인공신경망을 활용한 사출성형품의 질량과 치수 예측에 관한 연구
A Study on the Prediction of Mass and Length of Injection-molded Product Using Artificial Neural Network
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This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network(ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The inputparameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANNmodel. The generated training data were normalized to eliminate scale differences between factors to improve theprediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter ofthe ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of theinjection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length,the average deviation of ANN was 0.043 mm
Abstract
This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network(ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The inputparameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANNmodel. The generated training data were normalized to eliminate scale differences between factors to improve theprediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter ofthe ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of theinjection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length,the average deviation of ANN was 0.043 mm
참고문헌 (11)
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