[국내논문]사출성형공정에서 CAE 기반 품질 데이터와 실험 데이터의 통합 학습을 통한 인공지능 품질 예측 모델 구축에 대한 연구 A study on the construction of the quality prediction model by artificial neural intelligence through integrated learning of CAE-based data and experimental data in the injection molding process원문보기
In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection moldin...
In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.
In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.
Park, K. Y., "CAE Analysis and Optimization of Injection Molding for a Mobile Phone Cover", Korean Soc. Manuf. Process Eng., 11(2), pp. 60-65, 2012.
Nam, S. D., "A study on the injection mold design application method of CAE mold analysis data", J. D&M Eng., 13(3), pp. 1-6, 2019.
Sung, S. M. and Jung, S. J., "A study on the motorcycle lear cowl injection molding by CAE analysis", J. D&M Eng., 13(4), pp. 34-39, 2019.
Jong, W. R., Huang, Y. M., Lin, Y. Z., Chen, S. C., Chen, Y. W., "Integrating Taguchi Method and Artificial Neural Network to Explore Machine Learning of Computer Aided Engineering", J. Chin. Inst. Eng., 43(4), pp. 1-11, 2020.
Yang, D. C., Lee, J. H., and Kim, J. S., "A Study on the Prediction of Mass and Length of Injection-molded Product Using Artificial Neural Network", Korean Soc. Manuf. Process Eng., 14(3), pp. 1-7, 2020.
Hwang, S., Han, S. R., and Lee, H., "A Study on the Improvement of Injection Molding Process Using CAE and Decision-tree", J. Korea Acad.- Ind. cooperation Soc., 22(4), pp. 580-586, 2021.
Tercan, H., Guajardo, A., Heinisch, J., Thiele, T., Hopmann, C., and Meisen, T., "Transfer-learning: Bridging the gap between real and simulation data for machine learning in injection molding", Procedia Cirp, 72, pp. 185-190. 2018.
Lee, C., Na, J., Park, K., Yu, H., Kim, J., Choi, K., Park, D., Park, S., Rho, J., and Lee, S., "Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries", Adv. Intell. Syst., 2(10), 2020.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.