1 |
Rosato, D. V. and Rosato, M. G., "Injection Molding Handbook", Springer Science & Business, Germany, 2012.
|
2 |
Fernandes, C., Pontes, A. J., Viana, J. C., and Gaspar Cunha, A., "Modeling and optimization of the injection molding process: A review", Adv. Polym. Technol. Vol. 37(2), pp. 429 449, 2018.
|
3 |
Zink, B., Szabo, F., Hatos, I., Suplicz, A., Kovacs, N. K., Hargitai, H., Tabi, T., and Kovacs, J. G. "Enhanced injection molding simulation of advanced injection molds", Polymers 2017, Vol. 9(2), pp. 1-11. 2017.
DOI
|
4 |
Hentati, F., Hadriche, I., Masmoudi, N., and Bradai, C., "Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation", Int. J. Adv. Manuf. Technol. Vol. 104, pp. 4353-4363. 2019.
DOI
|
5 |
Heinisch, J., Lockner, Y., and Hopmann, C., "Comparison of design of experiment methods for modeling injection molding experiments using artificial neural networks", J. Manuf. Processe. Vol. 61, pp. 357-368. 2021.
DOI
|
6 |
Zhang, Y., and Yang, Q., "An overview of multi-task learning", Natl. Sci. Rev. 5, 30-43. 2018.
DOI
|
7 |
Shen, C., Wang, L., and Li, Q., "Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method", J. Mater. Process. Technol. Vol. 183(2-3), pp. 412-418. 2007.
DOI
|
8 |
Abdul, R., Guo, G., Chen, J. C., and Yoo, J. J. W., "Shrinkage prediction of injection molded high polyethylene parts with taguchi/artificial neural network hybrid experimental design", Int. J. Interact. Des. Manuf. Vol. 14, pp. 345-357. 2020.
DOI
|