Intention learning for decision of machining sequence via Deep Learning

Abstract

In recent years, automation of operations in the manufacturing industry has been actively pursued to reduce production costs. As part of this eff ort, systems have been developed to automatically determine the machining sequence instead of the expert. However, it is difficult to design a rule that completely reproduces the expert's decision. In this study, we propose to acquire rules that can emulate expert decisions via inverse reinforcement learning (IRL). The developed system acquires rules by training a neural network using past manufacturing, which represents the results of the expert's decision.