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A Study on the Prediction of Mass and Length of Injection-molded Product Using Artificial Neural Network  

Yang, Dong-Cheol (Shape Manufacturing R&D Department, Korea Institute of Industrial Technology)
Lee, Jun-Han (Shape Manufacturing R&D Department, Korea Institute of Industrial Technology)
Kim, Jong-Sun (Shape Manufacturing R&D Department, Korea Institute of Industrial Technology)
Publication Information
Design & Manufacturing / v.14, no.3, 2020 , pp. 1-7 More about this Journal
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 input parameters, 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 ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-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.
Keywords
Mass; Length; Optimization; Injection molding; Artificial neural network;
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