초록

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

키워드

Injection molding, Process parameter, Product quality, Artificial neural network, Single-task learning Multi-task learning

참고문헌(8)open

  1. [단행본] Rosato, D. V / 2012 / Injection Molding Handbook / Springer Science & Business

  2. [학술지] Fernandes, C / 2018 / Modeling and optimization of the injection molding process: A review / Adv. Polym. Technol 37 (2) : 429 ~ 449

  3. [학술지] Shen, C / 2007 / Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method / J. Mater. Process. Technol 183 (2-3) : 412 ~ 418

  4. [학술지] Zink, B / 2017 / Enhanced injection molding simulation of advanced injection molds / Polymers 2017 9 (2) : 1 ~ 11

  5. [학술지] Hentati, F / 2019 / Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation / Int. J. Adv. Manuf. Technol 104 : 4353 ~ 4363

  6. [학술지] Abdul, R / 2020 / Shrinkage prediction of injection molded high polyethylene parts with taguchi/artificial neural network hybrid experimental design / Int. J. Interact. Des. Manuf 14 : 345 ~ 357

  7. [학술지] Heinisch, J / 2021 / Comparison of design of experiment methods for modeling injection molding experiments using artificial neural networks / J. Manuf. Processe 61 : 357 ~ 368

  8. [학술지] hang, Y. / 2018 / An overview of multi-task learning / Natl. Sci. Rev 5 : 30 ~ 43