Artificial Neural Network를 이용한 사출압력과 사출성형품의 무게 예측에 대한 연구
A study on the prediction of injection pressure and weight of injection-molded product using Artificial Neural Network
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This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was %. In case of maximum injection pressure, error of the ANN was %. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.
Abstract
This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was %. In case of maximum injection pressure, error of the ANN was %. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.
참고문헌 (4)
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- Wen-Chin Chen, "Process Parameters Optimization for Multiple Quality Characteristics in Plastic Injection Molding using Taguchi Method, BPNN, GA, and Hybrid PSO-GA", International Journal of Precision Engineering and Manufacturing, 15(8), pp.1583~1593 (2014), 한국정밀공학회
- Li, K., "Multi-Objective Optimization of the Fiber-reinforced Composite Injection Molding Process using Taguchi method, RSM, and NSGA-II", Simul. Modell. Pract. Theory, 91, pp.69~82 (2019)