Investigation of transfer learning with changing machine, mold and material combinations in injection molding
One disadvantage of machine learning for quality prediction in injection molding is that when the machine, mold or material used changes, new data must be collected to build accurate prediction models. One solution is to use transfer learning, where pre-trained models are reused and adapted to new problems. The objective of this study was to evaluate the benefits of transfer learning. Therefore, two injection molding machines, two molds and two materials were used. Five transfer learning methods were evaluated, resulting in 35 models. It could be shown that the application of the transfers led to an overall higher prediction performance.
Preview
Cite
Citation style:
Could not load citation form.
Rights
License Holder:
© 2021 The Authors
Use and reproduction:
This work may be used under a
Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 License (CC BY-NC-ND 4.0)
.