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A Study on the Stability Control of Injection-molded Product Weight using Artificial Neural Network

인공신경망을 이용한 사출성형품의 무게 안정성 제어에 대한 연구

  • Lee, Jun-Han (Res. Inst. of Adv. Manuf. & Mater. Technol. Shape Manuf. R&D Dept, Korea Inst. of Ind. Technol.) ;
  • Kim, Jong-Sun (Res. Inst. of Adv. Manuf. & Mater. Technol. Shape Manuf. R&D Dept, Korea Institute of Industrial Technology)
  • 이준한 (한국생산기술연구원 형상제조연구부문) ;
  • 김종선 (한국생산기술연구원 형상제조연구부문)
  • Received : 2020.07.23
  • Accepted : 2020.08.26
  • Published : 2020.10.31

Abstract

In the injection molding process, the controlling stability of products quality is a very important factor in terms of productivity. Even when the optimum process conditions for the desired product quality are applied, uncontrollable external factors such as ambient temperature and humidity cause inevitable changes in the state of the melt resin, mold temperature. etc. Therefore, it is very difficult to maintain prodcut quality. In this study, a system that learns the correlation between process variables and product weight through artificial neural networks and predicts process conditions for the target weight was established. Then, when a disturbance occurs in the injection molding process and fluctuations in the weight of the product occur, the stability control of the product quality was performed by ANN predicting a new process condition for the change of weight. In order to artificially generate disturbance in the injection molding process, controllable factors were selected and changed among factors not learned in the ANN model. Initially, injection molding was performed with a polypropylene having a melt flow index of 10 g/10min, and then the resin was replaced with a polypropylene having a melt floiw index of 33 g/10min to apply disturbance. As a result, when the disturbance occurred, the deviation of the weight was -0.57 g, resulting in an error of -1.37%. Using the control method proposed in the study, through a total of 11 control processes, 41.57 g with an error of 0.00% in the range of 0.5% deviation of the target weight was measured, and the weight was stably maintained with 0.15±0.07% error afterwards.

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