A method for predicting properties of light-curing materials

Abstract

The present invention belongs to the field of 3D printing technology, and specifically relates to a method for predicting the performance of photocurable materials, comprising a first information set for collecting photocurable material formula data, a second information set for obtaining illumination condition data corresponding to the material formula, and a third information set for collecting 3D printing process parameters set for the illumination conditions. These three information sets are used to prepare training samples, and a multi-layer neural network structure is designed and trained to predict the performance of photocurable materials. By evaluating the prediction results, the printing parameters are adjusted to form an optimized parameter set, and the optimized parameters are applied to the new performance prediction to verify the improvement effect. Finally, the neural network is adjusted based on feedback from actual performance data to improve prediction accuracy. The present invention reduces experimental dependence, shortens the R&D cycle, reduces costs, and improves the quality and production efficiency of 3D printed products through an efficient neural network model.

Description

Technical Field

[0001] The present invention belongs to the technical field of 3D printing, and in particular relates to a method for predicting properties of photocurable materials. Background Art

[0002] Traditional methods for evaluating the performance of photocurable materials typically rely on extensive experimental testing, which is both time-consuming and costly. With the advancement of 3D printing technology, the demand for rapid and accurate prediction of the performance of photocurable materials under varying conditions has increased significantly. Currently, most research focuses on improving material formulations or optimizing printing parameters through experimental methods, but this approach is inefficient and struggles to adapt to rapidly changing market demands.

[0003] Existing methods for evaluating the performance of photocurable materials rely too much on experimental verification, resulting in long R&D cycles and high costs for new materials. It is also difficult to adjust printing parameters in real time to cope with different lighting conditions and changes in material formulations. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for predicting the properties of photocurable materials. By constructing a multi-level neural network model, it can efficiently and accurately predict the mechanical properties, thermal stability and optical characteristics of the material when the material formula, lighting conditions and printing parameters are input, 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: a method for predicting properties of a photocurable material, comprising the following steps:

[0006] S1: collecting a first information set including photocurable material recipe data;

[0007] S2: Based on the first information set, obtaining a second information set corresponding to the lighting condition data, wherein the second information set is directly associated with the material formula in the first information set;

[0008] S3: Based on the second information set, collecting a third information set related to 3D printing process parameters, ensuring that these parameters are set for the lighting conditions in the second information set;

[0009] S4: preparing training samples using the data obtained from the first to third information sets, each sample covering a recipe, a lighting condition, and a printing parameter;

[0010] S5: Based on the prepared training samples, a multi-layer neural network structure is designed and trained to predict the performance of light-curable materials. The trained neural network structure is used to input new formulation data to evaluate its performance under specific lighting conditions.

[0011] S6: Based on the evaluation results, adjust the printing parameters to form an optimized printing parameter set. Apply the optimized printing parameters to the new light-curing material performance prediction and verify the actual performance of the material under the improved printing conditions.

[0012] S7: Based on actual performance data, feedback is provided to adjust the multi-layer neural network to improve prediction accuracy, and the iteration continues until the expected prediction accuracy is achieved.

[0013] Preferably, in S1, the following sub-steps are included:

[0014] Quantify the proportion of each component in the first information set, using linear combination where x i represents the proportion of the i-th material, w i is the corresponding weight coefficient;

[0015] Using the quantified F(x) value, calculate the corresponding material mixture density D, through the formula where d i is the known density of the i-th material;

[0016] Based on the calculated density D, the basic physical properties of the material mixture are evaluated.

[0017] Preferably, in S2, the following sub-steps are included:

[0018] For each light-curable material formula in the first information set, record its illumination intensity I and illumination time t under a specific light source to form a second information set, where illumination intensity and illumination time are parameters that directly affect the curing of the material;

[0019] Based on the recorded illumination conditions, calculate the photopolymerization rate constant k using the formula Where T represents the transmittance under given lighting conditions;

[0020] The rate constant k is used to predict the curing degree C of the material under different lighting conditions. The curing degree is calculated by the formula C = 1-e -k·I·t to confirm.

[0021] Preferably, in S3, the following sub-steps are included:

[0022] For each lighting condition in the second information set, record the parameters of nozzle speed v, layer thickness h, and filling density p during the 3D printing process to form a third information set;

[0023] Calculate the single layer printing time T based on the nozzle speed v and layer thickness h l , using the formula Where l is the layer length, t is the time the nozzle waits before printing a new layer. lMatch the curing time under lighting conditions;

[0024] Using the obtained single layer printing time T l , combined with the filling density p, the actual curing quality Q of each layer of material under given printing parameters is evaluated by the formula Calculate, where t cure is the curing time determined based on the second information set.

[0025] Preferably, in S4, the following sub-steps are included:

[0026] Integrate the material formula information in the first information set, the lighting condition data in the second information set, and the 3D printing parameters in the third information set to form a structured data entry;

[0027] According to the data entry, define the feature vector X of each training sample, where X=[x1,x2,x3,...,x n ], x1 represents the material formula, x2 represents the lighting conditions, and x3 represents the printing parameters;

[0028] For each feature vector X, a corresponding performance label Y is assigned. This label is based on the material properties obtained from experimental measurements to form a training sample pair (X, Y) for subsequent analysis.

[0029] Preferably, in S5, the following sub-steps are included:

[0030] The prepared training sample pair (X, Y) is input into a pre-designed multi-layer neural network, where the input layer receives the feature vector X and the output layer produces the performance prediction

[0031] Using loss function To quantify the predicted value The difference between the actual label Y and the actual label Y, the loss function is defined as represents the squared loss of prediction error;

[0032] Based on the calculated loss, the weights W of each layer in the network are updated through the back-propagation algorithm to minimize the loss function, and the optimization is iterated until the network can accurately predict the performance of the photocurable material.

[0033] Preferably, in S5, the following sub-steps are included:

[0034] Create feature vector X for new recipe data new , where X new =[x 1,new ,x 2,new ,x 3,new ,...,x n,new ], x 1,newIndicates the information of the new recipe, x 2,new Indicates the corresponding lighting conditions, x 3,new Indicates the printing parameters that match it;

[0035] The constructed feature vector X new Input into the trained neural network to obtain the predicted performance output in Reflects the expected performance of new materials under specific lighting conditions;

[0036] based on Compare the goodness of fit R between the predicted performance and the actual test results using the formula where Y actual is the performance value obtained from actual testing. It's Y actual The average value of .

[0037] Preferably, in S6, the following sub-steps are included:

[0038] Analyze the performance difference ΔP in the evaluation results, Identify the printing parameters that have the greatest impact on performance;

[0039] According to the most influential parameter p max , apply the fine-tuning formula p′ max =p max +Δp, where Δp is the increment adjusted based on the performance difference ΔP to optimize the printing parameters;

[0040] Using the optimized parameter p′ max , reconfigure the printing parameter set P′ and predict the performance again through the neural network.

[0041] Preferably, in S6, the following sub-steps are included:

[0042] Use the optimized printing parameter set P′ to construct new training samples (X′, Y′), where X′ contains the adjusted printing parameters and other relevant variables;

[0043] Input the new training sample X′ into the previously trained multi-layer neural network to obtain the performance prediction value under the improved printing condition

[0044] Comparison of predicted values Compared with the actual test results Y′, the performance improvement index I is calculated using the formula Where Y is the performance value measured under original conditions.

[0045] Preferably, in S7, the following sub-steps are included:

[0046] The actual performance data Y″ and the corresponding predicted value collected A new training sample pair (X″, Y″) is formed, where Y″ refers to the performance data of the photocurable material obtained through actual testing or experiments after adopting the optimized printing parameter set P′. It refers to the results obtained by using a trained multi-layer neural network to predict the performance of new light-curing materials under the same conditions;

[0047] Using the new training sample pair (X″, Y″), the weight W of the multi-layer neural network is updated by the gradient descent method. The update rule is: Where η is the learning rate, is the gradient of the loss function with respect to the weights;

[0048] According to the updated weight W new , re-evaluate the prediction accuracy A of the neural network, using the formula in is the new predicted value after updating the weights, The average value of is calculated and iterated continuously until the prediction accuracy meets the predetermined standard.

[0049] Technical effects and advantages of the present invention: Compared with the prior art, the method for predicting the properties of photocurable materials proposed in the present invention has the following advantages:

[0050] By constructing a multi-level neural network model, this method can efficiently and accurately predict a material's mechanical properties, thermal stability, and optical characteristics, given the input of material formula, lighting conditions, and printing parameters. This method reduces reliance on traditional experimental methods, significantly shortens the development cycle for new materials, reduces R&D costs, and enables rapid adjustment of printing parameters based on actual needs, significantly improving the quality and production efficiency of 3D printed products. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Flowchart of the method for predicting properties of photocurable materials according to the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the 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 embodiments described are only part of the embodiments of the present invention, not all of 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 of 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.

[0053] This invention provides a method for predicting the properties of photocurable materials. By constructing a multi-level neural network model, it can efficiently and accurately predict the mechanical properties, thermal stability, and optical characteristics of materials by inputting material formulas, lighting conditions, and printing parameters. This method reduces reliance on traditional experimental methods, significantly shortens the development cycle of new materials, reduces R&D costs, and enables rapid adjustment of printing parameters based on actual needs, thereby significantly improving the quality and production efficiency of 3D printed products. The details are as follows:

[0054] like Figure 1 As shown, a method for predicting properties of a photocurable material in this embodiment includes the following steps:

[0055] S1: Collecting a first information set including photocurable material recipe data; specifically as follows:

[0056] Quantify the proportion of each component in the first information set, using linear combination where x i represents the proportion of the i-th material, w i is the corresponding weight coefficient; using the quantified F(x) value, calculate the corresponding material mixture density D, through the formula where d i is the known density of the i-th material;

[0057] Based on the calculated density D, the basic physical properties of the material mixture are evaluated. This makes the evaluation of material formulations more scientific and precise, helping to screen material combinations with ideal properties at an early stage. This reduces the number of subsequent experiments, accelerates the R&D process, and ensures that the ultimately selected material formulation will demonstrate excellent performance in practical applications. Furthermore, the material density information obtained in this way can provide an important reference for optimizing lighting conditions and printing parameters in subsequent steps, further improving the consistency and reliability of 3D printed products.

[0058] S2: Based on the first information set, obtain a second information set corresponding to the lighting condition data, wherein the second information set is directly associated with the material formula in the first information set; specifically as follows:

[0059] For each light-curable material formula in the first information set, record its illumination intensity I and illumination time t under a specific light source to form a second information set, where illumination intensity and illumination time are parameters that directly affect the curing of the material;

[0060] Based on the recorded illumination conditions, calculate the photopolymerization rate constant k using the formula Where T represents the transmittance under given lighting conditions; the transmittance T can be obtained through experimental measurement or by querying an existing material property database.

[0061] The rate constant k is used to predict the curing degree C of the material under different lighting conditions. The curing degree is calculated by the formula C = 1-e -k·I·t This formula reflects the degree of curing of the material under specific lighting conditions, that is, the degree of curing reaction of the material under the action of light.

[0062] By recording the intensity and duration of each formulation's exposure to a specific light source, we can generate a data set of lighting conditions directly linked to the material's formulation. The photopolymerization rate constant calculated from this data allows us to predict the degree of curing of the material under different lighting conditions. This not only helps us understand the material's curing mechanism but also provides an important basis for optimizing printing parameters.

[0063] By calculating the photopolymerization reaction rate constant and the degree of cure, we can more accurately predict the material's curing effect under specific lighting conditions, thereby improving the accuracy of the overall prediction model. Based on the predicted degree of cure, we can more rationally set the lighting conditions during the 3D printing process to ensure that the material's performance during the curing process meets expectations. By precisely controlling the curing process, we can optimize material properties and improve the quality and reliability of the final product.

[0064] S3: Based on the second information set, a third information set related to 3D printing process parameters is collected to ensure that these parameters are set for the lighting conditions in the second information set; specifically, as follows:

[0065] For each lighting condition in the second information set, the parameters of nozzle speed v, layer thickness h, and filling density p during the 3D printing process are recorded to form the third information set; these parameters directly affect the curing effect of the material during the printing process and the quality of the final product.

[0066] Calculate the single layer printing time T based on the nozzle speed v and layer thickness h l , using the formula Where l is the layer length, t is the time the nozzle waits before printing a new layer. l Match the curing time under lighting conditions; the purpose of this is to ensure that the single layer printing time matches the curing time under lighting conditions to ensure that each layer of material can be fully cured.

[0067] Using the obtained single layer printing time T l , combined with the filling density p, the actual curing quality Q of each layer of material under given printing parameters is evaluated by the formula Calculate, where t cure The curing time is determined based on the second information set. This formula quantifies the degree of curing of each layer of material under specific printing parameters, ensuring that the material achieves the expected curing effect during the printing process.

[0068] By calculating the printing time and curing quality of a single layer, it is possible to ensure that each layer of material reaches its optimal state during the curing process, avoiding product defects caused by under- or over-curing. Based on the curing quality assessment results, parameters such as nozzle speed, layer thickness, and fill density can be adjusted specifically to make the 3D printing process more efficient and stable, thereby improving the quality of the final product. By ensuring the curing quality of each layer of material, the overall consistency and reliability of 3D printed products can be significantly improved, the scrap rate can be reduced, and production efficiency can be increased. Based on the results of the curing quality assessment, they can be fed back into the early material formulation and lighting condition settings, forming a closed-loop optimization system and continuously improving the scientific nature and rationality of the entire process.

[0069] S4: Using the data obtained from the first to third information sets, prepare training samples, each sample covering a recipe, lighting conditions, and printing parameters; specifically, as follows:

[0070] The material formula information in the first information set, the lighting condition data in the second information set, and the 3D printing parameters in the third information set are integrated to form structured data entries; these entries contain a complete description of the experimental conditions, which facilitates subsequent data processing and analysis.

[0071] According to the data entry, define the feature vector X of each training sample, where X=[x1,x2,x3,...,x n ], x1 represents the material formula, x2 represents the lighting conditions, and x3 represents the printing parameters; each eigenvector X is a multidimensional vector containing all influencing factors and can fully describe the conditions of an experimental sample.

[0072] For each feature vector X, a corresponding performance label Y is assigned. This label is based on experimentally measured material properties, such as mechanical strength, thermal stability, and optical properties. This forms training sample pairs (X, Y) for subsequent analysis, where X represents the input feature and Y represents the output label. These training sample pairs are used for subsequent model training and performance prediction.

[0073] By integrating multiple information sets into structured data entries, data consistency and comparability are ensured, laying a solid foundation for subsequent model training. Converting material formulas, lighting conditions, and printing parameters into feature vectors enables machine learning models to effectively process these multivariate data, thereby improving prediction accuracy. By assigning each feature vector a performance label based on experimental measurements, it can be directly used to train a multi-layer neural network, enabling the model to learn the complex relationship between material properties and input conditions. Standardized training sample pairs can improve the learning efficiency of the model, allowing the prediction model to better generalize to new experimental conditions, thereby providing reliable performance predictions in practical applications.

[0074] S5: Based on the prepared training samples, design and train a multi-layer neural network structure to predict the performance of light-curable materials. Use the trained neural network structure to input new formula data to evaluate its performance under specific lighting conditions; the details are as follows:

[0075] The prepared training sample pair (X, Y) is input into a pre-designed multi-layer neural network, where the input layer receives the feature vector X and the output layer produces the performance prediction That is, the predicted material performance value; using the loss function To quantify the predicted value The difference between the actual label Y and the actual label Y, the loss function is defined as Represents the square loss of the prediction error; based on the calculated loss, the weights W of each layer in the network are updated through the back propagation algorithm to minimize the loss function, and the iterative optimization is carried out until the network can accurately predict the performance of the photocurable material.

[0076] Create feature vector X for new recipe data new , where X new =[x 1,new ,x 2,new ,x 3,new ,...,x n,new ], x 1,new Indicates the information of the new recipe, x 2,new Indicates the corresponding lighting conditions, x 3,new Indicates the printing parameters that match it; the constructed feature vector X new Input into the trained neural network to obtain the predicted performance output in Reflects the expected performance of new materials under specific lighting conditions;

[0077] based on Compare the goodness of fit R between the predicted performance and the actual test results using the formula where Y actual is the performance value obtained from actual testing. It's Y actual The average value of .

[0078] By training a multi-layer neural network, we can learn the complex relationships between material properties and formulations, lighting conditions, and printing parameters, thereby improving prediction accuracy. The neural network model can capture nonlinear relationships, making the prediction results closer to reality.

[0079] Using trained neural networks, we can quickly evaluate the performance of new materials under specific conditions, reducing the need for trial and error and accelerating the material development cycle. By assessing the agreement between predicted performance and actual test results, we can ensure the reliability of the prediction model, thereby improving the performance consistency of the final product. By reducing the number of unnecessary experimental verifications, we effectively reduce the cost of material development and improve economic benefits.

[0080] S6: Based on the evaluation results, adjust the printing parameters to form an optimized printing parameter set. Apply the optimized printing parameters to the new light-curing material performance prediction and verify the actual performance of the material under the improved printing conditions. The details are as follows:

[0081] Analyze the performance difference ΔP in the evaluation results, Determine the part of the printing parameters that has the greatest impact on performance; based on the identified parameters with the greatest impact p max , apply the fine-tuning formula p′ max =p max +Δp, where Δp is the increment adjusted based on the performance difference ΔP to optimize the printing parameters; using the optimized parameters p′ max , reconfigure the printing parameter set P′ and predict the performance again through the neural network.

[0082] Use the optimized printing parameter set P′ to construct a new training sample (X′, Y′), where X′ contains the adjusted printing parameters and other relevant variables; input the new training sample X′ into the previously trained multi-layer neural network to obtain the performance prediction value under the improved printing condition

[0083] Comparison of predicted values Compared with the actual test results Y′, the performance improvement index I is calculated using the formula Where Y is the performance value measured under original conditions. The performance improvement index II is used to evaluate whether the optimized printing parameters significantly improve the material performance.

[0084] By analyzing the performance difference ΔP, the key printing parameters that influence material performance are identified and adjusted to optimize the printing parameter set. This helps improve the material's performance under specific printing conditions. By reconstructing the training samples and performing predictions using the optimized printing parameter set, the model's performance in real-world applications can be verified, ensuring that the model accurately reflects the optimized material properties.

[0085] Optimized printing parameters ensure that the material achieves the expected performance level under different printing conditions, thereby improving the quality and reliability of the final product. By continuously adjusting printing parameters based on evaluation results, the optimal printing conditions can be quickly found, reducing unnecessary experiments and accelerating new product development.

[0086] S7: Based on actual performance data, feedback is provided to adjust the multi-layer neural network to improve prediction accuracy, and the algorithm is continuously iterated until the desired prediction accuracy is achieved. The details are as follows:

[0087] The actual performance data Y″ and the corresponding predicted value collected A new training sample pair (X″, Y″) is formed, where Y″ refers to the performance data of the photocurable material obtained through actual testing or experiments after adopting the optimized printing parameter set P′. It refers to the results obtained by using a trained multi-layer neural network to predict the performance of new light-curing materials under the same conditions;

[0088] Using the new training sample pair (X″, Y″), the weight W of the multi-layer neural network is updated by the gradient descent method. The update rule is: Where η is the learning rate, is the gradient of the loss function with respect to the weights;

[0089] According to the updated weight W new , re-evaluate the prediction accuracy A of the neural network, using the formula in is the new predicted value after updating the weights, The average value of is calculated and iterated continuously until the prediction accuracy meets the predetermined standard.

[0090] By collecting actual performance data and predicted values, new training sample pairs are formed. These pairs are then used to update the neural network weights, enabling the model to better fit the actual data and thus improve prediction accuracy. Through continuous iteration and weight updates, the neural network can gradually approach its optimal state, continuously improving prediction accuracy until the desired accuracy standard is reached. As actual performance data continues to increase, the model can learn more real-world application scenarios, improving its generalization ability under different conditions and enhancing its robustness.

[0091] In summary, the present invention collects a first information set of photocurable material formula data, obtains a second information set of illumination condition data corresponding to the material formula, and collects a third information set of 3D printing process parameters set for the illumination conditions. These three information sets are used to prepare training samples, and a multi-layer neural network structure is designed and trained to predict the performance of photocurable materials. The prediction results are evaluated, and the printing parameters are adjusted to form an optimized parameter set. The optimized parameters are then applied to new performance predictions to verify the improved results. Finally, the neural network is adjusted based on feedback from actual performance data to improve prediction accuracy. Through an efficient neural network model, the present invention reduces experimental dependence, shortens the R&D cycle, reduces costs, and improves the quality and production efficiency of 3D printed products.

[0092] 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, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or 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 scope of protection of the present invention.

Claims

1. A method for predicting properties of photocurable materials, characterized in that: The following steps are involved: S1: Collecting a first information set including photocurable material recipe data, including the following sub-steps: Quantify the proportion of each component in the first information set, using linear combination ,in represents the proportion of the i-th material, is the corresponding weight coefficient; Using quantified Value, calculate the corresponding material mixture density , through the formula ,in is the known density of the i-th material; According to the calculated density , evaluate the basic physical properties of material mixtures; S2: Based on the first information set, obtaining a second information set corresponding to the lighting condition data, wherein the second information set is directly associated with the material formula in the first information set, including the following sub-steps: For each light-curable material formula in the first information set, record its light intensity under a specific light source and photoperiod , forming a second information set, where light intensity and time are parameters that directly affect material curing; Calculate the photopolymerization rate constant based on the recorded illumination conditions , using the formula ,in Indicates the light transmittance under given lighting conditions; Using rate constants , predict the degree of material curing under different lighting conditions , the degree of curing is determined by the formula to determine; S3: Based on the second information set, collecting a third information set related to 3D printing process parameters, ensuring that these parameters are set for the lighting conditions in the second information set, including the following sub-steps: For each lighting condition in the second information set, record the nozzle speed during 3D printing , layer thickness and filling density Parameters, constituting a third information set; According to the nozzle speed and layer thickness , calculate the single layer printing time , using the formula ,in It's the layer length. It is the time that the nozzle waits before printing a new layer. Match the curing time under lighting conditions; Using the obtained single layer printing time , combined with the packing density , evaluate the actual curing quality of each layer of material under given printing parameters , through the formula Calculate, where is a curing time determined based on the second information set; S4: preparing training samples using the data obtained from the first to third information sets, each sample covering a recipe, a lighting condition, and a printing parameter; S5: Based on the prepared training samples, a multi-layer neural network structure is designed and trained to predict the performance of light-curable materials. The trained neural network structure is used to input new formulation data to evaluate its performance under specific lighting conditions. S6: Based on the evaluation results, adjust the printing parameters to form an optimized printing parameter set. Apply the optimized printing parameters to the new light-curing material performance prediction and verify the actual performance of the material under the improved printing conditions. S7: Based on actual performance data, feedback is provided to adjust the multi-layer neural network to improve prediction accuracy, and the iteration continues until the expected prediction accuracy is achieved.

2. The method for predicting properties of a photocurable material according to claim 1, wherein: In S4, the following sub-steps are included: Integrate the material formula information in the first information set, the lighting condition data in the second information set, and the 3D printing parameters in the third information set to form a structured data entry; Based on the data entry, define the feature vector of each training sample ,in , Indicates the material formula, Indicates the lighting conditions, Indicates printing parameters; For each eigenvector , assign corresponding performance labels , the label is based on the material properties obtained by experimental measurement, forming a training sample pair , used for subsequent analysis.

3. The method for predicting properties of a photocurable material according to claim 2, wherein: In S5, the following sub-steps are included: Prepare the training samples Input into a pre-designed multi-layer neural network, where the input layer receives the feature vector , the output layer produces performance predictions ; Using loss function To quantify the predicted value With actual label The difference between the two, the loss function is defined as , represents the square loss of prediction error; Based on the calculated loss, the weights of each layer in the network are updated through the back-propagation algorithm , so that the loss function is minimized, and the optimization is iteratively performed until the network can accurately predict the properties of the light-curing material.

4. The method for predicting properties of a photocurable material according to claim 3, wherein: In S5, the following sub-steps are included: Create feature vectors for new recipe data ,in , Indicates information about new recipes. Indicates the corresponding lighting conditions, Indicates the printing parameters that match it; The constructed feature vector Input into the trained neural network to obtain the predicted performance output ,in Reflects the expected performance of new materials under specific lighting conditions; based on Compare the predicted performance with the actual test results , using the formula ,in is the performance value obtained from actual testing. yes The average value of .

5. The method for predicting properties of a photocurable material according to claim 4, wherein: In S6, the following sub-steps are included: Analyze performance differences in evaluation results ΔP= ,determine the part of printing parameters that has the greatest impact on performance; Based on the identified most influential parameters , apply the fine-tuning formula ,in Based on performance differences Increments of adjustment to optimize printing parameters; Using optimized parameters , reconfigure the printing parameter set and predict the performance again through the neural network.

6. The method for predicting properties of a photocurable material according to claim 5, wherein: In S6, the following sub-steps are included: Use optimized printing parameter set Construct new training samples ,in Contains adjusted printing parameters and other relevant variables; The new training samples Input into a previously trained multi-layer neural network to obtain performance predictions under improved printing conditions '; Comparison of predicted values ′ and the actual test result Y′, calculate the performance improvement index , using the formula ,in It is the performance value measured under original conditions.

7. The method for predicting properties of a photocurable material according to claim 6, wherein: In S7, the following sub-steps are included: Actual performance data collected and the corresponding predicted values , forming a new training sample pair ,in, It refers to the performance data of the light-curing material obtained through actual testing or experiments after adopting the optimized printing parameter set P'. It refers to the results obtained by using a trained multi-layer neural network to predict the performance of new light-curing materials under the same conditions; Using new training samples , update the weights of the multi-layer neural network by gradient descent , the update rule is ,in is the learning rate, is the gradient of the loss function with respect to the weights; According to the updated weight , re-evaluate the prediction accuracy of neural networks , using the formula ,in is the new predicted value after updating the weights, The average value of is calculated and iterated continuously until the prediction accuracy meets the predetermined standard.

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