Statistical Process Monitoring for IoT-Enabled Cybermanufacturing: Opportunities and Challenges

Initiated from services and consumer products industries, there is a growing interest in using Internet of Things (IoT) technologies in various industries. In particular, IoT-enabled cybermanufacturing starts to draw increasing attention. Because IoT devices such as IoT sensors are usually much cheaper and smaller than the traditional sensors, there is a potential for instrumenting manufacturing systems with massive number of sensors. The premise is that the big data subsequently collected from IoT sensors can be utilized to advance manufacturing. Therefore, data-driven statistical process monitoring (SPM) is expected to contribute significantly to the advancement of cybermanufacturing. In this work, the state-of-the-art in cybermanufacturing is reviewed; an IoT-enabled manufacturing technology testbed (MTT) was built to explore the potential of IoT sensors for manufacturing, as well as to understand the characteristics of data produced by the IoT sensors; finally, the potentials and challenges associated with big data analytics presented by cybermanufacturing systems is discussed; and we propose statistics pattern analysis (SPA) as a promising SPM tool for cybermanufacturing.