Adaptive support structure generation method for top-down stereolithography 3D printing

Abstract

This invention provides an adaptive support structure generation method for top-down stereolithography 3D printing. This method incorporates external forces as constraints into the support generation process, optimizing the number and placement of supports. Layers with similar characteristics are clustered together to significantly reduce the time and effort required to generate supports and the number of simulations. This method comprehensively considers multiple factors, including model geometry, material properties, printing direction, and external force distribution, to generate the optimal support structure. This method ensures successful printing while minimizing support usage, thereby improving the efficiency and quality of 3D printing.

Description

Technical Field

[0001] The present invention belongs to the field of additive manufacturing, and in particular relates to a method for generating an adaptive support structure for top-pull light-curing 3D printing. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of additive manufacturing, 3D printing has become a revolutionary technology in the manufacturing industry. This technology enables designers to quickly create complex CAD models that are difficult to achieve using traditional methods. Stereolithography 3D printing, in particular, leads the way in material diversity, manufacturing precision, and speed, capable of printing a wide range of materials, including elastic materials, ceramics, rigid materials, and metals.

[0004] However, the widespread adoption of 3D printing technology faces a key challenge: the generation of adaptive support structures. Support structures are crucial for the successful printing of 3D models. They not only connect the part being printed to the build platform but also need to resist external forces that could cause printing failure. In top-down, stereolithography 3D printing, release force is a crucial factor that cannot be ignored. However, existing mechanically guided support optimization methods primarily focus on gravity or residual stress, lacking support optimization methods specifically targeting this release force.

[0005] The design and optimization of support structures face two major constraints: external force constraints and overhang constraints. When external forces exceed the support structure's load-bearing capacity, they can break or deform, leading to printing failure. Furthermore, if overhanging areas are not properly supported, they may sag or collapse during printing. Therefore, developing an algorithm that can minimize the number of support structures while satisfying these two constraints has become an important research direction in the development of 3D printing technology.

[0006] Currently, designers often need to perform extensive manual adjustments to ensure the effectiveness of support structures. However, even after these adjustments, the support structures still pose a risk of breaking during the actual printing process, potentially leading to serious part defects or even the failure of the entire print job. To increase the success rate of printing, designers often add excessive support structures. However, this practice not only increases the complexity and time cost of post-processing, but also may damage the surface of the printed object during removal, affecting the quality and aesthetics of the final product. Summary of the Invention

[0007] To address the above-mentioned issues, the present invention proposes an adaptive support structure generation method for top-down light-curing 3D printing. This method comprehensively considers multiple factors, including model geometry, material properties, printing direction, and external force distribution, to generate an optimal support structure. This method can minimize the use of support structures while ensuring successful printing, thereby improving the efficiency and quality of 3D printing.

[0008] According to some embodiments, the present invention adopts the following technical solutions:

[0009] A method for generating an adaptive support structure for top-down stereolithography 3D printing, comprising the following steps:

[0010] Build a 3D model, slice the 3D model to obtain a series of 2D cross-sectional images, preprocess them, cluster the preprocessed slice images, add constraints and select representative layers;

[0011] According to the position of the representative layer, the 3D model with support is segmented and mechanical simulation is performed to determine the stress field area and the force conditions of each support point of the segmented model;

[0012] The strong overhang area and the weak overhang area are distinguished based on the stress field obtained from the mechanical simulation;

[0013] According to the stress conditions of each support point, the stress of the support point is converted into the stress field of the overhanging area, and each support point is optimized to obtain the optimized solution.

[0014] As an optional implementation, the process of preprocessing the slice image includes: downsampling the original slice image, binarizing the downsampled image to convert it into an image containing only black and white colors, and normalizing the binarized image to ensure that all pixel values ​​in the image are strictly 0 or 1.

[0015] As an optional implementation, the preprocessed slice images are clustered. The process of adding constraints includes: clustering the preprocessed slice images using a clustering algorithm, using relative distance as a similarity measure in the clustering process, introducing a continuity constraint, determining a distance threshold, and clustering layer by layer starting from the first layer. For each layer, the distance between it and the last layer in the current class is calculated. If the distance is less than the distance threshold, the layer is added to the current class; otherwise, a new class is started; and the layer-by-layer iterative clustering process is repeated until all layers are classified.

[0016] As a further embodiment, the process of introducing the continuity constraint is that if the image indices between two classes are discontinuous, the distance between them is defined as being greater than a preset distance threshold;

[0017] The process of determining the distance threshold is to analyze the distance distribution between the slice images, calculate the mean and standard deviation of the distance, and adaptively determine the optimal distance threshold.

[0018] As an optional implementation, the process of selecting a representative layer includes using a prediction model based on a multi-layer perceptron to predict the Z-direction pulling force required to destroy the vacuum force of the layer based on the processed slice image, calculating the total external force of each layer, and selecting the layer with the largest force as the representative layer of this class.

[0019] As an optional implementation, voxel grids are used to divide the model during mechanical simulation.

[0020] As an optional implementation method, the process of distinguishing strong overhang areas from weak overhang areas based on the stress field obtained from mechanical simulation includes: setting a stress threshold, distinguishing strong overhang areas from weak overhang areas based on the comparison between the stress threshold and the force magnitude, and for areas where the stress value exceeds the predetermined threshold, it is a strong overhang area, and the support points are increased; for areas where the stress value is less than the predetermined threshold, it is a weak overhang area, and the support points are reduced.

[0021] As a further embodiment, a transition zone is provided between the strong and weak overhanging areas to gradually change the support density.

[0022] As an optional implementation method, the process of converting the force of the support point into the force field of the overhanging area according to the force conditions of each support point includes selecting the maximum force encountered by each support point in all slice models as the force of the support point, and using the Gaussian distribution formula to convert the force of the support point into the force field of the overhanging area. During the conversion process, the expansion coefficient of the Gaussian field is adaptively adjusted to map the support force of each support point.

[0023] As an optional implementation method, the process of optimizing each support point includes: constructing a total energy function: E total =E f +λ k E o , the total energy function is minimized by the gradient descent method;

[0024] Among them E f Represents the energy caused by force, E o represents the energy term for overhang constraint optimization, E f It is used to optimize the position of the support points so that the forces borne by all support points are as similar as possible, thereby minimizing the number of support points. o The optimization goal is to ensure that the support point is the center of the circle and the self-support The circle with a radius of λ can completely cover the strong overhang area, and the calculation process is to calculate the sum of the product of the farthest distance from the support point and its weight in the Voronoi area of ​​each support point;k is a decreasing coefficient, which decreases gradually with the increase of the number of iterations.

[0025] As an alternative embodiment,

[0026] Where χ(p) represents the density at point p

[0027]

[0028] When the distance is less than the maximum overhang distance d self-support When , it indicates that the support of the current area meets the overhang constraint, ω i If set to 0, the area will be optimized only according to the force constraint, and the distribution of support points will not be affected by the overhang constraint. Otherwise, it is set to 1, where P is infinite, that is, the infinite norm is taken for ||p-pi||.

[0029] As an optional implementation, a minimum spanning tree algorithm is used to construct the support structure, and support points whose distance is less than a predetermined value are connected by trusses.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention innovatively proposes an adaptive support structure generation method for top-down light-curing 3D printing, which can automatically optimize the number and distribution of supports according to external force requirements, realize the automation of support structure generation, and significantly improve the efficiency and quality of 3D printing.

[0032] The present invention achieves a significant reduction in the number of simulations (by at least 1-2 orders of magnitude, by comparison) through an innovative clustering method, greatly shortens the time required for simulation, and improves the efficiency of support structure generation.

[0033] This invention introduces the concept of weak overhang areas and satisfies overhang and force constraints by optimizing the number and layout of supports, thereby achieving adaptive adjustment of the support structure, reducing the number of required supports, minimizing damage to the model surface, and reducing manual post-processing time, significantly improving the printing success rate and model quality.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1It is a schematic diagram of the process of distinguishing strong overhang areas from weak overhang areas;

[0037] Figure 2 This is a schematic diagram of adding additional supports in the weak overhang area;

[0038] Figure 3 is an overall flow chart of an embodiment;

[0039] Figure 4 is a schematic diagram of a maximum overhang distance and maximum overhang angle experiment according to an embodiment;

[0040] Figure 5 is a schematic diagram of boundary conditions of a simulation of an embodiment;

[0041] Figure 6 is an iterative force result diagram of an embodiment;

[0042] Figure 7 This is a comparison of the printing results of an embodiment with that of Materialise Magics e-Stage when the overhang angle is set to 30°. DETAILED DESCRIPTION

[0043] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0045] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0046] In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0047] As described in the background, the present invention proposes an adaptive support structure generation method for top-down stereolithography 3D printing, aiming to solve the following key technical problems:

[0048] (1) Minimize the impact of the support structure on the model surface while satisfying the force constraints and overhang constraints.

[0049] (2) Reduce the computational complexity and time cost of support structure generation.

[0050] (3) Optimize the distribution of support structures to adapt to the geometric characteristics and stress conditions of complex models.

[0051] (4) Solve the problem of insufficient support caused by the overly strict definition of the overhanging area in traditional methods.

[0052] Specifically, the adaptive support structure generation method for top-down stereolithography 3D printing incorporates external forces as constraints into the support generation process, thereby optimizing the number and placement of supports. Furthermore, an efficient clustering method is employed to group layers with similar characteristics together, significantly reducing the time cost of support generation and the number of simulations.

[0053] like Figure 3 As shown, the specific steps include:

[0054] Step (1): construct a 3D model, slice the 3D model to obtain a series of 2D cross-sectional images, preprocess them, cluster the preprocessed slice images, add constraints and select representative layers;

[0055] Step (2): According to the position of the representative layer, the 3D model with added support is segmented and mechanical simulation is performed to determine the stress field area and the force condition of each support point of the segmented model;

[0056] Step (3): distinguishing strong overhang areas from weak overhang areas based on the stress field obtained from mechanical simulation;

[0057] Step (4): According to the force conditions of each support point, the force of the support point is converted into the force field of the overhanging area, and each support point is optimized to obtain an optimized solution.

[0058] The details of each step are as follows:

[0059] Step (1) mainly includes the following steps:

[0060] The main goal of this step is to reduce the number of layers required for mechanical simulation by clustering, while ensuring that the selected representative layers can accurately reflect the mechanical properties of the entire model. It specifically includes the following sub-steps:

[0061] Step (1-1): Model slicing and clustering preprocessing:

[0062] First, the 3D model is sliced ​​along the printing direction to obtain a series of two-dimensional cross-sectional images. In order to improve the efficiency and accuracy of subsequent clustering, these slice images need to be preprocessed:

[0063] a) Downsampling: Downsample the original slice image to reduce its resolution. This step can significantly reduce the number of pixels in each image, thereby reducing the dimensionality of the feature vector used for clustering. The degree of downsampling needs to strike a balance between preserving sufficient detail and reducing the amount of computation required.

[0064] b) Binarization: The downsampled image is binarized to convert it into an image consisting of only black and white. This step can further reduce the complexity of the information while preserving the key geometric features of the model cross section.

[0065] c) Normalization: Normalize the binarized image to ensure that all pixel values ​​in the image are strictly 0 or 1. This step can eliminate small numerical errors that may be introduced during image processing and provide a consistent basis for subsequent distance calculations.

[0066] Step (1-2): Adaptive clustering:

[0067] Clustering algorithm is used to cluster the preprocessed slice images. The key to this step is to define a suitable distance function and automatically determine the distance threshold:

[0068] a) Relative distance function: In order to adapt to both large and small cross-sections that may exist in the model, we use relative distance as the similarity metric. The definition of relative distance is shown in formula (1):

[0069]

[0070] d(i,j) represents the Euclidean distance between image i and image j, and |i| represents the number of non-zero pixels in image i. This definition of relative distance can effectively handle cross-sections of different scales, making the clustering process more robust to changes in the overall shape of the model.

[0071] b) Continuity Constraint: To ensure the continuity of slice image indices within each class, a continuity constraint is introduced during the clustering process. Specifically, if the image indices between two classes are discontinuous, the distance between them is defined as greater than a preset distance threshold. This prevents non-adjacent images from being merged into the same class, ensuring that images within each class are continuous.

[0072] c) Automatic threshold determination: The method proposed by Rahmah et al. (Rahmah N, Sitanggang IS. Determination of optimal epsilon (eps) value on dbscan algorithm to clustering data on peatland hotspots in sumatra [C] / / IOP conference series: earth and environmental science. IoP Publishing, 2016, 31 (1): 01-2012) was used to automatically calculate the clustering distance threshold ∈ d This method analyzes the distance distribution between slice images, calculates the mean and standard deviation of the distance, and then uses an adaptive formula to determine the optimal distance threshold. This method can automatically adjust the strictness of clustering according to the characteristics of different models, improving the adaptability of the algorithm.

[0073] d) Iterative clustering process: Starting from the first layer, clustering is performed layer by layer. For each layer, the distance between it and the last layer in the current class is calculated. If the distance is less than a threshold ∈ d , then add the layer to the current class; otherwise, start a new class. This process continues until all layers are classified.

[0074] Steps (1-3): Select the representative layer:

[0075] In each cluster, a representative layer needs to be selected for subsequent mechanical simulation. The principle of selecting the representative layer is to ensure that it can represent the most unfavorable mechanical situation in the cluster:

[0076] a) Calculate external force: It mainly consists of two parts: gravity and release force.

[0077] Gravity calculations are relatively simple and can be directly calculated based on the volume and material density of each layer.

[0078] The release force is calculated using a prediction model based on a multi-layer perceptron (MLP). This model uses a slice image as input and predicts the Z-direction pulling force required to break the vacuum layer. This approach significantly improves computational efficiency compared to direct simulation using the 3D model.

[0079] b) Select the layer with the largest stress: After calculating the total external force (gravity + release force) of each layer, select the layer with the largest stress as the representative layer of this type. This ensures that the support structure is designed under the most unfavorable conditions.

[0080] In this embodiment, step (2) mainly includes the following steps:

[0081] Step (2-1): By clustering similar layers and selecting representative layers, we determine the specific slices that need to be simulated. Based on the location of the representative layers, the model with added supports is sliced ​​and simulated;

[0082] Step (2-2): Dynamic simulation is used based on the simulation results to improve the accuracy and reliability of the simulation, so as to better simulate the dynamic behavior in the actual production process and ensure that the simulation results can accurately reflect the stress and deformation in actual operation.

[0083] In this embodiment, the boundary conditions of the simulation are as follows: Figure 5 As shown, a uniform velocity in the positive direction of the Z axis is applied to the top end point of the support, while the bottom surface of the PDMS material of the box is fixed.

[0084] Steps (2-3): While adaptive meshing provides more refined mesh management, adding support structures to the segmented model can introduce self-intersection issues, rendering the mesh non-manifold. To address this issue and ensure simulation stability and efficiency, we used voxel meshing to segment the model.

[0085] a) The use of voxel grids is due to their fast meshing speed, which can greatly improve simulation efficiency.

[0086] b) When iteratively refining the mesh in support structures and key areas, there is no need to re-mesh the entire model, which simplifies the mesh update process.

[0087] The step (3) mainly comprises the following steps:

[0088] In this step, the strong overhang area and the weak overhang area are distinguished based on the previously calculated stress field. Figure 1 This process is crucial for subsequent support optimization because it determines the support strength and density required in different areas. The following is a detailed implementation method:

[0089] Strong overhang areas are those with significant overhangs that necessitate the addition of support structures and the need to meet overhang constraints to prevent print failure. These areas are defined similarly to overhang areas in traditional methods; these areas typically have a very shallow angle with the build platform or overhangs that exceed the material's self-supporting capacity.

[0090] Weak overhang areas are those areas where the support structure can be expanded. When the support structure of the strong overhang area is not sufficient to meet the force constraints, additional supports can be added in the weak overhang area to improve the overall strength and stability of the support structure, such as Figure 2 as shown in (a) and (b).

[0091] It is worth noting that the weak overhang regions do not need to satisfy the overhang constraints because their main role is to strengthen the supporting structure rather than prevent the collapse of the overhang region.

[0092] Step (3-1): Determine a stress threshold to distinguish strong and weak overhang areas.

[0093] a) For areas where the force exceeds a predetermined threshold, add support points to prevent fracture;

[0094] b) For areas with less stress, reduce the number of support points to save material and reduce damage to the model surface.

[0095] Step (3-2): Divide the model surface into strong and weak overhang areas:

[0096] a) Strong overhang areas: areas where stress values ​​are higher than the threshold, these areas require stronger support.

[0097] b) Weak overhang areas: areas where stress values ​​are below the threshold, these areas can use lighter supports or no support.

[0098] c) Transition zone: A transition zone is set between the strong and weak overhang areas to make the support density gradually change and avoid sudden changes.

[0099] The step (4) mainly comprises the following steps:

[0100] In this step, based on the previous analysis results, the support structure is fully optimized to balance the force requirements and overhang constraints. This process is the core of the algorithm and directly affects the final printing quality and efficiency. The following is a detailed optimization strategy:

[0101] Step (4-1): The force applied to each support point of each sliced ​​model is obtained through simulation analysis. To ensure the integrity of the support structure during the entire printing process, the maximum force applied to each support point across all sliced ​​models is selected as the force applied to that support point.

[0102] Step (4-2): After obtaining the maximum force of each support point, convert the support force into the force field of the overhanging area to further optimize the position and number of supports.

[0103] a) Use the Gaussian distribution formula to transform the force at the support point into the force field in the overhanging area, as shown in formula (2):

[0104]

[0105] Among them F si is the force on the ith support, where μ xi is the x value of the support point, μ yi is the y value of the support point, is the expansion coefficient of the support force of the i-th support mapped to the Gaussian field, σ x Represents the standard deviation of the Gaussian distribution in the x direction, σ y represents the standard deviation of the Gaussian distribution in the y direction, d self-support Represents the self-supporting distance, π is the pi constant. Adaptively adjust The value of can be used to expand the Gaussian field range of the support map. In this way, the nearby weak overhang area can be included in the force field, as shown in formula (3):

[0106]

[0107] Among them F break is the stress threshold for support fracture.

[0108] b) An innovative total energy function is proposed to optimize the distribution of support points, which is defined as shown in formula (4):

[0109] E total =λ k E f +E o (4)

[0110] Among them E f Represents the energy caused by force, E o represents the energy term for overhang constraint optimization. E f The purpose is to optimize the position of the support points so that the forces borne by all support points are as similar as possible, thereby minimizing the number of support points. The specific expression is shown in formula (5):

[0111]

[0112] Where χ(p) represents the density at point p; E o The optimization goal is to ensure that the support point is the center of the circle and the d self-support The circle with a radius of can completely cover the strong overhang area, which is specifically expressed as shown in formula (6):

[0113]

[0114] The energy term is calculated in the Voronoi region of each support point, the maximum distance from the support point and its weight ω i The sum of the products of . Figure 4 As shown, when the distance is less than the maximum overhang distance d self-support When , it indicates that the support of the current area satisfies the overhang constraint, so ω iSet to 0, so that the area will be optimized only according to the force constraint, and the distribution of support points will not be affected by the overhang constraint. Where P is infinite, that is, the infinite norm of ||p-pi|| is taken, ω i It is expressed as shown in formula (7):

[0115]

[0116] In order to ensure the dynamic balance of force constraints and overhang constraints during the optimization process, a decreasing coefficient λ is introduced. k , as shown in formula (8):

[0117] λ k =λ k-1 *θ k (8)

[0118] where θ k The value of is less than 1, ensuring that λ k It will gradually decrease with the number of iterations. This ensures that the force field constraints are the primary optimization objective in the initial stages, helping to determine reasonable initial support positions based on the force distribution. In the later stages, for areas where overhang constraints are not met, the optimization focus shifts to overhang constraints.

[0119] Step (4-3): Minimize the energy by gradient descent. Use the minimum spanning tree algorithm to build the support structure, and connect the support points with close distances through trusses to enhance the overall stability of the structure and prevent breakage caused by mechanical vibration during the printing process.

[0120] In order to reduce the contact area between the model and the support, the cross-sectional area of ​​the contact area is set to be smaller than the cross-sectional area of ​​the other parts of the support.

[0121] As an implementation, Figure 6 The top image shows the number of supports, support force distribution, maximum force, and 3D support results for the dragon model during the iteration process. The bottom image shows the number of supports and maximum force for the cat, armadillo, sofa, and semi-hollow skeleton models. In the image, the black line indicates the maximum force during the iteration process, and the blue line indicates the number of supports. The specific values ​​are indicated in black and blue fonts, respectively. The red dashed line represents the maximum force threshold of 1.2N.

[0122] Figure 7This figure compares the print results using this method with those using Materialise Magics e-Stage with an overhang angle set to 30°. The Materialise Magics e-Stage-generated supports show rendering and printing results. Only the hollowed-out cat model printed successfully; the supports on the armadillo model broke during printing. The black dashed box shows a magnified image of the broken supports on the armadillo model. The figure on the right shows the rendering and printing results using the adaptive supports of the method proposed in this example. All models printed successfully.

[0123] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made by those skilled in the art that fall within the spirit and principles of the present invention and do not require creative effort are intended to be within the scope of protection of the present invention.

Claims

1. A method for generating an adaptive support structure for top-down light-curing 3D printing, characterized in that: The following steps are involved: Build a 3D model, slice the 3D model to obtain a series of 2D cross-sectional images, preprocess them, cluster the preprocessed slice images, add constraints and select representative layers; According to the position of the representative layer, the 3D model with support is segmented and mechanical simulation is performed to determine the stress field area and the force conditions of each support point of the segmented model; The strong overhang area and the weak overhang area are distinguished based on the stress field obtained from the mechanical simulation; According to the stress conditions of each support point, the stress of the support point is converted into the stress field of the overhanging area, and each support point is optimized to obtain the optimized solution; The process of optimizing each support point includes: constructing a total energy function: E total =E f +λ k E o , the total energy function is minimized by the gradient descent method; Among them E f Represents the energy caused by force, E o represents the energy term for overhang constraint optimization, E f It is used to optimize the position of the support points so that the forces borne by all support points are as similar as possible, thereby minimizing the number of support points. o The optimization goal is to ensure that the support point is the center of the circle and the self-support The circle with a radius of λ can completely cover the strong overhang area, and the calculation process is to calculate the sum of the product of the farthest distance from the support point and its weight in the Voronoi area of ​​each support point; k is a decreasing coefficient, which decreases gradually with the increase of the number of iterations; The method for generating an adaptive support structure for top-down light-curing 3D printing is as follows: Where χ(p) represents the density at point p; When the distance is less than the maximum overhang distance d self-support When , it indicates that the support of the current area meets the overhang constraint, ω i If set to 0, the area will be optimized only according to the force constraint, and the distribution of support points will not be affected by the overhang constraint. Otherwise, it is set to 1, where P is infinite, that is, for ||pp i ||Take the infinity norm.

2. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 1, wherein: The preprocessed slice images are clustered, and the process of adding constraints includes: clustering the preprocessed slice images using a clustering algorithm, using relative distance as a similarity measure in the clustering process, introducing continuity constraints, determining the distance threshold, starting from the first layer, and clustering layer by layer. For each layer, the distance between it and the last layer in the current class is calculated. If the distance is less than the distance threshold, the layer is added to the current class; otherwise, a new class is started; and the iterative clustering process is repeated layer by layer until all layers are classified.

3. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 2, wherein: The process of continuity constraint is that if the image indices between two classes are discontinuous, the distance between them is defined as greater than a preset distance threshold; The process of determining the distance threshold is to analyze the distance distribution between the slice images, calculate the mean and standard deviation of the distance, and adaptively determine the optimal distance threshold.

4. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 1, wherein: The process of selecting the representative layer includes using a prediction model based on a multi-layer perceptron to predict the Z-direction pulling force required to destroy the vacuum force of the layer based on the processed slice images, calculating the total external force of each layer, and selecting the layer with the largest force as the representative layer of this class; using a voxel grid to divide the model when performing mechanical simulation.

5. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 1, wherein: The process of distinguishing strong overhang areas from weak overhang areas based on the stress field obtained from mechanical simulation includes: setting a stress threshold, distinguishing strong overhang areas from weak overhang areas based on the comparison between the stress threshold and the force magnitude, and defining areas with stress values ​​exceeding the predetermined threshold as strong overhang areas and adding support points; and defining areas with stress values ​​less than the predetermined threshold as weak overhang areas and reducing support points.

6. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 1, wherein: A transition zone is set between the strong and weak overhang areas to make the support density gradually change.

7. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 1, wherein: According to the force conditions of each support point, the process of converting the force of the support point into the force field of the overhang area includes selecting the maximum force encountered by each support point in all slice models as the force of the support point, and using the Gaussian distribution formula to convert the force of the support point into the force field of the overhang area. In the conversion process, the expansion coefficient of the support force mapping of each support point into the Gaussian field is adaptively adjusted.

8. The method for generating an adaptive support structure for top-down stereolithography 3D printing according to claim 1, wherein: The minimum spanning tree algorithm is used to construct the support structure, and the support points with a distance less than a predetermined value are connected by trusses.

Images