure constructed from voxels so that the structural stiffness of the 3D printed model is optimal. The steps include steps S10010, S10011, and S10012.

[0071] In step S10010, the computer device determines a bounding box that can surround the 3D printed model based on the 3D printed model.

[0072] The bounding box can be defined as the smallest space that can enclose the 3D printed model. It can be, for example, an AABB (Axis Aligned Bounding Box) or an OBB (Oriented Bounding Box). The length, width, and height of the bounding box are the dimensions occupied by the bounding box on the X-axis, Y-axis, and Z-axis, respectively.

[0073] In step S10011, the computer device uses a bounding box to determine the support generation area.

[0074] In one embodiment, the step of the computer device determining the support generation region using the bounding box includes: directly using the bounding box as the support generation region. Specifically, the computer device directly uses the bounding box obtained in step S10010 as the support generation region of the support structure.

[0075] In another embodiment, the step of the computer device determining the support generation region using the bounding box includes: biasing the bounding box based on expansion parameters input by the user in the region expansion interface to use the biased bounding box as the support generation region. In one example, the input expansion parameters include only a single overall parameter that indicates the length, width, and height of the bounding box for biasing. For example, see [link to relevant documentation]. Figure 10 The diagram shows a bounding box and a corresponding support generation area in one embodiment of this application. In this example, the overall parameter is c. The computer device expands the length, width, and height of the bounding box D1 bidirectionally according to the overall parameter c to obtain the support generation area D2. In another example, the input expansion parameters include parameters in the length direction, width direction, and height direction. The computer device then expands the length of the bounding box bidirectionally based on the length direction parameter, expands the width of the bounding box bidirectionally based on the width direction parameter, and expands the height of the bounding box bidirectionally based on the height direction parameter to obtain the support generation area. The expansion parameters in any of the above examples are input by the user in the area expansion interface. Therefore, the support reinforcement method for the 3D printed model further includes the step of providing an area expansion interface for the user to input the expansion parameters. The area expansion interface can be an independent interface or it can be in the same interface as the first deformation input window and the second deformation input window in the aforementioned embodiments.

[0076] In one specific embodiment, where the preset triggering conditions include a first triggering condition and a second triggering condition, and the 3D printed model is in a suspended state, in order to ensure that the support generation area includes the suspended area corresponding to the bottom of the 3D printed model, thereby guaranteeing the generation of a voxel-based support structure to support the 3D printed model in the suspended area, the computer device needs to offset the bounding box based on the expansion parameters input by the user in the area expansion interface, using the offset bounding box as the support generation area. For example, the input overall parameter is not less than the suspended height of the 3D printed model (i.e., the vertical distance between the 3D printed model and the construction plane). Similarly, the input height direction parameter is not less than the suspended height of the 3D printed model.

[0077] In another embodiment, the step of the computer device determining the support generation region using the bounding box includes: using the Boolean difference between the bounding box and a spatial model as the support generation region, wherein the region corresponding to the spatial model is a region where the generation of support structures is prohibited. By using the Boolean difference between the bounding box and a spatial model as the support generation region, the support generation region can be limited according to user needs to avoid generating support structures in regions unsuitable for them. In some examples, the spatial model may include functional regions of the 3D printed model, regions where it is inconvenient to remove supports, etc.

[0078] Please see Figure 11 The figure shows a schematic diagram of a 3D printed model and a spatial domain model in one embodiment of this application. As shown, the computer device can use the Boolean difference between the bounding box of the 3D printed model M and the spatial domain model D3 as the support generation region. That is, the region obtained after removing the part of the bounding box of the 3D printed model M that overlaps with the spatial domain model D3 is used as the support generation region. Although the above embodiment uses the Boolean difference between the bounding box and a spatial domain model as the support generation region as an example, in other embodiments, the computer device can also use the Boolean difference between the offset bounding box and a spatial domain model as the support generation region.

[0079] In some embodiments, the spatial domain model can be a solid model or a shell model. The solid model has both its interior and surface as solid regions, meaning the interior of the solid model will also be printed by the 3D printer. The shell model has only its surface as a solid region, while its interior is a hollow region; that is, only the surface of the shell model will be printed by the 3D printer. In embodiments where the spatial domain model is a solid model, the entire solid region of the solid model is a region where the generation of support structures is prohibited. In embodiments where the spatial domain model is a shell model, the surface of the shell model is a region where the generation of support structures is prohibited.

[0080] In one embodiment, the spatial domain model is obtained based on a user-created initial model and input spatial domain information. The spatial domain information indicates whether the initial model is a solid model or a shell model. The initial model is a user-created model with complete geometric shape and size information. For example, if the user-created initial model is a cylinder, and the input spatial domain information indicates that the initial model is a solid model, then the spatial domain model obtained by the computer device is a solid cylinder. As another example, if the user-created initial model is a cylinder, and the input spatial domain information indicates that the initial model is a shell model, then the spatial domain model obtained by the computer device is a hollow cylinder.

[0081] In one embodiment, the support and reinforcement method for the 3D printed model further includes providing a spatial information input interface for a user to input the spatial information. The spatial information can be represented by text, icons, numbers, letters, etc. In an embodiment where the spatial information is represented by text, the word "entity" can be used to indicate that the initial model is a solid model, and the word "non-entity" can be used to indicate that the initial model is a shell model.

[0082] Step S10012: Within the support generation area, based on size constraints and / or upper surface constraints, topology optimization is performed with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model, so that the structure constructed from voxels corresponding to the iteration cutoff condition is used as the support structure constructed from voxels.

[0083] The dimensional constraint is that the ratio of the size of the support structure constructed from voxels to the size of the support generation area is below a preset ratio. The size is exemplified by volume. For instance, if the preset ratio is b, then the dimensional constraint is that the ratio of the volume of the support structure constructed from voxels to the volume of the support generation area is below b. Although the above embodiment uses volume as an example, it is not limited to this. In other embodiments, the size can also be length, width, and height. For example, if the preset ratio is b, the dimensional constraint is that the ratio of the length of the support structure constructed from voxels to the length of the support generation area is below b, the ratio of the width of the support structure constructed from voxels to the width of the support generation area is below b, and the ratio of the height of the support structure constructed from voxels to the height of the support generation area is below b.

[0084] The upper surface constraint prohibits the generation of supports on the upper surface of the 3D printed model to ensure appearance and detachability. The normal vector of the upper surface of the 3D printed model has an upward component. In one example, if the upper side of the 3D printed model is the positive Z-axis in computer 3D space, then the normal vector of the patch elements on the upper surface of the 3D printed model has a component along the positive Z-axis. Please continue reading. Figure 5 , Figure 5 The 3D printed model M shown has an upper surface M1 and a lower surface M2. The normal vector of the patch element of the upper surface M1 has a component along the positive Z-axis.

[0085] The flexibility of the 3D printed model is used to characterize the deformation ability of the 3D printed model under external load. The larger the value, the easier the 3D printed model is to deform. The stiffness of the 3D printed model is used to characterize the ability of the 3D printed model to resist deformation and is inversely proportional to the flexibility.

[0086] In order to make the calculation of the flexibility of the 3D printed model take into account the combined effects of gravity load and residual stress load during the printing process, the flexibility of the 3D printed model includes the flexibility under gravity load and the flexibility under residual stress load. In other words, the external load includes gravity load and residual stress load. For example, the flexibility of the 3D printed model can be expressed by the following formula (1):

[0087] (1)

[0088] in, Indicates the flexibility of a 3D printed model. For gravity load, Represented as transpose, For residual stress load, Let F1 be the transpose of F2, and U2 be the displacement of the 3D printed model under the action of F1. This is the weighting coefficient for the gravity load. is the weighting coefficient for residual stress load, where w1+w2=1.

[0089] In embodiments where the compliance of the 3D printed model includes compliance under gravity load and compliance under residual stress load, to quickly calculate the residual stress load, this application obtains the residual stress load of the entire 3D printed model using inherent strain values. In embodiments of this application, the computer device quickly predicts the residual stress load of the 3D printed model using the inherent strain values ​​of the printing material (including inherent strain values ​​in the X and Y directions). For example, the residual stress load of the 3D printed model is obtained based on the inherent strain values ​​and elastic finite element calculations. Furthermore, to simplify the calculation, the residual stress load remains unchanged throughout the topology optimization process.

[0090] In other embodiments, the flexibility of the 3D printed model may also include only the flexibility under gravity load; in other words, the external load includes only gravity load.

[0091] The iteration cutoff conditions include the topology optimization iteration count reaching a preset number and / or the target value of the 3D printed model being within a preset target threshold for two consecutive iterations. The target value is the flexibility or stiffness of the 3D printed model.

[0092] In one embodiment, topology optimization is performed within the support generation region based on size constraints, with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model. The structure constructed from voxels that reaches the iteration cutoff condition is used as the support structure constructed from voxels, and the support structure constructed from voxels enables the 3D printed model to have optimal structural stiffness.

[0093] In another embodiment, topology optimization is performed within the support generation region based on a preset upper surface constraint, with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model. The structure constructed from voxels that reaches the iteration cutoff condition is used as the support structure constructed from voxels, and the support structure constructed from voxels enables the 3D printed model to have optimal structural stiffness.

[0094] In another embodiment, topology optimization is performed within the support generation region based on size constraints and upper surface constraints, with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model. The structure constructed from voxels that reaches the iteration cutoff condition is used as the support structure constructed from voxels, and the support structure constructed from voxels enables the 3D printed model to have optimal structural stiffness.

[0095] For ease of explanation, the following embodiments will use the example of the computer device performing topology optimization based on size constraints and upper surface constraints.

[0096] In some examples, the iteration cutoff condition is that the number of iterations for topology optimization reaches a preset number. Specifically, within the support generation region, based on dimensional and upper surface constraints, topology optimization is performed with the goal of minimizing the flexibility or maximizing the stiffness of the 3D printed model. When the preset number of iterations is reached, the corresponding voxel-constructed structure is taken as the voxel-constructed support structure that optimizes the structural stiffness of the 3D printed model. This ensures that the generated voxel-constructed support structure guarantees optimal structural stiffness of the 3D printed model while satisfying the dimensional and upper surface constraints.

[0097] In other examples, the iteration cutoff condition is that the target value of the 3D printed model in two consecutive iterations is within a preset target threshold. For instance, within the support generation area, the computer device performs topology optimization based on dimensional and upper surface constraints, aiming to minimize the flexibility of the 3D printed model. The structure constructed from voxels corresponding to the minimum flexibility, where the flexibility of the 3D printed model in two consecutive iterations is within the preset target threshold, is selected as the voxel-constructed support structure that optimizes the structural stiffness of the 3D printed model. As another example, within the support generation area, the computer device performs topology optimization based on dimensional and upper surface constraints, aiming to maximize the stiffness of the 3D printed model. The structure constructed from voxels corresponding to the maximum stiffness, where the stiffness of the 3D printed model in two consecutive iterations is within the preset target threshold, is selected as the voxel-constructed support structure that optimizes the structural stiffness of the 3D printed model.

[0098] In one embodiment, the topology optimization is voxel-based. For example, each voxel in the support generation region has a design variable. Voxel-based topology optimization iteratively adjusts the design variables of each voxel to determine which regions are retained. Then, when the iteration cutoff condition is met, the structure constructed by the retained voxels is taken as the voxel-constructed support structure that optimizes the structural stiffness of the 3D printed model (i.e., the result of topology optimization).

[0099] In one embodiment, step S10012 further includes providing a task progress window to display the generation progress of the support structure. Specifically, the task progress window displays the task type and task progress. The task progress can be represented by a percentage or other numerical values ​​and / or a progress bar. For an example, please refer to... Figure 12 The image shown is a schematic diagram of an interactive interface including a task progress window in one embodiment of this application. Figure 12 The progress of the construction of the intermediate support structure is represented by a progress bar and a percentage.

[0100] In one specific embodiment, please refer to Figure 13 and combined Figure 5 , Figure 13 This application is displayed. Figure 5 The diagram shows the 3D printed model and the corresponding generated support structure constructed from voxels. This is relevant to the simulation of the 3D printing process. Figure 5 When the maximum deformation in the current simulation results of the 3D printed model M exceeds the preset maximum deformation, the generation calculation of the support structure will be triggered. During the support structure generation calculation, the computer device performs topology optimization within the bounding box of the 3D printed model M, based on dimensional constraints and upper surface constraints, with the goal of minimizing the flexibility of the 3D printed model, to generate a structure that optimizes the structural stiffness of the 3D printed model M. Figure 13 The support structure N1, constructed from voxels, is shown.

[0101] In another specific embodiment, please refer to Figure 14 and Figure 15 , Figure 14 The diagram shown is a schematic representation of a 3D printed model obtained in another embodiment of this application. Figure 15 This application is displayed. Figure 14 The figure shows a schematic diagram of the 3D printed model and the corresponding generated support structure constructed from voxels. During the 3D printing process simulation, when... Figure 14 When the maximum deformation in the current simulation results of the 3D printed model M exceeds the preset maximum deformation, the generation calculation of the support structure will be triggered. During the support structure generation calculation, the computer device performs topology optimization within the support generation area of ​​the 3D printed model M, based on dimensional constraints and upper surface constraints, with the goal of minimizing the flexibility of the 3D printed model, to generate a structure that optimizes the structural stiffness of the 3D printed model M. Figure 15 The support structure N1, constructed from voxels, is shown. It should be noted that... Figure 15 The support structure N1, constructed from voxels, shown is represented by triangular facet units.

[0102] In yet another specific embodiment, please refer to Figure 16 The image shows a schematic diagram of a suspended 3D printed model and the corresponding generated support structure constructed from voxels in one embodiment of this application. Figure 16 As shown, the 3D printed model M is suspended in mid-air. During the 3D printing simulation, when the current simulation result of the 3D printed model M meets the second trigger condition, the generation calculation of the support structure is triggered. In the generation calculation of the support structure, the computer device performs topology optimization within the bounding box of the offset 3D printed model M based on dimensional constraints and upper surface constraints, with the goal of minimizing the flexibility of the 3D printed model, to generate a structure that optimizes the structural stiffness of the 3D printed model M. Figure 16 The support structure N1, constructed from voxels, is shown.

[0103] In another specific embodiment, please refer to Figure 17 and combined Figure 11 , Figure 17 The diagram shown illustrates a suspended 3D printed model and the corresponding generated support structure constructed from voxels in another embodiment of this application. Figure 11As shown, the 3D printed model M is in a suspended state. During the 3D printing simulation, when the current simulation result of the 3D printed model M meets the second trigger condition, the generation calculation of the support structure will be triggered. In the generation calculation of the support structure, the computer device performs topology optimization within the support generation region corresponding to the Boolean difference between the bounding box of the biased 3D printed model M and the spatial model D3, based on size constraints and upper surface constraints, with the goal of minimizing the flexibility of the 3D printed model, to generate a structure that optimizes the structural stiffness of the 3D printed model M. Figure 17 The support structure N1, constructed from voxels, is shown.

[0104] In one embodiment, in order to accelerate the calculation speed of the support structure, the support reinforcement method of the 3D printed model further includes the step of stopping the 3D printing process simulation during the topology optimization process described in any of the above embodiments to release computing resources.

[0105] In one embodiment, the generated support structure constructed from voxels is generated under the condition of satisfying a second triggering condition. The support reinforcement method for the 3D printed model further includes: performing a 3D printing process simulation on the structure including the generated support structure constructed from voxels and the 3D printed model to obtain the current simulation result in real time, and continuing to perform topology optimization if the maximum deformation value in the current simulation result is above a preset maximum deformation. For example, please refer to [link to relevant documentation]. Figure 16 and Figure 17 The support structure N1 constructed from voxels generated in step S1001 is generated under the condition of satisfying the second triggering condition. Therefore, it can be further determined whether the structure including the generated support structure constructed from voxels and the structure of the 3D printed model has a maximum deformation value in the current simulation result that exceeds the preset maximum deformation. Therefore, the computer device will re-execute the 3D printing process simulation on the structure including the generated support structure constructed from voxels and the structure of the 3D printed model to obtain the current simulation result in real time. If the maximum deformation value in the current simulation result exceeds the preset maximum deformation, topology optimization will continue to be performed. As a result, the 3D printed model has a support structure constructed from voxels obtained from two topology optimization calculations.

[0106] In one embodiment, the step of obtaining a model to be reinforced includes determining the support to be reinforced using a voxel-based support structure of the 3D printed model. In one example, the voxel-based support structure is used as the support to be reinforced. For example, the voxel-based support structure obtained through topology optimization in any of the above embodiments can be used as the support to be reinforced. Figure 15The voxel-based support structure shown serves as the support to be reinforced. In another example, basic units are filled into the region corresponding to the voxel-based support structure to form a lattice-like support structure, which serves as the support to be reinforced. For example, the lattice-like support structure is composed of basic units. The basic units are rod-shaped lattices, planar lattices, or hybrid lattices. The rod-shaped lattice is a structure composed of rod units, and examples of rod-shaped lattices include BCC lattices, FCC lattices, Octet lattices, and Vino lattices. The planar lattice is a structure formed by curved surfaces and / or planes, and examples of planar lattices include Gyroid lattices, Schwarz-P lattices, Diamond lattices, and TPMS lattices. The hybrid lattice includes rod-shaped lattices and planar lattices, and examples of hybrid lattices include framework-membrane composite structures. In the following embodiments, the basic unit is a rod-shaped lattice, which is used as an example for illustration.

[0107] In some embodiments, the basic unit filling methods include, but are not limited to, periodic lattice replication, implicit modeling-based filling, and sampling point-based filling. The periodic lattice replication method involves translating, rotating, or mirroring the basic units within a region to achieve a periodic arrangement. The implicit modeling-based filling method generates basic units within a region by defining implicit functions and utilizing the isosurfaces of these implicit functions; for example, implicit functions constructed using three-dimensional periodic functions can form continuous TPMS structures. The sampling point-based filling method first generates a set of sampling points within a region, and then uses these sampling points to generate connecting lines and / or connecting surfaces to construct the basic units.

[0108] The following explanation uses a sampling-point-based filling method as an example to illustrate the basic unit filling method. Please refer to [link / reference]. Figure 18 and combined Figure 15 , Figure 18 This application is displayed as being in Figure 15 The diagram shown illustrates the filling of basic units within the area corresponding to the support structure constructed from voxels. Specifically, the computer device... Figure 15 Sampling is performed within the region corresponding to the support structure N1 constructed from voxels, and then a model is generated based on the sampling points. Figure 18 The image shows the support B to be reinforced, which is composed of a rod-shaped lattice. It should be noted that using a lattice-shaped support structure as the support to be reinforced is to ensure the strength of the support while avoiding material waste. This application does not limit the specific type of basic unit or the filling method.

[0109] In step S120, the computer device biases the area corresponding to the support structure based on the bias parameters to obtain the biased area that intersects with the 3D printed model.

[0110] The offset parameter indicates the outward offset dimension of the area corresponding to the support structure. The offset parameter can be preset or input by the user in an offset parameter input interface. In embodiments where the offset parameter is input by the user in an offset parameter input interface, the 3D printed model support reinforcement method further includes providing an offset parameter input window for the user to input the offset parameter. For example, the offset parameter input window may provide an input box for the offset parameter, where the user can input data to complete the input of the offset parameter. Alternatively, the offset parameter input window may provide a selection box for the offset parameter, where the user can select data to complete the input of the offset parameter. It should be understood that those skilled in the art can also make adaptive adjustments to the offset parameter input window under the guidance of this application, and this application does not limit this.

[0111] In one embodiment, the bias parameter is close to the size of the voxel that constructs the support structure; in one example, the bias parameter is not smaller than the size of the voxel that constructs the support structure. For example, the value of the bias parameter ranges from 1 to 3 times the voxel size, such as 1 time the voxel size, 2 times the voxel size, or 3 times the voxel size. In the embodiment where the voxel is a cube, the size of the voxel is the side length of the voxel. In the embodiment where the voxel is a cuboid, the size of the voxel can be represented by any one of the length, width, height, or height of the cuboid.

[0112] In another embodiment, the bias parameter is in the range of 0.1-10mm, for example, the bias parameter is 0.1mm, 1mm, 2mm, 3mm, 4mm, 5mm, 6mm, 7mm, 8mm, 9mm, or 10mm.

[0113] The intersection of the offset region with the 3D printed model means that the offset region and the 3D printed model have an overlapping portion; in other words, the top of the offset region has a portion located on the 3D printed model. It should be noted that this application does not require that every position of the top of the offset region be located on the 3D printed model.

[0114] In one embodiment, the computer device can implicitly model the region corresponding to the support structure based on the bias parameters to offset it outwards. See also... Figure 19 and combined Figure 15 , Figure 19 This application is shown as an embodiment of the present application. Figure 15 The diagram shows the area corresponding to the supporting structure after being offset outwards, as shown in the figure. Implicit modeling is used to... Figure 15 The area corresponding to the supporting structure in the middle is offset outward to obtain Figure 19The biased region D4 shown has an overlap with the 3D printed model M.

[0115] In another embodiment, the computer device may also use morphological dilation operations based on the bias parameters to offset the region corresponding to the support structure outward. For example, please refer to... Figure 20 And combined with 2, Figure 20 This application is shown as an embodiment of the present application. Figure 2 The diagram shows the region corresponding to the supporting structure after being offset outwards, as shown in the figure. Morphological expansion is used to... Figure 2 The area corresponding to the supporting structure in the middle is offset outward to obtain Figure 20 The offset region D4 shown has an overlap with the 3D printed model M before offset, where the support structure only partially fits the 3D printed model.

[0116] After obtaining the biased region, the computer device executes step S130.

[0117] In step S130, the computer device determines the support points located in the offset region on the surface of the 3D printed model, and connects each determined support point to the support to be reinforced to obtain the reinforced model.

[0118] Specifically, the computer device identifies support points located in the offset region on the surface of the 3D printed model, and determines a reinforcement support for each support point. The support points are then connected to the support to be reinforced through the reinforcement support corresponding to each support point, so as to obtain the reinforced model.

[0119] In one embodiment, please refer to Figure 21 The figure shows a flowchart illustrating the steps of determining support points located in the offset region in one embodiment of this application. As shown, the steps of determining support points located in the offset region on the surface of the 3D printed model include steps S1300 and S1301.

[0120] In step S1300, the computer device samples the surface of the 3D printed model according to the preset support point sampling parameters to obtain multiple support points on the surface of the 3D printed model.

[0121] The preset support point sampling parameters are used to define the position of the sampled support points. In some embodiments, the preset support point sampling parameters include a critical angle and / or a minimum support height. The critical angle is used to define the angle between the surface where the sampled support point is located and the vertical direction. Specifically, the angle between the surface where the sampled support point is located and the vertical direction must be above the critical angle. It should be noted that when the surface where the sampled support point is located is curved, the angle between the tangent at the sampled support point and the vertical direction can be used as the angle between the surface where the sampled support point is located and the vertical direction. In one example, the value range of the critical angle is 50°-60°, for example, the critical angle is 50°, 51°, 52°, 53°, 54°, 55°, 56°, 57°, 58°, 59°, or 60°. The minimum support height is used to define the minimum distance between the sampled support point and the construction plane. Specifically, the minimum distance between the sampled support point and the construction plane must be above the minimum support height. For example, the minimum support height is 1mm, 2mm, or 3mm.

[0122] Furthermore, in one embodiment, the preset support point sampling parameters further include a maximum support point spacing and / or a minimum support point spacing, wherein the maximum support point spacing is used to limit the maximum spacing between two adjacent support points, and the minimum support point spacing is used to limit the minimum spacing between two adjacent support points.

[0123] In one embodiment, the surface of the 3D printed model is the lower surface, meaning that the computer device samples the lower surface of the 3D printed model according to preset support point sampling parameters to avoid generating redundant reinforcement supports. For example, the normal vector of the patch unit of the lower surface has a negative component along the Z-axis.

[0124] In step S1301, the computer device determines the support points located within the offset region based on the determined multiple support points and the offset region.

[0125] In one embodiment, the computer device filters the plurality of support points determined in step S1300 based on the offset region to determine the support points located within the offset region.

[0126] In one embodiment, step S130 further includes determining a reinforcing support for each support point to connect each support point to the support to be reinforced via a corresponding reinforcing support. In some examples, the reinforcing support may be a rod support or a surface support.

[0127] In one embodiment, the step of determining a reinforcement support for a support point includes determining the nearest position on the support to be reinforced to the support point and establishing a reinforcement support that satisfies a support angle constraint between the support point and the nearest position. The computer device can use the position on the support to be reinforced that is closest to the support point in the vertical direction as the nearest position, or it can use the position on the support to be reinforced that is closest to the support point by a line connecting the support point and the support point as the nearest position. The support angle constraint is that the angle between the support and the horizontal plane is greater than a preset angle. For example, the preset angle ranges from 35° to 45°, such as 35°, 36°, 37°, 38°, 40°, 41°, 42°, 43°, 44°, or 45°.

[0128] In one specific embodiment, taking the reinforcement support as an example including rod supports, the computer device can establish at least one rod support between the support point and the position on the support to be reinforced closest to the support point, and the angle between each rod support and the horizontal plane is greater than the preset angle. For example, please refer to... Figure 22 and combined Figure 18 and Figure 19 , Figure 22 This application is shown as a counterpart to... Figure 18 The diagram shows a reinforced support after reinforcement. The computer device determines the location on the surface of the 3D printed model M. Figure 19 The support points of D4 within the offset region are shown, and the determined support points are connected to the 3D printed model M through rod support N2, thereby obtaining... Figure 22 The reinforced model shown.

[0129] In one embodiment, the support to be reinforced is a lattice-structured support. The method for reinforcing the 3D printed model further includes: optimizing the wall thickness of the support to be reinforced and / or the reinforced support based on the simulation results of the reinforced model to obtain an optimized model. The simulation results of the reinforced model include the simulation results of the 3D printed model, the support to be reinforced, and the reinforced support. Specifically, a computer device performs a 3D printing process simulation on the reinforced model to obtain the simulation results of the reinforced model. Then, based on the simulation results of the reinforced model, the wall thickness of the support to be reinforced and / or the reinforced support is optimized to obtain the optimized model. This ensures that the lightweight support can be tightly connected to the 3D printed model while also providing sufficient strength at locations with large deformation or stress.

[0130] In one embodiment, taking the optimization of the wall thickness of the support to be reinforced and the reinforced support based on simulation results to obtain an optimized model as an example, the method of optimizing the wall thickness is explained in detail. Specifically, the computer device adjusts the wall thickness of the support to be reinforced and the reinforced support based on the simulation results and a preset control relationship. Taking the stress magnitude at different locations in the support to be reinforced and the reinforced support as an example, the preset control relationship represents the mapping relationship between stress magnitude and wall thickness. The preset control relationship is a positive linear relationship. Specifically, the computer device can obtain the wall thickness at different locations by inputting the stress magnitude at different locations into the preset control relationship to optimize the wall thickness of the support to be reinforced and the reinforced support.

[0131] In one embodiment, the "preset control relationship" can be generated from the simulation results of the support to be reinforced and the reinforced support, the rule base, or the threshold given by the user, and can be obtained by linear interpolation, piecewise linear interpolation, or table lookup method.

[0132] In one embodiment, the support and reinforcement method for the 3D printed model further includes a step of determining the preset control relationship based on the upper limit of stress magnitude, the lower limit of stress magnitude, the upper limit of wall thickness, and the lower limit of wall thickness. In a specific example, the preset control relationship can be obtained according to the linear interpolation formula and the upper limit of stress magnitude, the lower limit of stress magnitude, the upper limit of wall thickness, and the lower limit of wall thickness. The linear interpolation formula is shown in the following formula (2):

[0133] K=K min +(HH) min )×(K max -K min ) / (H max -H min (2)

[0134] Where K is the wall thickness, H is the stress magnitude, and K max K is the upper limit of the wall thickness. min H is the lower limit of wall thickness. max H is the upper limit of the stress magnitude. min This represents the lower limit of the stress magnitude.

[0135] In one embodiment, the upper and lower limits of the stress magnitude can be obtained from the upper and lower limits of the stress magnitude in the simulation results of the support to be reinforced and the reinforced support, or they can be given by the user based on experience.

[0136] In one embodiment, the support and reinforcement method for the 3D printed model further includes providing a control relationship construction window for the user to input the upper and lower limits of the stress magnitude and the upper and lower limits of the wall thickness. For example, the control relationship construction window provides input boxes for the upper limit of the wall thickness, the lower limit of the wall thickness, the upper limit of the stress magnitude, and the lower limit of the stress magnitude, and the user can input the corresponding data in each input box.

[0137] In one embodiment, to further reinforce the support, the support reinforcement method for the 3D printed model further includes steps S1400 and S1401.

[0138] In step S1400, the computer device performs a 3D printing process simulation on the reinforced model to obtain the simulation results of the reinforced model;

[0139] The specific implementation method for simulating the 3D printing process of the reinforced model using computer equipment is the same as or similar to the specific implementation method for simulating the 3D printing process of the 3D printed model in the aforementioned embodiments, and will not be repeated here.

[0140] In step S1401, the computer device adds auxiliary supports that satisfy the support angle constraints to the 3D printed model based on the simulation results of the reinforced model.

[0141] In one embodiment, the computer device adds auxiliary supports that satisfy the support angle constraints to the 3D printed model based on the simulation results of the support to be reinforced and / or the reinforced support. The support angle constraints are the same as or similar to those described above and will not be repeated here. The auxiliary supports can be rod supports or surface supports.

[0142] Taking the example of adding auxiliary supports that meet the support angle constraints to the 3D printed model using only the simulation results of the support to be reinforced, and using the simulation results of the support to be reinforced as the stress magnitude at each location of the support to be reinforced, the computer equipment determines the locations in the simulation results of the support to be reinforced that exceed the stress critical value, and adds auxiliary supports that meet the support angle constraints at the locations of the stress critical value. For example, please refer to [link to example]. Figure 23 and combined Figure 22 and Figure 18 , Figure 23 This application is shown as a counterpart to... Figure 22 The diagram shown is a schematic of the 3D printed model after the auxiliary support is added. As shown in the figure, in the simulation results of the support B to be reinforced, the computer device adds an auxiliary support N3 configured as a rod support to meet the support angle constraint at each position where the stress critical value is exceeded.

[0143] It should be noted that in embodiments where the simulation results represent the deformation at various locations of the support to be reinforced, the critical stress value should be adaptively adjusted to the critical deformation value. In other embodiments, auxiliary supports satisfying the support angle constraints can be added to the 3D printed model using only the simulation results of the reinforced support, or auxiliary supports satisfying the support angle constraints can be added to the 3D printed model using the simulation results of both the support to be reinforced and the reinforced support.

[0144] In some embodiments, this application also proposes a support and reinforcement system for 3D printed models, which can be deployed, for example, in a computer device as a software tool or software module capable of processing data, performing data processing with the help of the hardware devices and / or the operating environment provided by the operating system in the computer device.

[0145] Please see Figure 24 The figure shows a block diagram of a support and reinforcement system for a 3D printed model according to one embodiment of this application. As shown, the support and reinforcement system 1 for the 3D printed model includes an acquisition module 10, an offset module 11, and a reinforcement module 12. The acquisition module 10 is used to acquire a model to be reinforced, wherein the model to be reinforced includes a 3D printed model of a target part and its support to be reinforced; the support to be reinforced is determined based on a support structure constructed from voxels of the 3D printed model; the offset module 11 is used to offset the area corresponding to the support structure based on offset parameters to obtain an offset area intersecting with the 3D printed model; the reinforcement module 12 is used to determine support points located in the offset area on the surface of the 3D printed model and connect each determined support point to the support to be reinforced to obtain a reinforced model.

[0146] In one embodiment, the 3D printed model support and reinforcement system 1 includes an acquisition module 10, an offset module 11, and a reinforcement module 12, which coordinately execute the 3D printed model support and reinforcement methods disclosed in any of the foregoing embodiments of this application according to the functions described above. Please refer to the documentation for... Figures 1 to 23 In any embodiment described herein, for example, the acquisition module 10, the bias module 11, and the hardening module 12 respectively implement the functions in steps S110 to S130 of the aforementioned method.

[0147] The acquisition module 10, bias module 11, and hardening module 12 can also be implemented in software running on different types of processors. For example, an executable code module may include one or more physical or logical blocks of computer instructions organized as objects, programs, or functions. However, the executable files of the modules do not necessarily have to be physically located together, but may include different commands stored in different locations, which, when logically connected together, encompass the module and implement the module's specified objectives.

[0148] Of course, the executable code module can be one or more instructions, and can even be distributed across several different code segments, different programs, and multiple storage devices. Similarly, computational data can be identified and represented within the module, and can be embodied in any suitable form and organized in any suitable data structure. The computational data can be a single dataset, or can be distributed across different locations (including different storage devices), and can exist at least partially as electrical signals within a system or network. When the module or a portion thereof is implemented in software, the software portion is stored on one or more computer-readable media.

[0149] This application also provides a computer device, including a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computer device may be a server, a laptop computer, a desktop computer, an edge device, etc., and the embodiments of this application do not specifically limit it, nor do they limit the number of processors and memories in the computer device.

[0150] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one line is used in the diagram, but this does not imply that there is only one bus or one type of bus. A bus can include pathways for transmitting information between various components of a computer device (e.g., memory, processor, communication interfaces).

[0151] In one embodiment, the computer device is used to implement the support and reinforcement method for the 3D printed model described in any of the above embodiments. In one embodiment, the computer device is a device capable of performing digital calculations, logical processing, and information processing on data, including but not limited to: personal computers, industrial control computers, tablets, smartphones, servers, server clusters, smart terminals, cloud-based server systems, etc.

[0152] Please see Figure 25 The image shows a schematic diagram of a computer device 2 according to one embodiment of this application. The computer device 2 includes a storage device 20 and a processing device 21 connected to the storage device 20. Furthermore, the computer device also includes a communication interface 22.

[0153] In some embodiments, the processing unit 21 and the storage unit 20 in the computer device 2 are interconnected at high speed via a system bus; when the processing unit 21 executes the program in the storage unit 20, it can call the system kernel, graphics card interface, or GPU computing power to accelerate print simulation or topology optimization calculations. Optionally, the computer device 2 can interact with an external printer control unit or industrial control platform through a communication interface 22, thereby directly transmitting the generated support structure data to the print control terminal to achieve automated linkage from design to printing.

[0154] In some embodiments, the storage device 20 is used to store at least one program that can be executed by the processing device 21 to coordinate the storage device 20 in implementing the support and reinforcement method for the 3D printed model described in any of the above embodiments. Here, the storage device 20 includes, but is not limited to, read-only memory (ROM), random access memory (RAM), and nonvolatile RAM (NVRAM). For example, the storage device 20 includes flash memory or other nonvolatile solid-state storage devices. In some embodiments, the storage device 20 may also include memory located remotely from one or more processing devices 21, such as network-attached memory accessed via RF circuitry or external ports and communication networks, wherein the communication network may be the Internet, one or more intranets, local area networks, wide area networks, storage area networks, etc., or suitable combinations thereof. A memory controller can control access to the memory by other components of the device, such as the CPU and peripheral interfaces.

[0155] In some embodiments, the processing device 21 includes one or more processors. The processing device 21 is operatively capable of performing data read and write operations with the storage device 20. The processing device 21 includes one or more general-purpose microprocessors, one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), one or more field-programmable gate arrays (FPGAs), or any combination thereof.

[0156] In some embodiments, the communication interface 22 includes at least one interface unit, each interface unit being used to output a visual interface, receive human-computer interaction events generated according to the operation of a technician, etc. For example, the communication interface 22 includes, but is not limited to, serial interfaces such as HDMI interfaces or USB interfaces, or parallel interfaces, etc. In one embodiment, the communication interface 22 further includes a network communication unit, which is a device for data transmission using wired or wireless networks, examples of which include, but are not limited to, integrated circuits including network cards, local area network modules such as WiFi modules or Bluetooth modules, and wide area network modules such as mobile networks, etc.

[0157] This application also provides a computer-readable storage medium storing at least one program that, when invoked and executed by a computer's processor, implements the support and reinforcement method for a 3D printed model as described in any of the above embodiments.

[0158] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the 3D printing model support and reinforcement method in any of the above embodiments.

[0159] Therefore, the above-mentioned programs, media, and program products can all be regarded as computer implementation carriers of the support and reinforcement method of the 3D printing model of the present invention, which can realize the integrated processing of three-dimensional modeling, printing simulation, support generation, and lattice filling on the same platform.

[0160] If the method is implemented as a software functional unit and sold or used as an independent product, it 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 part 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 enable a computer device with the storage medium installed to execute all or part of the steps of the methods described in the various embodiments of this application.

[0161] In the embodiments provided in this application, the provided computer storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Additionally, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. However, it should be understood that computer storage medium and data storage medium do not include connections, carrier waves, signals, or other transient media, but are intended for non-transient, tangible storage media. The disks and optical discs used in the application include compact discs (CDs), laser discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs. Among these, disks typically copy data magnetically, while optical discs use lasers to copy data optically.

[0162] In summary, this application discloses a method and system for supporting and reinforcing a 3D printed model, as well as a computer device, medium, and computer program product. The method for supporting and reinforcing a 3D printed model involves biasing the region corresponding to the support structure constructed from voxels of the 3D printed model, then determining support points located within the biased region on the surface of the 3D printed model, and connecting each determined support point to a support to be reinforced based on the support structure. This enables a reliable connection between the support and the 3D printed model in the reinforced model, improves the reliability of the support structure, ensures sufficient load-bearing capacity of the support, and avoids problems such as support instability and forming defects.

[0163] Furthermore, this application can automatically generate the support structure of the 3D printed model and automatically obtain a lightweight and highly stable support structure. In the process of automatically generating the support structure of the 3D printed model, this application performs a printing process simulation on the 3D printed model in the same software platform and obtains the simulation results in real time during the simulation. When the current simulation result meets the preset trigger conditions, the generation calculation of the support structure is automatically triggered, thereby realizing the dynamic linkage between the support generation and the simulation process. In the generation calculation of the support structure, this application adopts a topology optimization algorithm with the goal of minimizing flexibility or maximizing stiffness, and applies size constraints, upper surface constraints, and spatial constraints in the support generation area, thereby automatically generating a support structure that optimizes the structural stiffness of the 3D printed model while satisfying printability.

[0164] Furthermore, the support to be reinforced in this application is determined based on an automatically generated support structure constructed from voxels. Compared to the existing technology that relies on human experience or offline simulation followed by manual adjustment of the support, this application, in the process of generating the support structure, not only monitors the simulation results in real time during the simulation process and triggers support generation based on the simulation results, but also accurately determines the area in the model that actually needs support, avoiding the over-support or under-support problems that occur in traditional manual support methods. Moreover, this application automatically performs topology optimization calculations when the triggering conditions are met, and can quickly obtain a support structure that meets the stiffness requirements without manual intervention, significantly improving the efficiency of support design. In particular, by introducing dimensional constraints, upper surface constraints, and spatial constraints, the support generation process simultaneously considers print manufacturability and post-processing removability, thereby achieving the optimal balance between structural strength and material utilization. In short, this application, by introducing a real-time triggering mechanism and topology optimization algorithm in an integrated 3D design and simulation environment, realizes the automation, intelligence, and verifiability of support structure generation, which not only effectively improves the efficiency and accuracy of support design, but also significantly enhances the forming quality and overall stability of printed parts.

[0165] Finally, after obtaining the reinforced model, in order to further ensure the stability of the support, this application also performs 3D printing process simulation on the reinforced model to optimize the wall thickness of the support or add auxiliary supports. The added auxiliary supports meet the processing requirements, so there is no need to perform further processing on the added auxiliary supports.

[0166] The above embodiments are merely illustrative of the inventive essence and beneficial effects of this application, and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the principles and scope of this application. Therefore, all equivalent modifications or alterations achieved by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A support reinforcement method of a 3D printed model, characterized by, The method comprises the following steps: acquiring a model to be reinforced, wherein the model to be reinforced comprises a 3D printing model of a target part and a support to be reinforced of the 3D printing model, and the support to be reinforced is determined based on a support structure constructed by voxels of the 3D printing model; biasing a region corresponding to the support structure based on a biasing parameter to obtain a biased region intersecting the 3D printing model; determining support points on a surface of the 3D printing model in the biased region, and connecting each of the determined support points to the support to be reinforced to obtain a reinforced model.

2. The support reinforcement method of a 3D printed model according to claim 1, wherein, The step of acquiring a model to be reinforced comprises the step of determining the support to be reinforced by using a support structure constructed by voxels of the 3D printing model.

3. The support reinforcement method of a 3D printed model according to claim 2, characterized by, The step of determining the support to be reinforced by using a support structure constructed by voxels of the 3D printing model comprises the step of taking the support structure constructed by voxels as the support to be reinforced.

4. The support reinforcement method of a 3D printed model according to claim 2, characterized by, The step of determining the support to be reinforced by using a support structure constructed by voxels of the 3D printing model comprises the step of performing basic unit filling in a region corresponding to the support structure constructed by voxels to take a lattice support structure obtained by filling as the support to be reinforced.

5. The support reinforcement method of a 3D printed model according to claim 4, characterized by, The basic unit is a rod-shaped lattice.

6. The support reinforcement method of a 3D printed model according to claim 1, wherein, The 3D printing model is a model constructed by face sheet units, and the face sheet unit is a triangular face sheet unit.

7. The support reinforcement method of a 3D printed model according to claim 1, characterized by, The method further comprises the following steps: The step of performing topology optimization in the support generation region of the 3D printing model to obtain the support structure constructed by voxels.

8. The support reinforcement method of a 3D printed model according to claim 7, wherein, The step of performing topology optimization in the support generation region of the 3D printing model to obtain the support structure constructed by voxels comprises the following steps: acquiring a 3D printing model and performing a 3D printing process simulation to obtain a current simulation result of the 3D printing model in real time; when the current simulation result satisfies a preset triggering condition, triggering generation calculation of the support structure; wherein the generation calculation of the support structure comprises the following steps: performing topology optimization in a support generation region according to a preset size constraint or / and an upper surface constraint, taking the minimum flexibility or the maximum rigidity of the 3D printing model as a target, to generate a support structure constructed by voxels so as to optimize the structural rigidity of the 3D printing model.

9. The support reinforcement method of a 3D printed model according to claim 8, wherein, The preset triggering condition is that a maximum deformation value in the current simulation result is above a preset maximum deformation.

10. The support reinforcement method of a 3D printed model according to claim 8, wherein, The preset triggering condition comprises a first triggering condition and a second triggering condition, and the current simulation result is determined to satisfy the preset triggering condition when the current simulation result satisfies any one of the first triggering condition and the second triggering condition; wherein the first triggering condition is that the maximum deformation value in the current simulation result is above the preset maximum deformation; and the second triggering condition is that a minimum deformation value in the current simulation result is above a preset minimum deformation and the whole 3D printing model is in a suspended state.

11. The support reinforcement method of a 3D printed model according to claim 10, wherein, The generated support structure is generated when a second trigger condition is met, and the support reinforcement method further comprises: performing a 3D printing process simulation on a structure including the generated support structure and the 3D printing model to obtain a current simulation result in real time, and continuing the step of topology optimization when a maximum deformation in the current simulation result is greater than a preset maximum deformation.

12. The support reinforcement method of a 3D printed model according to claim 8, characterized by, Further comprising: A step of stopping the 3D printing process simulation during the topology optimization to release computing resources.

13. The support reinforcement method of a 3D printed model according to claim 8, wherein, The size constraint is that a ratio of a size of the support structure to a size of the support generation area is less than a preset ratio.

14. The support reinforcement method of a 3D printed model according to claim 8, wherein, The upper surface constraint is that generation of support on an upper surface of the 3D printing model is prohibited.

15. The support reinforcement method of a 3D printed model according to claim 8, wherein, The step of topology optimization in the support generation area according to the preset size constraint or / and the upper surface constraint, aiming at minimizing the flexibility or maximizing the stiffness of the 3D printing model, to generate the support structure to optimize the stiffness of the structure of the 3D printing model, comprises: Determining a bounding box capable of enclosing the 3D printing model based on the 3D printing model; Determining the support generation area using the bounding box; In the support generation area, topology optimization is performed according to the preset size constraint or / and the upper surface constraint, aiming at minimizing the flexibility or maximizing the stiffness of the 3D printing model, to generate the support structure to optimize the stiffness of the structure of the 3D printing model.

16. The support reinforcement method of a 3D printed model according to claim 1, wherein, The bias parameter is preset or input by a user in a bias parameter input interface.

17. The support reinforcement method of a 3D printed model according to claim 1, wherein, The bias parameter has a value range of 1 to 3 times the size of a voxel.

18. The support reinforcement method of a 3D printed model according to claim 1, wherein, The step of determining the support points in the biased area on the surface of the 3D printing model comprises: Sampling on the surface of the 3D printing model according to preset support point sampling parameters to obtain a plurality of support points on the surface of the 3D printing model; Based on the plurality of determined support points and the biased area, determining the support points in the biased area.

19. The support reinforcement method of a 3D printed model according to claim 1 or 18, characterized by, The surface is a lower surface of the 3D printing model.

20. The support reinforcement method of a 3D printed model according to claim 18, wherein, The preset support point sampling parameters include a critical angle and / or a minimum support height.

21. The support reinforcement method of a 3D printed model according to claim 1, wherein, Further comprising: A step of determining a reinforcement support for each support point to connect each support point to the support to be reinforced by the corresponding reinforcement support.

22. The support reinforcement method of a 3D printed model according to claim 21, wherein, The step of determining a reinforcement support for a support point comprises: determining the nearest position on the support to be reinforced to the support point and establishing a reinforcement support between the support point and the nearest position that satisfies the support angle constraint.

23. The support reinforcement method of a 3D printed model according to claim 21, wherein, The support to be reinforced is a lattice support structure, and the support reinforcement method of the 3D printing model further comprises: based on the simulation result of the reinforced model, optimizing the wall thickness of the support to be reinforced or / and the reinforcement support to obtain an optimized model.

24. The support reinforcement method of a 3D printed model according to claim 1, wherein, The support reinforcement method of the 3D printing model further comprises: Performing a 3D printing process simulation on the reinforced model to obtain a simulation result of the reinforced model; Based on the simulation result of the reinforced model, adding an auxiliary support that satisfies the support angle constraint to the 3D printing model.

25. The support reinforcement method of a 3D printed model according to claim 22 or 24, characterized by, The support angle constraint is that an included angle between the support and a horizontal plane is greater than a preset angle.

26. A support reinforcement system for a 3D printed model, characterized in that, The support reinforcement system of the 3D printing model is used to perform the support reinforcement method of the 3D printing model as claimed in any one of claims 1 to 25, and the support reinforcement system of the 3D printing model comprises: An acquisition module is configured to acquire a model to be reinforced, wherein the model to be reinforced comprises a 3D printing model of a target part and a support to be reinforced of the 3D printing model, and the support to be reinforced is determined based on a support structure constructed by voxels of the 3D printing model; A biasing module is configured to bias a region corresponding to the support structure based on a biasing parameter to obtain a biased region intersecting the 3D printing model; A reinforcement module is configured to determine support points on a surface of the 3D printing model located in the biased region, and connect each determined support point to the support to be reinforced to obtain a reinforced model.

27. A computer device, comprising: comprise: a storage device configured to store at least one program; a processing device connected to the storage device and configured to invoke the at least one program from the storage device and implement the support reinforcement method of the 3D printing model as claimed in any one of claims 1 to 25 when executed.

28. A computer-readable storage medium, characterized in that, at least one program is stored, and the at least one program is invoked and executed by a processor of a computer to implement the support reinforcement method of the 3D printing model as claimed in any one of claims 1 to 25.

29. A computer program product, characterised in that, When the computer program product runs on the computer, the computer is caused to perform the support reinforcement method of the 3D printing model as claimed in any one of claims 1 to 25.

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