Industrial Transfer Learning: Boosting Machine Learning in Production

Abstract

In the field of production, machine learning offers great potentials to develop innovative solutions for optimization or automation. However, it faces challenges with regard to the availability of data and the high training effort of the learning models in the event of changes in the production process. In this paper, we address these challenges by introducing deep transfer learning for production. We demonstrate its potentials and benefits in a real application for predictive quality in injection molding and propose a novel approach for the continual training of neural networks across manufactured products. By creating a neural network that leverages knowledge from previous products without forgetting them, the approach shows better learning rates and more accurate predictions while requiring much less data for training. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> to reproduce our results and build upon them.