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 process

In this study, an artificial neural network model was constructed to convert CAE analysis data into similarexperimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through thedesign of experiment and random selection method. The injection molding conditions and the weight, height, and diameterof the product derived from CAE results were used as the input parameters for learning of the convert model. Also theproduct qualities of experimental results were used as the output parameters for learning of the convert model. Theaccuracy 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. Anartificial neural network model was constructed to predict the quality of injection molded product by using convertedsimilar experimental data and injection molding experiment data. The injection molding conditions were used as inputparameters for learning of the predicted model and weight, height, and diameter of the product were used as outputparameters 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 similarexperimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through thedesign of experiment and random selection method. The injection molding conditions and the weight, height, and diameterof the product derived from CAE results were used as the input parameters for learning of the convert model. Also theproduct qualities of experimental results were used as the output parameters for learning of the convert model. Theaccuracy 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. Anartificial neural network model was constructed to predict the quality of injection molded product by using convertedsimilar experimental data and injection molding experiment data. The injection molding conditions were used as inputparameters for learning of the predicted model and weight, height, and diameter of the product were used as outputparameters 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.