An intelligent light-cured 3D printing model optimization method

Abstract

The present application belongs to the technical field of 3D printing, and specifically relates to an intelligent light-curing 3D printing model optimization method, which covers the whole process from data representation of three-dimensional design form to final printing configuration selection. First, based on data representation analysis of spatial distribution characteristics, internal and surface fragile areas are identified, and customized structural reinforcement processing is implemented. Then, according to the reinforcement processing, the wall thickness parameter is adjusted, and the support structure is corrected to adapt to the new requirements. Next, the corrected support structure is used for virtual printing process simulation to evaluate the influence of the optimization measures on the printing result. Finally, based on the evaluation result, the optimal configuration is selected for manufacturing. This method enhances the weak areas of the model through automatic analysis and customized strategies, dynamically adjusts the wall thickness and support, improves the printing precision and reliability, reduces material waste, shortens the preparation time, and thus improves the production efficiency and printing quality.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of 3D printing, and specifically relates to an intelligent light-cured 3D printing model optimization method. BACKGROUND

[0002] Light-cured 3D printing technology has been widely used in rapid prototyping and industrial production fields. However, when printing parts with complex geometrical shapes, due to uneven material distribution and insufficient structural strength, printing failure or poor product quality often occurs. Traditional manual optimization methods are time-consuming and labor-intensive, and it is difficult to provide accurate optimization solutions for each unique design. In addition, the lack of effective tools to predict and simulate potential problems during the printing process makes the optimization process difficult and lacks pertinence.

[0003] How to automatically identify and solve structural weaknesses in 3D printing models while improving printing efficiency and product quality is a major challenge currently faced by light-cured 3D printing. Especially when faced with models with complex internal structures and thin-walled features, how to ensure that these models do not deform or break during the printing process while reducing unnecessary material waste becomes a problem that needs to be solved. SUMMARY

[0004] The purpose of the present application is to provide an intelligent light-cured 3D printing model optimization method, which effectively identifies and enhances weak areas in 3D models through automated analysis and customized optimization strategies, dynamically adjusts wall thickness and support structures to adapt to different printing needs, to solve the problems raised in the background art.

[0005] To achieve the above purpose, the present application adopts the following technical solution: an intelligent light-cured 3D printing model optimization method, comprising the following steps:

[0006] Obtain a data representation of a three-dimensional design form, based on the data representation, analyze the spatial distribution characteristics of the three-dimensional design form using a specific framework;

[0007] According to the spatial distribution characteristics, determine the fragile areas inside and on the surface of the three-dimensional design form, and use customized strategies to strengthen the structure of the determined fragile areas;

[0008] After the structure strengthening process, adjust the wall thickness parameters of the three-dimensional design form according to the strengthening process, and correct the support structure to adapt to the new design requirements in combination with the changes in the wall thickness parameters;

[0009] Using the corrected support structure, perform a virtual printing process simulation, collect information from the virtual printing process simulation, and evaluate the impact of the optimization measures on the printing results;

[0010] Based on the influence assessment, an optimal configuration is selected for the final light-cured three-dimensional build manufacturing.

[0011] Preferably, a data representation of the three-dimensional design morphology is obtained, comprising:

[0012] forming a set consisting of a point cloud wherein each point has coordinates in three-dimensional space ;

[0013] Based on the point cloud set P, a distance matrix D is calculated, wherein quantifies the relative positional relationship between points;

[0014] By analyzing the distance matrix D, the positions in the point cloud representing structural weak links are identified. For the identified weak positions, an enhancement vector E is defined to indicate the areas that need to be strengthened in subsequent operations. The value of the enhancement vector E is determined by the number of neighboring points of the corresponding point, i.e. wherein represents the number of points with a distance less than a predetermined threshold from point , and the function f determines the enhancement degree.

[0015] Preferably, based on the data representation, the spatial distribution characteristics of the three-dimensional design morphology are analyzed using a specific framework, comprising:

[0016] By calculating the volume density ρ of each part in the three-dimensional design morphology, wherein , represents the number of points contained in the neighborhood of point , and V is the volume of the neighborhood.

[0017] Using the volume density ρ, the high-density and low-density areas within the three-dimensional design morphology are determined. A threshold T is set to distinguish different density levels. If , it is marked as a high-density area, otherwise as a low-density area.

[0018] For the identified low-density areas, a density compensation factor C is applied according to the distribution of the surrounding high-density areas to calculate the compensated density , wherein the compensation factor C depends on the ratio of the average density of the adjacent high-density area to its own density.

[0019] Preferably, according to the spatial distribution characteristics, the fragile areas inside and on the surface of the three-dimensional design morphology are determined, comprising:

[0020] By calculating the local curvature K of each point , wherein is the local curvature of point . ​and the distribution of points in its neighborhood;

[0021] Applying the local curvature Identify potential vulnerability on the structure, define the area with curvature exceeding a pre-defined threshold K as vulnerable area, i.e. when >K, mark the point and its neighborhood as vulnerable;

[0022] For the identified vulnerable area, assign a reinforcement factor S according to the stability of its surrounding structure, the reinforcement factor S is determined by the difference between the average curvature of adjacent non-vulnerable area and the curvature of the vulnerable area , the formula is , where the larger the value of S indicates the higher reinforcement demand.

[0023] Preferably, for the identified vulnerable area, adopt a customized strategy for structural reinforcement, including:

[0024] First measure the minimum cross-sectional dimension of the vulnerable area, and record it;

[0025] According to the minimum cross-sectional dimension , calculate the required minimum wall thickness to ensure structural stability, the formula is , where is a pre-defined scaling factor to ensure the wall thickness is sufficient to provide adequate support force;

[0026] Apply the calculated minimum wall thickness to adjust the design of the vulnerable area, add extra material until the required wall thickness is reached or exceeded, thereby enhancing the overall structural strength of the area.

[0027] Preferably, after the structural reinforcement, adjust the wall thickness parameters of the three-dimensional design form according to the reinforcement, including:

[0028] Based on the new wall thickness after reinforcement and the original design wall thickness , calculate the wall thickness increase ratio , the formula is ;

[0029] According to the wall thickness increase ratio , adjust the thickness of all the wall layers connected to the reinforced area to maintain the consistency and stability of the overall design, the adjusted wall layer thickness is ;

[0030] Ensure that adjustments to all associated wall thickness parameters do not cause the design to exceed material or printer physical limitations, and verify the adjusted wall thickness. Is it within the allowable range, i.e., tmin≤ ≤tmax, where tmin and tmax are the minimum and maximum wall thicknesses acceptable to the material and process, respectively.

[0031] Preferably, the support structure is adjusted to adapt to new design requirements based on changes in wall thickness parameters, including:

[0032] First, based on the adjusted wall thickness Calculate the contact area ratio of the supporting structure The formula is ,in To support the actual contact area between the structure and the building, This represents the theoretical contact area.

[0033] Based on the contact area ratio Determine the density adjustment coefficient of the supporting structure. ,like Less than the predetermined threshold Increase the density of the supporting structure if the density is high, and decrease the density if the density is low, adjusting the coefficient accordingly. The calculation formula is ;

[0034] Apply the density adjustment coefficient The support structure is rearranged by increasing or decreasing the number of support points and adjusting their positions.

[0035] Preferably, using the corrected support structure, a virtual printing process simulation is performed, including:

[0036] Determine the printing path based on the final layout of the support structure. , where the path This includes the order of each layer of the structure and its supports;

[0037] During the simulation, according to the path Calculate the printing time for each layer. Using formula ,in V represents the path length of the i-th layer, and V is the printhead moving speed.

[0038] Based on the printing time of each layer Predict the complete printing cycle The formula is ,in This represents the total number of floors. Additional time required for switching between all layers and supporting removal;

[0039] through the entire printing cycle Predict, evaluate the impact of current support structure on printing efficiency, and adjust support design as needed to optimize printing time and quality.

[0040] Preferably, collect information from the virtual printing process simulation, evaluate the impact of optimization measures on printing results, including:

[0041] By recording the deviation of material deposition during the printing process of each layer , the deviation is expressed as , where is the actual amount of material deposited in the i-th layer is the amount of material in the ideal state;

[0042] According to the deviation of material deposition of each layer , calculate the overall printing error , the formula is , where is the weight given according to the importance of the layer, and n is the total number of layers;

[0043] Use the overall printing error to evaluate the effectiveness of the current optimization measures. If is lower than the preset threshold , it is confirmed that the optimization is successful; otherwise, the optimization parameters need to be adjusted until reaches an acceptable level.

[0044] Preferably, based on the impact evaluation, select the optimal configuration for the final light-cured three-dimensional object manufacturing, including:

[0045] According to the overall printing error and the printing cycle , calculate the comprehensive score , the formula is , where and are the weights set according to the quality and time priority;

[0046] According to the comprehensive score , compare the results of different optimization schemes, and select the scheme with the lowest comprehensive score as the optimal configuration;

[0047] Apply the selected optimal configuration to prepare the final printing instruction set, ensure that all optimization measures are correctly implemented in the light-cured three-dimensional object manufacturing process, to achieve the established quality standards and production efficiency targets.

[0048] The technical effects and advantages of the present application: the intelligent light-cured 3D printing model optimization method proposed in the present application has the following advantages compared with the prior art.

[0049] The present application can effectively identify and enhance the weak areas in the 3D model by automatic analysis and customized optimization strategy, dynamically adjust the wall thickness and support structure to adapt to different printing needs. This method not only can significantly improve the accuracy and reliability of the printed parts, but also can improve the production efficiency by reducing material waste and shortening the printing preparation time, so as to bring higher quality printing experience and economic benefits to the light-cured 3D printing technology. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The flowchart of the intelligent light-cured 3D printing model optimization method of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. The specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] The present application provides an intelligent light-cured 3D printing model optimization method, which aims to improve the structural strength and printing quality of 3D printing model through automatic means, as shown in Figure 1 The method comprises the following steps:

[0053] Step 1: Obtain the data representation of the three-dimensional design form, which is extracted from the STL or other format file derived from CAD software. Based on the data representation, analyze the spatial distribution characteristics of the three-dimensional design form using a specific framework;

[0054] Specifically, obtaining the data representation of the three-dimensional design form further comprises:

[0055] Read the three-dimensional design file and convert it into point cloud form to form a set composed of point clouds , where each point has coordinates in three-dimensional space. Converting the three-dimensional design form into point cloud form facilitates subsequent analysis and processing. This process enables the system to handle complex geometric structures and provides a basis for subsequent analysis.

[0056] According to the point cloud set P, calculate the distance matrix D between points, where , to quantify the relative positional relationship between points; by calculating the distance between each point in the point cloud, a distance matrix is formed, enabling the system to quantify the relative positional relationship between points and points. This helps the system better understand the spatial distribution characteristics of the design form.

[0057] By analyzing the distance matrix D, the positions in the point cloud that represent structural weak links are identified, i.e. those point sets with larger spatial gaps or fewer connections. For the identified weak positions, define the enhancement vector E to indicate the areas that need to be strengthened in subsequent operations. The value of the enhancement vector E is determined by the number of neighboring points of the corresponding point, i.e. , where represents the number of points with a distance less than a preset threshold from point , and the function f determines the enhancement degree. By analyzing the distance matrix D, the system can identify the positions in the design form that are structurally weak, and define the enhancement vector E for these positions to indicate the areas that need to be strengthened. The value of the enhancement vector E depends on the number of neighboring points of the corresponding point, thereby achieving targeted structural optimization.

[0058] It can automatically identify potential structural weaknesses in 3D models and provide corresponding optimization suggestions. Through measures such as strengthening structures, adjusting wall thickness, and improving support settings, the system can significantly improve printing accuracy and efficiency, reduce material waste, and provide users with customized optimization solutions, promoting the development of light-cured 3D printing technology to a higher level of intelligence.

[0059] Specifically, based on the data representation, analyzing the spatial distribution characteristics of the three-dimensional design form using a specific framework further comprises:

[0060] By calculating the volume density p of each part in the three-dimensional design form, where , represents the number of points contained in the neighborhood of point , is the volume of the neighborhood; this calculation provides a quantitative description of the internal structural density of the design form.

[0061] Using the volume density p, determine the high-density and low-density areas within the three-dimensional design form. Set a threshold T to distinguish different density levels. If , it is marked as a high-density area, otherwise it is a low-density area; this step helps to identify areas in the design that may have structural instability.

[0062] For the identified low-density areas, apply a density compensation factor C based on the distribution of the surrounding high-density areas to calculate the compensated density The compensation factor C depends on the ratio of the average density of neighboring high-density regions to its own density. This compensation mechanism ensures that low-density regions receive sufficient material filling during printing to enhance their structural stability.

[0063] Through the above technical means, this invention can automatically identify potential structural weaknesses in 3D models and effectively enhance the structural strength of these areas by adjusting density and material distribution. This method not only improves the accuracy and reliability of printed parts but also reduces unnecessary material waste and shortens printing preparation time, thereby improving production efficiency and printing quality, and bringing greater economic benefits to the application of photopolymer 3D printing technology.

[0064] Step 2: Based on the spatial distribution characteristics, identify the vulnerable areas inside and on the surface of the three-dimensional design form, and use a customized strategy to strengthen the structure of the identified vulnerable areas.

[0065] Specifically, based on the aforementioned spatial distribution characteristics, determining the vulnerable areas within and on the surface of the three-dimensional design further includes:

[0066] By calculating each point Local curvature ,in From point The distribution of points within its neighborhood determines the curvature and trend of the model surface; this step helps the system understand the curvature and trend of change of the model surface.

[0067] Using the local curvature Identify potential structural vulnerabilities by defining regions with curvature exceeding a preset critical value K as vulnerable regions. When K >, the marker point The area and its adjacent regions are vulnerable;

[0068] For identified vulnerable areas, a reinforcement factor S is assigned based on the stability of the surrounding structure. The reinforcement factor S is determined by the average curvature of adjacent non-vulnerable areas. With the curvature of the vulnerable region The difference is determined by the calculation formula. The larger the value of S, the higher the reinforcement requirement.

[0069] Through the above technical means, this invention can automatically identify potential structural weaknesses in 3D models and determine which areas need reinforcement through quantitative methods. This method can not only significantly improve the accuracy and reliability of printed parts, but also reduce unnecessary material waste and shorten printing preparation time, thereby improving production efficiency and printing quality, and bringing greater economic benefits to the application of photopolymer 3D printing technology.

[0070] Specifically, for the identified fragile regions, the customized strategy for structural reinforcement further includes:

[0071] First, measure the minimum cross-sectional dimension of the fragile region and record; this is to assess the structural stability of the region during printing. By adjusting the wall thickness of the fragile region, the material density of these regions is increased, thereby enhancing their structural strength and reducing the risk of deformation or breakage during printing.

[0072] According to the minimum cross-sectional dimension , calculate the required minimum wall thickness to ensure structural stability, the formula is , where is a predefined scaling factor to ensure that the wall thickness is sufficient to provide adequate support; by accurately calculating the required wall thickness rather than blindly increasing the material, it avoids excessive use of materials, reduces waste, and also reduces manufacturing costs.

[0073] Apply the calculated minimum wall thickness to adjust the design of the fragile region, add additional material until it reaches or exceeds the required wall thickness , thereby enhancing the overall structural strength of the region.

[0074] The reinforcement ensures the reliability and durability of the printed part under complex geometry, improves the quality of the finished product, and makes the final product more consistent with the design expectations. The automated process reduces the need for manual intervention, simplifies the design process, and allows designers to focus more on creativity rather than tedious optimization work, improving production efficiency. Not only can it significantly improve the structural stability and printing quality of 3D printed parts, but also reduce costs through reasonable material usage strategies, thereby bringing higher economic benefits to the light-cured 3D printing technology.

[0075] Step three: After the structural reinforcement, adjust the wall thickness parameters of the three-dimensional design form according to the reinforcement, and correct the support structure to adapt to the new design requirements in combination with the changes in wall thickness parameters;

[0076] Specifically, after the structural reinforcement, adjusting the wall thickness parameters of the three-dimensional design form according to the reinforcement further includes:

[0077] Based on the new wall thickness after reinforcement and the original design wall thickness , calculate the wall thickness increase ratio , the formula is ; this ratio reflects the degree of wall thickness increase relative to the original design.

[0078] According to the wall thickness increase ratio , adjust the thickness of all wall layers connected to the reinforced area to maintain consistency and stability in the overall design. The adjusted wall layer thickness is ; this step ensures consistency in wall thickness throughout the model, preventing structural imbalance issues caused by local reinforcement.

[0079] Ensure that the adjustment of all associated wall layer thickness parameters does not cause the design to exceed material limitations or printer physical limitations by verifying that the adjusted wall thickness is within the allowed range, i.e., tmin ≤ tmax, where tmin and tmax are the minimum and maximum wall thicknesses acceptable by the material and process, respectively. This ensures the feasibility of the final design and avoids printing failures or material waste caused by unreasonable wall thickness.

[0080] By adjusting the wall layer thickness, the overall consistency and stability of the model after structural reinforcement are ensured, avoiding structural imbalance problems caused by local reinforcement. By verifying that the wall thickness is within the allowed range, it is ensured that the design meets the requirements of the material and process, avoiding exceeding the physical limitations of the printer, thereby ensuring the feasibility and success rate of printing. Reasonable wall thickness adjustment not only enhances the structural strength of the model, but also improves the quality of the printed part, reduces the risk of failure during printing, and ensures the reliability and durability of the finished product. By accurately calculating and adjusting the wall thickness, unnecessary material waste is avoided, making material usage more economical and reasonable, and reducing production costs.

[0081] Specifically, in combination with the changes in wall layer thickness parameters, the correction of the support structure to adapt to the new design requirements further includes:

[0082] First, according to the adjusted wall thickness , calculate the contact area ratio of the support structure, the formula is , where is the actual contact area of the support structure with the printed part, is the theoretical contact area; this step helps the system quantify the contact condition between the support structure and the printed part.

[0083] According to the contact area ratio , determine the density adjustment coefficient of the support structure. If is less than a predetermined threshold , increase the density of the support structure, otherwise reduce the density. The adjustment coefficient is calculated by the formula , ensuring that the density of the support structure is moderate;

[0084] Applying the density adjustment coefficient , rearranging the support structure by increasing or decreasing the number of support points and adjusting the positions of the support points, ensuring that the support structure can provide sufficient support for the adjusted wall thickness, while avoiding excessive support leading to material waste.

[0085] By adjusting the density of the support structure, it ensures that the support structure can properly support the model during printing, avoiding printing failure or quality problems caused by insufficient or excessive support. The optimized support structure can better adapt to new design requirements, improving the accuracy and reliability of the printed part, reducing the risk of deformation or fracture during printing. By accurately calculating and adjusting the density of the support structure, unnecessary material waste is avoided, making material use more efficient and reducing production costs.

[0086] Step four: using the corrected support structure, performing a virtual printing process simulation, collecting information from the virtual printing process simulation, and evaluating the impact of optimization measures on the printing result;

[0087] Specifically, using the corrected support structure to perform a virtual printing process simulation further includes:

[0088] According to the final layout of the support structure, determine the printing path , wherein the path includes the sequence of each layer of the build object and the support; this step ensures the orderliness and efficiency of the printing process.

[0089] During the simulation, according to the path , calculate the printing time of each layer, using the formula , wherein represents the length of the i-th layer path, and V is the printing head moving speed; this step provides a specific quantification of the printing time of each layer, which helps to evaluate the overall printing efficiency.

[0090] Based on the printing time of each layer , predict the complete printing cycle , the formula is , wherein is the total number of layers, is the additional time required for switching between all layers and support removal; this prediction helps to fully understand the time required for the printing process and provides a basis for optimization.

[0091] Through the complete printing cycle prediction, evaluate the impact of the current support structure on printing efficiency, and adjust the support design as needed to optimize printing time and quality. This step ensures that the support structure not only physically supports the printed object, but also optimizes the time efficiency.

[0092] Through virtual printing process simulation, potential printing bottlenecks can be discovered and addressed in advance, optimizing print paths and support structures to reduce printing time and improve printing efficiency. Reasonable support structure design not only supports the printed object but also avoids stress concentration or deformation during printing, thereby improving the quality of the printed part. By accurately calculating the printing time of each layer and the overall printing cycle, unnecessary material waste can be avoided, reducing production costs. Virtual printing process simulation provides feedback at the design stage, allowing designers to make necessary adjustments before actual printing, simplifying the overall design and printing process.

[0093] Specifically, collecting information from the virtual printing process simulation and evaluating the impact of optimization measures on the printing result further includes:

[0094] By recording the deviation of material deposition during the printing of each layer , the deviation is expressed as , where is the actual amount of deposited material at the i-th layer is the ideal amount of material; this step helps the system quantify the accuracy of material deposition during the printing of each layer.

[0095] According to the deviation of material deposition of each layer , the overall printing error is calculated, with the formula , where is the weight given according to the importance of the layer, and n is the total number of layers; this calculation provides a quantitative evaluation of the overall printing process error.

[0096] Using the overall printing error , the effectiveness of the current optimization measures is evaluated. If is lower than the preset threshold , it is confirmed that the optimization is successful; otherwise, the optimization parameters need to be adjusted until reaches an acceptable level. This step ensures that the actual effect of the optimization measures meets the expected standards.

[0097] By quantifying the material deposition deviation in each layer and calculating the overall printing error, the effectiveness of optimization measures can be accurately assessed, thereby improving the precision and quality of the printed parts. Precise material deposition control can reduce material waste and ensure that each layer meets the design requirements, thereby optimizing material usage efficiency. By evaluating the material deposition deviation of each layer and adjusting the optimization parameters accordingly, the printed parts can be ensured to be consistent across all layers, avoiding overall quality problems caused by local deviations. Through virtual printing process simulation and error evaluation, problems can be discovered and solved before actual printing, reducing the number of trial and error, thereby improving printing efficiency.

[0098] Step five: based on the impact assessment, select the optimal configuration for the final light-cured three-dimensional construction manufacturing, further comprising:

[0099] According to the overall printing error and the printing cycle , calculate the comprehensive score , the formula is , where and are weights set according to quality and time priority; this calculation step combines the two key factors of printing quality and efficiency to provide a comprehensive index to evaluate the overall performance of different optimization schemes.

[0100] According to the comprehensive score , compare the results of different optimization schemes, and select the scheme with the lowest comprehensive score as the optimal configuration; in this way, it can be ensured that the selected scheme meets the quality requirements while also having high production efficiency.

[0101] Apply the selected optimal configuration to prepare the final printing instruction set, ensuring that all optimization measures are correctly implemented in the light-cured three-dimensional construction manufacturing process to achieve the established quality standards and production efficiency targets. This step ensures that the optimization results can be successfully applied to the actual production environment, ensuring the quality and production efficiency of the final product.

[0102] By comprehensively evaluating the printing error and the printing cycle, the optimal configuration is selected to ensure that the quality of the printed parts meets the expected standards, reducing the probability of printing failure. The selection of the optimal configuration not only considers the printing quality, but also takes into account the production efficiency, making the production process more efficient and reducing unnecessary waiting time and material waste. Through standardized evaluation methods and the selection of the optimal configuration, the consistency of each printing is ensured, avoiding product quality problems caused by improper parameter settings. The introduction of the comprehensive score simplifies the decision-making process, allowing users to quickly identify the most suitable printing configuration, reducing the time and effort of manual adjustments.

[0103] To sum up, the present application firstly analyzes the spatial distribution characteristics based on data representation, then identifies the internal and surface fragile areas, and implements customized structural reinforcement processing. Subsequently, the wall thickness parameters are adjusted according to the reinforcement processing, and the support structure is corrected to adapt to the new requirements. Then, the virtual printing process simulation is carried out using the corrected support structure to evaluate the influence of the optimization measures on the printing results. Finally, the optimal configuration is selected based on the evaluation results for manufacturing. This method enhances the weak areas of the model through automated analysis and customized strategies, dynamically adjusts the wall thickness and support, improves the printing piece accuracy and reliability, reduces material waste, shortens the preparation time, and thus improves the production efficiency and printing quality.

[0104] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for the purpose of limiting the present application, although the foregoing detailed description of the present application is made with reference to the foregoing embodiments, for those skilled in the art, it still can be modified to the technical solutions recorded in the foregoing embodiments, or equivalent replacement of some of the technical features, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included within the scope of the present application.

Claims

1. An intelligent light-cured 3D printing model optimization method, characterized in that, The method comprises the following steps: acquiring a data representation of a three-dimensional design form, based on the data representation, analyzing spatial distribution characteristics of the three-dimensional design form using a specific framework; The acquiring of the data representation of the three-dimensional design form includes forming a set consisting of a point cloud wherein each point has coordinates in a three-dimensional space; According to the point cloud set P, a distance matrix D between points is calculated, wherein , to quantify the relative position relationship between points; By analyzing the distance matrix D, the positions of the point cloud representing the weak links of the structure are identified. For the identified weak positions, an enhancement vector E is defined to indicate the areas that need to be strengthened in subsequent operations. The value of the enhancement vector E is determined by the number of neighboring points of the corresponding point, that is wherein represents the number of points with a distance less than a preset threshold from the point , and the function f determines the enhancement degree. The method comprises the following steps: calculating the volume density ρ of each part in the three-dimensional design form, wherein , The number of points contained in the neighborhood of the representative point , is the volume of the neighborhood. With the bulk density p, high-density areas and low-density areas in the three-dimensional design form are determined, a threshold T is set to distinguish different density levels, if , it is marked as a high-density area, otherwise as a low-density area; For the identified low density region, according to the distribution of its surrounding high density region, a density compensation factor C is applied to calculate the compensated density wherein the compensation factor C depends on the ratio of the average density of the neighboring high density region to its own density; determining fragile regions inside and on the surface of the three-dimensional design form according to the spatial distribution characteristics, and using a customized strategy to perform structural reinforcement processing on the determined fragile regions; The determining of the fragile region inside and on the surface of the three-dimensional design form includes: calculating the local curvature of each point , wherein the local curvature is determined by the distribution of the point and the points in its neighborhood. Applying the local curvature Identifying potential weaknesses on the structure, defining as weak areas the regions where the curvature exceeds a preset threshold K, i.e. when >K, marking the point and its adjacent region as weak; For the identified fragile region, a reinforcement coefficient S is assigned according to the stability degree of its surrounding structure, the reinforcement coefficient S is determined by the difference between the average curvature of the adjacent non-fragile region and the curvature of the fragile region, the calculation formula is wherein the greater the value of S represents the higher the reinforcement requirement. wherein the greater the value of S represents the higher the reinforcement requirement.​ For the determined weak area, a customized strategy is adopted for structural reinforcement, including: first, measure the minimum cross-sectional size of the weak area and record; According to the minimum cross-sectional dimension , the minimum wall thickness required to ensure structural stability is calculated as , where , and is a predefined scaling factor to ensure that the wall thickness is sufficient to provide adequate support. Applying the calculated minimum wall thickness Adjusting the design of the vulnerable area to add additional material until the required wall thickness is reached or exceeded thereby enhancing the overall structural strength of the area; after the structural reinforcement processing, adjusting the wall thickness parameters of the three-dimensional design form according to the reinforcement processing, and correcting the support structure to adapt to new design requirements in combination with the changes in the wall thickness parameters; using the corrected support structure, performing a virtual printing process simulation, collecting information from the virtual printing process simulation, and evaluating the impact of optimization measures on the printing result; based on the impact evaluation, selecting the optimal configuration for the final light-cured three-dimensional object manufacturing. 2.The intelligent light-cured 3D printing model optimization method of claim 1, wherein, after the structural reinforcement processing, adjusting the wall thickness parameters of the three-dimensional design form according to the reinforcement processing, comprises: reinforced new wall thickness and the original design wall thickness , the wall thickness increase ratio , the formula is ; According to the wall thickness increase ratio , adjust the thickness of all wall layers connected to the reinforced area to maintain the consistency and stability of the overall design, and the adjusted thickness of the wall layer is ; Ensuring that all associated wall layer thickness parameters are adjusted without causing the design to exceed material limitations or printer physical limitations by verifying the adjusted wall thickness is within the allowable range, i.e. tmin≤ ≤ tmax, where tminand tmaxare the minimum and maximum wall thicknesses acceptable for the material and process, respectively. 3.The intelligent light-cured 3D printing model optimization method of claim 2, wherein, in combination with the changes in the wall thickness parameters, correcting the support structure to adapt to new design requirements, comprises: First, based on the adjusted wall thickness Calculate the contact area ratio of the supporting structure The formula is ,in To support the actual contact area between the structure and the building, This represents the theoretical contact area. According to the contact area ratio , a density adjustment factor of the support structure is determined , if is less than a predetermined threshold , the density of the support structure is increased, otherwise the density is decreased, the adjustment factor is calculated by the formula ; application of the density adjustment coefficient rearranging the support structure by increasing or decreasing the number of support points and adjusting the position of the support points. 4.The intelligent light-cured 3D printing model optimization method of claim 2, wherein, using the corrected support structure, performing a virtual printing process simulation, comprises: determining a print path in dependence on the final layout of the support structure wherein the path comprises a sequence of each layer of the build and the support During the simulation, the time to print each layer is calculated based on the path using the formula where represents the path length of the i-th layer and V is the speed of the print head movement;​ Based on the print time per layer , the total print cycle is predicted , the formula is where is the total number of layers, is the additional time required for switching between all layers and support removal; through the complete printing cycle Predict, evaluate the current support structure impact on print efficiency, and adjust support design as needed to optimize print time and quality. 5.The intelligent light-cured 3D printing model optimization method of claim 3, wherein, collecting information from the virtual printing process simulation, evaluating the impact of optimization measures on the printing result, comprises: By recording the deviation of the material deposition during the printing process of each layer , the deviation is expressed as wherein is the actual amount of material deposited for the i-th layer is the amount of material in the ideal state; According to the deposition deviation of each layer of material , the overall printing error is calculated , the formula is , wherein is the weight given according to the layer importance, and n is the total number of layers Utilizing the overall print error , evaluating the effectiveness of the current optimization measures, if the result is below a preset threshold , confirming the optimization successful; otherwise, the optimization parameters need to be adjusted until an acceptable level is reached. 6.The intelligent light-cured 3D printing model optimization method of claim 4, wherein, based on the impact evaluation, selecting the optimal configuration for the final light-cured three-dimensional object manufacturing, comprises: According to the overall print error and the print cycle , a composite score is calculated, with the formula where and are weights set according to quality and time priority; According to the comprehensive score , comparing the results of different optimization schemes, selecting the scheme with the lowest comprehensive score as the optimal configuration; applying the selected optimal configuration, preparing the final printing instruction set, ensuring that all optimization measures are correctly implemented in the light-cured three-dimensional object manufacturing process to achieve the established quality standards and production efficiency targets.

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