Virtual Operators with Self and Transfer Learning Ability in EDM

Abstract

Increasing the manufacturing resource efficiency requires pushing the process limits by customizing the machining parameters to the job at hand. Such optimizations performed usually based on empirical methods and experience due to the sheer complexity of physical modeling. Current research presents a system comprised of advanced machine learning methods, where model-based optimization employed to simultaneously create a digital twin and optimize the process as a self-learning virtual operator using a case study with the EDM processes.