An Intelligent Manufacturing Approach Based on a Novel Deep Learning Method for Automatic Machine and Working Status Recognition

Abstract

Smart manufacturing uses robots and artificial intelligence techniques to minimize human interventions in manufacturing activities. Inspection of the machine’ working status is critical in manufacturing processes, ensuring that machines work correctly without any collisions and interruptions, e.g., in lights-out manufacturing. However, the current method heavily relies on workers onsite or remotely through the Internet. The existing approaches also include a hard-wired robot working with a computer numerical control (CNC) machine, and the instructions are followed through a pre-program path. Currently, there is no autonomous machine tending application that can detect and act upon the operational status of a CNC machine. This study proposes a deep learning-based method for the CNC machine detection and working status recognition through an independent robot system without human intervention. It is noted that there is often more than one machine working in a representative industrial environment. Therefore, the SiameseRPN method is developed to recognize and locate a specific machine from a group of machines. A deep learning-based text recognition method is designed to identify the working status from the human–machine interface (HMI) display.