A Low-Cost Vision-Based Monitoring of Computer Numerical Control (CNC) Machine Tools for Small and Medium-Sized Enterprises (SMEs)

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 20; p. 4506
• Main Authors: Kim, Hyungjung, Jung, Woo-Kyun, Choi, In-Gyu, Ahn, Sung-Hoon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 17.10.2019; MDPI
• Subjects: Automation; Clinical decision making; Decision making; Deep learning; Energy consumption; Factories; Flooring; Internet of Things; Low cost; Machine learning; machine tool monitoring; Machine tools; Manufacturing; Milling machines; Monitoring; Monitoring systems; open-source software; optical character recognition; Optimization; Productivity; Sensors; Small & medium sized enterprises-SME; small and medium-sized enterprises; Small business; smart factory; Strain gauges; Three axis; Vision systems; Webcams
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19204506

In the new era of manufacturing with the Fourth Industrial Revolution, the smart factory is getting much attention as a solution for the factory of the future. Despite challenges in small and medium-sized enterprises (SMEs), such as short-term strategies and labor-intensive with limited resources, they have to improve productivity and stay competitive by adopting smart factory technologies. This study presents a novel monitoring approach for SMEs, KEM (keep an eye on your machine), and using a low-cost vision, such as a webcam and open-source technologies. Mainly, this idea focuses on collecting and processing operational data using cheaper and easy-to-use components. A prototype was tested with the typical 3-axis computer numerical control (CNC) milling machine. From the evaluation, availability of using a low-cost webcam and open-source technologies for monitoring of machine tools was confirmed. The results revealed that the proposed system is easy to integrate and can be conveniently applied to legacy machine tools on the shop floor without a significant change of equipment and cost barrier, which is less than $500 USD. These benefits could lead to a change of monitoring operations to reduce time in operation, energy consumption, and environmental impact for the sustainable production of SMEs.