In Situ Material Identification, Machining Monitoring, and Cloud Logging Integrated with AI and Machine Vision

Current process monitoring in machining hampers manufacturing efficiency due to manual data entry, fragmented systems, and lack of real-time data capture. This challenge is particularly tough for small to medium-sized enterprises (SMEs) without advanced tools. Our proposed methodology employs a collaborative robot (UR16e) with a machine vision system (InSight 7802C). The robot shifts the vision system to capture images of machining processes and workpieces. Enhanced with a fine-tuned Support Vector Machine (SVM) classifier, the vision system accurately identifies Aluminum, Copper, Mild Steel, and Medium-density fibreboard (MDF). Operational data, including material types and machine usage, is locally recorded and uploaded to Microsoft Azure cloud storage for data integrity and stakeholder transparency. This approach enhances process control and decision-making. Preliminary results validate its practicality and potential to boost manufacturing process monitoring. It sets a solid foundation for further exploration into smart manufacturing research, which is crucial for staying competitive in today’s rapidly evolving industrial landscape.