Intelligent photocuring 3D printing model optimization method

Abstract

The invention belongs to the technical field of 3D printing, and particularly relates to an intelligent photocuring 3D printing model optimization method which covers the whole process from obtaining of data representation of a three-dimensional design form to final printing configuration selection. Firstly, spatial distribution characteristics are analyzed based on data representation, then internal and surface fragile areas are identified, and customized structure strengthening processing is implemented. Then, wall layer thickness parameters are adjusted according to strengthening treatment, and the supporting structure is corrected to meet new requirements. Thirdly, virtual printing process simulation is conducted through the corrected supporting structure, and the influence of optimization measures on a printing result is evaluated; finally, an optimal configuration is selected for manufacturing based on the evaluation result. According to the method, through automatic analysis and customization strategies, the weak area of the model is enhanced, the wall thickness and support are dynamically adjusted, the precision and reliability of a printed piece are improved, material waste is reduced, and the preparation time is shortened, so that the production efficiency and the printing quality are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of 3D printing, and in particular relates to an intelligent light-curing 3D printing model optimization method. Background Art

[0002] Stereolithography 3D printing technology has been widely used in the fields of rapid prototyping and industrial production. However, when printing parts with complex geometries, printing failures or poor quality of finished products often occur due to uneven material distribution and insufficient structural strength. 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 in the printing process makes the optimization process difficult and untargeted.

[0003] How to automatically identify and resolve structural weaknesses in 3D printed models while improving printing efficiency and finished product quality is a major challenge facing current light-curing 3D printing. Especially when facing models with complex internal structures and thin-walled features, how to ensure that these models will not deform or break during the printing process while reducing unnecessary material waste has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent light-curing 3D printing model optimization method, which can effectively identify and enhance weak areas in the 3D model through automated analysis and customized optimization strategies, and dynamically adjust the wall thickness and support structure to adapt to different printing requirements, so as to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention adopts the following technical solution: an intelligent light-curing 3D printing model optimization method, comprising the following steps: Obtaining a data representation of the three-dimensional design form, and analyzing the spatial distribution characteristics of the three-dimensional design form using a specific framework based on the data representation; According to the spatial distribution characteristics, the vulnerable areas inside and on the surface of the three-dimensional design form are determined, and customized strategies are adopted to strengthen the structure of the determined vulnerable areas; After the structural strengthening treatment, the wall thickness parameters of the three-dimensional design form are adjusted according to the strengthening treatment, and the support structure is corrected to adapt to the new design requirements in combination with the change of 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 the optimization measures on the printing results; Based on the impact assessment, the optimal configuration is selected for final photo-stereolithography 3D structure fabrication.

[0006] Preferably, obtaining a data representation of a three-dimensional design form includes: Form a collection of point clouds , where each point In three-dimensional space, there are coordinates ; According to the point cloud set P, calculate the distance matrix D between each point, where , to quantify the relative position relationship between points; By analyzing the distance matrix D, the positions representing the weak links of the structure in the point cloud are identified. For the identified weak positions, the enhancement vector E is defined to indicate the area that needs 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, ,in Representation and point The number of points whose distance is less than a preset threshold, function f determines the degree of enhancement.

[0007] Preferably, based on the data representation, a specific framework is used to analyze the spatial distribution characteristics of the three-dimensional design form, including: By calculating the volume density ρ of each part in the three-dimensional design shape, , Representative Points The number of points contained in the neighborhood of is the volume of the neighborhood; Using the volume density ρ, the high-density area and the low-density area in the three-dimensional design form are determined, and a threshold T is set to distinguish different density levels. , it is marked as a high-density area, otherwise it is a low-density area; For the marked low-density area, the density compensation factor C is applied according to the distribution of the surrounding high-density areas to calculate the compensated density. , where the compensation factor C depends on the ratio of the average density of the neighboring high-density area to its own density.

[0008] Preferably, determining the vulnerable areas inside and on the surface of the three-dimensional design form according to the spatial distribution characteristics includes: By calculating each point The local curvature ,in By point and the distribution of points in its neighborhood; Using the local curvature Identify the potential vulnerability of the structure and define the area where the curvature exceeds the preset critical value K as the vulnerable area. >K, mark point and its adjacent areas are vulnerable; For the identified vulnerable areas, a reinforcement factor S is assigned according to the stability of the surrounding structure. The reinforcement factor S is calculated by the average curvature of the adjacent non-vulnerable areas. The curvature of the fragile area The calculation formula is , where the larger the value of S is, the higher the reinforcement requirement is.

[0009] Preferably, customized strategies are used to strengthen the structure of identified vulnerable areas, including: First measure the smallest cross-sectional size of the vulnerable area , and record; According to the minimum cross-sectional dimensions , calculate the minimum required wall thickness To ensure structural stability, the formula is ,in is a predefined scaling factor used to ensure that the wall thickness is sufficient to provide adequate support; Apply the calculated minimum wall thickness , adjust the design of the weak area and add additional material until the required wall thickness is reached or exceeded , thereby enhancing the overall structural strength of the area.

[0010] Preferably, after the structural strengthening treatment, adjusting the wall thickness parameters of the three-dimensional design form according to the strengthening treatment includes: Based on the new wall thickness after strengthening and the original designed wall thickness , calculate the wall thickness increase ratio , the formula is ; Increase proportion according to the wall thickness , adjust the thickness of all walls connected to the strengthened area to maintain the consistency and stability of the overall design. The adjusted wall thickness for ; Ensure that adjustments to all associated wall thickness parameters do not cause the design to exceed material limitations or printer physical limitations by verifying the adjusted wall thickness Is it within the allowable range, that is, tmin≤ ≤tmax, where tmin and tmax are the minimum and maximum wall thicknesses acceptable for the material and process respectively.

[0011] Preferably, in combination with the change in the wall thickness parameter, the support structure is corrected to adapt to the new design requirements, including: First, according to the adjusted wall thickness , calculate the contact area ratio of the support structure , the formula is ,in is the actual contact area between the support structure and the structure, is the theoretical contact area; According to the contact area ratio , determine the density adjustment factor of the support structure ,like Less than a predetermined threshold , then increase the density of the support structure, otherwise reduce the density and adjust the coefficient The calculation formula is ; Apply the density adjustment factor , rearrange the support structure by increasing or decreasing the number of support points and adjusting the positions of support points.

[0012] Preferably, using the corrected support structure, a virtual printing process simulation is performed, including: Determine the printing path based on the final layout of the support structure , where the path Includes the order of each layer of construction and support; During the simulation, according to the path , calculate the printing time of each layer , using the formula ,in represents the path length of the i-th layer, and V is the moving speed of the print head; Based on the printing time per layer , predicting the complete printing cycle , the formula is ,in is the total number of layers, The extra time required to switch between all layers and support removal; Through the complete printing cycle Predict and evaluate the impact of the current support structure on printing efficiency, and adjust the support design as needed to optimize printing time and quality.

[0013] Preferably, collecting information from the virtual printing process simulation to evaluate the impact of optimization measures on printing results includes: By recording the deviation of material deposition during each layer printing process ,deviation Expressed as ,in is the actual amount of deposited material in the i-th layer is the amount of material under ideal conditions; According to the deposition deviation of each layer material , calculate the overall printing error , the formula is ,in is the weight assigned according to the importance of the layer, and n is the total number of layers; Using the overall printing error , evaluate the effectiveness of the current optimization measures, if Below the preset threshold , the optimization is confirmed to be successful; otherwise, the optimization parameters need to be adjusted until reach an acceptable level.

[0014] Preferably, based on the impact assessment, an optimal configuration is selected for final photocurable three-dimensional structure manufacturing, including: According to the overall printing error and print cycle , calculate the comprehensive score , the formula is ,in and Weights set based on quality and time priorities; According to the comprehensive score , compare the results of different optimization schemes and select the one with the lowest comprehensive score The solution is taken as the optimal configuration; Apply the selected optimal configuration and prepare the final printing instruction set, ensuring that all optimization measures are correctly implemented in the light-stereolithography 3D structure manufacturing process to achieve the established quality standards and production efficiency goals. Technical effects and advantages of the present invention: Compared with the prior art, the intelligent light-curing 3D printing model optimization method proposed by the present invention has the following advantages: The present invention can effectively identify and enhance weak areas in 3D models through automated analysis and customized optimization strategies, and dynamically adjust wall thickness and support structures to meet different printing requirements. This method can not only significantly improve the accuracy and reliability of printed parts, but also improve production efficiency by reducing material waste and shortening printing preparation time, thereby bringing higher quality printing experience and economic benefits to light-curing 3D printing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flow chart of an intelligent light-curing 3D printing model optimization method. DETAILED DESCRIPTION

[0016] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0017] The present invention provides an intelligent light-curing 3D printing model optimization method, the purpose of which is to improve the structural strength and printing quality of the 3D printing model by automated means, such as Figure 1 As shown, the following steps are included: Step 1: Obtain a data representation of the 3D design form, which is extracted from an STL or other format file exported from the CAD software, and analyze the spatial distribution characteristics of the 3D design form using a specific framework based on the data representation; Specifically, obtaining the data representation of the three-dimensional design form further includes: Read the 3D design file and convert it into point cloud form to form a collection of point clouds , where each point In three-dimensional space, there are coordinates ; Convert the 3D design form into point cloud form for subsequent analysis and processing. This process enables the system to handle complex geometric structures and provides a basis for subsequent analysis.

[0018] According to the point cloud set P, calculate the distance matrix D between each point, where , to quantify the relative position relationship between points; by calculating the distance between points in the point cloud and forming a distance matrix, the system can quantify the relative position relationship between points. This helps the system better understand the spatial distribution characteristics of the design form.

[0019] By analyzing the distance matrix D, the locations representing the weak links of the structure in the point cloud are identified, that is, those point sets with large spatial gaps or fewer connections. For the identified weak locations, an enhancement vector E is defined to indicate the area that needs 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, ,in Representation and point The number of points whose distance is less than a preset threshold, and the function f determines the degree of reinforcement. By analyzing the distance matrix D, the system can identify the locations of the design form where the structure is relatively weak, and define reinforcement vectors E for these locations, indicating the areas that need to be strengthened. The value of the reinforcement vector E depends on the number of neighboring points of the corresponding point, thus achieving targeted structural optimization.

[0020] It can automatically identify potential structural weaknesses in 3D models and make corresponding optimization suggestions. By strengthening the structure, adjusting the wall thickness and improving the support settings, the system can significantly improve printing accuracy and efficiency, reduce material waste, and provide users with tailored optimization solutions, promoting the development of light-curing 3D printing technology to a higher level of intelligence.

[0021] Specifically, based on the data representation, using a specific framework to analyze the spatial distribution characteristics of the three-dimensional design form further includes: By calculating the volume density ρ of each part in the three-dimensional design shape, , Representative Points The number of points contained in the neighborhood of is the volume of this neighborhood; this calculation provides a quantitative description of the density of the structure inside the designed form.

[0022] Using the volume density ρ, the high-density area and the low-density area in the three-dimensional design form are determined, and a threshold T is set to distinguish different density levels. , 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.

[0023] For the marked low-density area, the density compensation factor C is applied according to the distribution of the surrounding high-density areas to calculate the compensated density. , where the compensation factor C depends on the ratio of the average density of the adjacent high-density area to its own density. This compensation mechanism ensures that the low-density area can be filled with sufficient material during the printing process to enhance its structural stability.

[0024] Through the above technical means, the present invention can automatically identify possible structural weaknesses in 3D models and effectively enhance the structural strength of these areas by adjusting the 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 higher economic benefits to the application of light-curing 3D printing technology.

[0025] Step 2: According to the spatial distribution characteristics, the vulnerable areas inside and on the surface of the three-dimensional design form are determined, and a customized strategy is used to strengthen the structure of the determined vulnerable areas; Specifically, according to the spatial distribution characteristics, determining the vulnerable areas inside and on the surface of the three-dimensional design form further includes: By calculating each point The local curvature ,in By point and the distribution of points in its neighborhood; this step can help the system understand the curvature and change trend of the model surface; Using the local curvature Identify the potential vulnerability of the structure and define the area where the curvature exceeds the preset critical value K as the vulnerable area. >K, mark point and its adjacent areas are vulnerable; For the identified vulnerable areas, a reinforcement factor S is assigned according to the stability of the surrounding structure. The reinforcement factor S is calculated by the average curvature of the adjacent non-vulnerable areas. The curvature of the fragile area The calculation formula is , where the larger the value of S is, the higher the reinforcement requirement is.

[0026] Through the above technical means, the present invention can automatically identify potential structural weaknesses in 3D models and determine which areas need to be strengthened in a quantitative manner. 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, bringing higher economic benefits to the application of light-curing 3D printing technology.

[0027] Specifically, for the identified vulnerable areas, customized strategies are adopted for structural strengthening, including: First measure the smallest cross-sectional size of the vulnerable area , and record it; this is to evaluate the structural stability problems that the area may face during the printing process. By adjusting the wall thickness of the fragile areas, the material density of these areas is increased, thereby enhancing their structural strength and reducing the risk of deformation or breakage during the printing process.

[0028] According to the minimum cross-sectional dimensions , calculate the minimum required wall thickness To ensure structural stability, the formula is ,in It is a predefined proportional factor used to ensure that the wall thickness is sufficient to provide sufficient support. By accurately calculating the required wall thickness instead of blindly adding material, excessive use of material is avoided, waste is reduced, and manufacturing costs are also reduced.

[0029] Apply the calculated minimum wall thickness , adjust the design of the weak area and add additional material until the required wall thickness is reached or exceeded , thereby enhancing the overall structural strength of the area.

[0030] Strengthening treatment ensures the reliability and durability of printed parts under complex geometric shapes, improves the quality of finished products, and makes the final products more in line with 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, thereby improving production efficiency. It can not only significantly improve the structural stability and printing quality of 3D printed parts, but also reduce costs through reasonable material use strategies, thereby bringing higher economic benefits to light-curing 3D printing technology.

[0031] Step 3: After the structural strengthening treatment, the wall thickness parameters of the three-dimensional design form are adjusted according to the strengthening treatment, and the support structure is corrected to adapt to the new design requirements in combination with the change of the wall thickness parameters; Specifically, after the structural strengthening treatment, adjusting the wall thickness parameters of the three-dimensional design form according to the strengthening treatment further includes: Based on the new wall thickness after strengthening and the original designed wall thickness , calculate the wall thickness increase ratio , the formula is ; This ratio reflects the extent to which the wall thickness needs to be increased relative to the original design.

[0032] Increase proportion according to the wall thickness , adjust the thickness of all walls connected to the strengthened area to maintain the consistency and stability of the overall design. The adjusted wall thickness for ; This step ensures the consistency of wall thickness throughout the model and prevents structural imbalance caused by local strengthening.

[0033] Ensure that adjustments to all associated wall thickness parameters do not cause the design to exceed material limitations or printer physical limitations by verifying the adjusted wall thickness Is it within the allowable range, that is, tmin≤ ≤tmax, where tmin and tmax are the minimum and maximum wall thicknesses acceptable to the material and process, respectively. This ensures the feasibility of the final design and avoids printing failure or material waste due to unreasonable wall thickness.

[0034] By adjusting the wall thickness, the overall consistency and stability of the model after structural reinforcement is ensured, and the structural imbalance caused by local reinforcement is avoided. By verifying whether the wall thickness is within the allowable range, it is ensured that the design meets the requirements of materials and processes, and the situation of exceeding the physical limitations of the printer is avoided, 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 print, reduces the risk of failure during the printing process, and ensures the reliability and durability of the finished product. By accurately calculating and adjusting the wall thickness, unnecessary material waste is avoided, making the use of materials more economical and reasonable, and reducing production costs.

[0035] Specifically, in combination with the change in the wall thickness parameter, correcting the support structure to adapt to the new design requirements further includes: First, according to the adjusted wall thickness , calculate the contact area ratio of the support structure , the formula is ,in is the actual contact area between the support structure and the structure, is the theoretical contact area; this step helps the system quantify the contact conditions between the support structure and the printed part.

[0036] According to the contact area ratio , determine the density adjustment factor of the support structure ,like Less than a predetermined threshold , then increase the density of the support structure, otherwise reduce the density and adjust the coefficient The calculation formula is , ensure that the density of the supporting structure is moderate; Apply the density adjustment factor , rearrange the support structure by increasing or decreasing the number of support points and adjusting the positions of support points to ensure that the support structure can provide sufficient support for the adjusted wall thickness while avoiding material waste caused by excessive support.

[0037] By adjusting the density of the support structure, it is ensured that the support structure can properly support the model during the printing process, avoiding printing failure or quality problems caused by insufficient or excessive support. The optimized support structure can better adapt to new design requirements, improve the accuracy and reliability of the printed parts, and reduce the risk of deformation or breakage that may occur during the printing process. 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.

[0038] Step 4: 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; Specifically, using the corrected support structure, performing the virtual printing process simulation further includes: Determine the printing path based on the final layout of the support structure , where the path This includes the order of each layer of build and support; this step ensures order and efficiency during the printing process.

[0039] During the simulation, according to the path , calculate the printing time of each layer , using the formula ,in represents the path length of the i-th layer, and V is the print head moving speed; this step provides a specific quantification of the printing time of each layer, which helps to evaluate the overall printing efficiency.

[0040] Based on the printing time per layer , predicting the complete printing cycle , the formula is ,in is the total number of layers, The additional time required for switching between all layers and support removal is calculated; this prediction helps to fully understand the time required for the printing process and provide a basis for optimization.

[0041] Through the complete printing cycle Predict and 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 is also optimized in terms of time efficiency.

[0042] Through virtual printing process simulation, potential printing bottlenecks can be discovered and resolved in advance, and the printing path and support structure can be optimized, thereby reducing printing time and improving printing efficiency. Reasonable support structure design can not only support the printing object, but also avoid problems such as stress concentration or deformation during the printing process, 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 and production costs can be reduced. Virtual printing process simulation provides feedback in the design stage, allowing designers to make necessary adjustments before actual printing, simplifying the overall design and printing process.

[0043] Specifically, collecting information from the virtual printing process simulation and evaluating the impact of optimization measures on printing results further includes: By recording the deviation of material deposition during each layer printing process ,deviation Expressed as ,in is the actual amount of deposited material in the i-th layer is the ideal amount of material; this step helps the system quantify the accuracy of material deposition during each layer printing process.

[0044] According to the deposition deviation of each layer material , calculate the overall printing error , the formula is ,in is the weight assigned according to the importance of the layer, and n is the total number of layers; this calculation provides a quantitative assessment of the error of the overall printing process.

[0045] Using the overall printing error , evaluate the effectiveness of the current optimization measures, if Below the preset threshold , the optimization is confirmed to be successful; otherwise, the optimization parameters need to be adjusted until This step ensures that the actual effect of the optimization measures meets the expected standards.

[0046] By quantifying the material deposition deviation during each layer of printing and calculating the overall printing error, the effect of optimization measures can be accurately evaluated, thereby improving the accuracy and quality of printed parts. Accurate material deposition control can reduce material waste and ensure that each layer of printing meets the design requirements, thereby optimizing the efficiency of material use. By evaluating the material deposition deviation of each layer and adjusting the optimization parameters accordingly, it is possible to ensure that the prints are consistent at all levels and avoid 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 times, thereby improving printing efficiency.

[0047] Step 5: Based on the impact assessment, selecting the optimal configuration for final photocurable 3D structure manufacturing, further comprising: According to the overall printing error and print cycle , calculate the comprehensive score , the formula is ,in and The weights are set according to the quality and time priorities; this calculation step combines the two key factors of print quality and efficiency to provide a comprehensive indicator to facilitate the evaluation of the overall performance of different optimization solutions.

[0048] According to the comprehensive score , compare the results of different optimization schemes and select the one with the lowest comprehensive score The solution is taken as the optimal configuration; in this way, it can be ensured that the selected solution has high production efficiency while meeting the quality requirements.

[0049] Apply the selected optimal configuration, prepare the final printing instruction set, and ensure that all optimization measures are correctly implemented in the light-curing 3D structure manufacturing process to achieve the established quality standards and production efficiency goals. This step ensures that the optimization results can be smoothly applied to the actual production environment, ensuring the quality and production efficiency of the final product.

[0050] By comprehensively evaluating the printing error and printing cycle, the optimal configuration is selected to ensure that the quality of the printed part meets the expected standards and reduce the probability of printing failure. The selection of the optimal configuration not only takes into account 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 print is ensured, avoiding product quality problems caused by improper parameter settings. The introduction of comprehensive scoring simplifies the decision-making process, allowing users to quickly identify the most appropriate printing configuration and reduce the time and effort of manual adjustments.

[0051] In summary, the present invention first analyzes the spatial distribution characteristics based on data representation, and then identifies the internal and surface vulnerable areas, and implements customized structural strengthening treatment. Subsequently, the wall thickness parameters are adjusted according to the strengthening treatment, and the support structure is corrected to adapt to the new requirements. Next, the virtual printing process simulation is carried out using the corrected support structure to evaluate the impact of the optimization measures on the printing results. Finally, the optimal configuration is selected for manufacturing based on the evaluation results. This method uses automated analysis and customized strategies to enhance the weak areas of the model, dynamically adjust the wall thickness and support, improve the accuracy and reliability of the printed parts, reduce material waste, shorten preparation time, and thus improve production efficiency and printing quality.

[0052] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent light-curing 3D printing model optimization method, characterized in that: The following steps are involved: Obtaining a data representation of the three-dimensional design form, and analyzing the spatial distribution characteristics of the three-dimensional design form using a specific framework based on the data representation; According to the spatial distribution characteristics, the vulnerable areas inside and on the surface of the three-dimensional design form are determined, and customized strategies are adopted to strengthen the structure of the determined vulnerable areas; After the structural strengthening treatment, the wall thickness parameters of the three-dimensional design form are adjusted according to the strengthening treatment, and the support structure is corrected to adapt to the new design requirements in combination with the change of 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 the optimization measures on the printing results; Based on the impact assessment, the optimal configuration is selected for final photo-stereolithography 3D structure fabrication.

2. The intelligent light-curing 3D printing model optimization method according to claim 1, characterized in that: Obtain data representation of the three-dimensional design form, including: Form a collection of point clouds , where each point In three-dimensional space, there are coordinates ; According to the point cloud set P, calculate the distance matrix D between each point, where , to quantify the relative position relationship between points; By analyzing the distance matrix D, the positions representing the weak links of the structure in the point cloud are identified. For the identified weak positions, the enhancement vector E is defined to indicate the area that needs 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, ,in Representation and point The number of points whose distance is less than a preset threshold, function f determines the degree of enhancement.

3. The intelligent light-curing 3D printing model optimization method according to claim 2, characterized in that: Based on the data representation, a specific framework is used to analyze the spatial distribution characteristics of the three-dimensional design form, including: By calculating the volume density ρ of each part in the three-dimensional design shape, , Representative Points The number of points contained in the neighborhood of is the volume of the neighborhood; Using the volume density ρ, the high-density area and the low-density area in the three-dimensional design form are determined, and a threshold T is set to distinguish different density levels. , it is marked as a high-density area, otherwise it is a low-density area; For the marked low-density area, the density compensation factor C is applied according to the distribution of the surrounding high-density areas to calculate the compensated density. , where the compensation factor C depends on the ratio of the average density of the neighboring high-density area to its own density.

4. The intelligent light-curing 3D printing model optimization method according to claim 3, characterized in that: According to the spatial distribution characteristics, the vulnerable areas inside and on the surface of the three-dimensional design form are determined, including: By calculating each point The local curvature ,in By point and the distribution of points in its neighborhood; Using the local curvature Identify the potential vulnerability of the structure and define the area where the curvature exceeds the preset critical value K as the vulnerable area. >K, mark point and its adjacent areas are vulnerable; For the identified vulnerable areas, a reinforcement factor S is assigned according to the stability of the surrounding structure. The reinforcement factor S is calculated by the average curvature of the adjacent non-vulnerable areas. The curvature of the fragile area The calculation formula is , where the larger the value of S is, the higher the reinforcement requirement is.

5. The intelligent light-curing 3D printing model optimization method according to claim 4, characterized in that: Customized strategies are used to strengthen the structure in the identified vulnerable areas, including: First measure the smallest cross-sectional size of the vulnerable area , and record; According to the minimum cross-sectional dimensions , calculate the minimum required wall thickness To ensure structural stability, the formula is ,in is a predefined scaling factor used to ensure that the wall thickness is sufficient to provide adequate support; Apply the calculated minimum wall thickness , adjust the design of the weak area and add additional material until the required wall thickness is reached or exceeded , thereby enhancing the overall structural strength of the area.

6. The intelligent light-curing 3D printing model optimization method according to claim 5, characterized in that: After the structural strengthening treatment, the wall thickness parameters of the three-dimensional design form are adjusted according to the strengthening treatment, including: Based on the new wall thickness after strengthening and the original designed wall thickness , calculate the wall thickness increase ratio , the formula is ; Increase proportion according to the wall thickness , adjust the thickness of all walls connected to the strengthened area to maintain the consistency and stability of the overall design. The adjusted wall thickness for ; Ensure that adjustments to all associated wall thickness parameters do not cause the design to exceed material limitations or printer physical limitations by verifying the adjusted wall thickness Is it within the allowable range, that is, tmin≤ ≤tmax, where tmin and tmax are the minimum and maximum wall thicknesses acceptable for the material and process respectively.

7. The intelligent light-curing 3D printing model optimization method according to claim 6, characterized in that: Incorporating changes in wall thickness parameters, the support structure is corrected to accommodate new design requirements, including: First, according to the adjusted wall thickness , calculate the contact area ratio of the support structure , the formula is ,in is the actual contact area between the support structure and the structure, is the theoretical contact area; According to the contact area ratio , determine the density adjustment factor of the support structure ,like Less than a predetermined threshold , then increase the density of the support structure, otherwise reduce the density and adjust the coefficient The calculation formula is ; Apply the density adjustment factor , rearrange the support structure by increasing or decreasing the number of support points and adjusting the positions of support points.

8. The intelligent light-curing 3D printing model optimization method according to claim 7, characterized in that: Using the corrected support structure, perform a virtual printing process simulation, including: Determine the printing path based on the final layout of the support structure , where the path Includes the order of each layer of construction and support; During the simulation, according to the path , calculate the printing time of each layer , using the formula ,in represents the path length of the i-th layer, and V is the moving speed of the print head; Based on the printing time per layer , predicting the complete printing cycle , the formula is ,in is the total number of layers, The extra time required to switch between all layers and support removal; Through the complete printing cycle Predict and evaluate the impact of the current support structure on printing efficiency, and adjust the support design as needed to optimize printing time and quality.

9. The intelligent light-curing 3D printing model optimization method according to claim 8, characterized in that: Collect information from the virtual printing process simulation to evaluate the impact of optimization measures on printing results, including: By recording the deviation of material deposition during each layer printing process ,deviation Expressed as ,in is the actual amount of deposited material in the i-th layer is the amount of material under ideal conditions; According to the deposition deviation of each layer material , calculate the overall printing error , the formula is ,in is the weight assigned according to the importance of the layer, and n is the total number of layers; Using the overall printing error , evaluate the effectiveness of the current optimization measures, if Below the preset threshold , the optimization is confirmed to be successful; otherwise, the optimization parameters need to be adjusted until reach an acceptable level.

10. The intelligent light-curing 3D printing model optimization method according to claim 9, characterized in that: Based on the impact assessment, the optimal configuration is selected for the final photo-stereolithography 3D structure fabrication, including: According to the overall printing error and print cycle , calculate the comprehensive score , the formula is ,in and Weights set based on quality and time priorities; According to the comprehensive score , compare the results of different optimization schemes and select the one with the lowest comprehensive score The solution is taken as the optimal configuration; Apply the selected optimal configuration and prepare the final printing instruction set, ensuring that all optimization measures are correctly implemented in the light-stereolithography 3D structure manufacturing process to achieve the established quality standards and production efficiency goals.

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