An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions
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Automation & Control Systems↗
Engineering topics
Li, Guann-Pyng
Publications and source records attributed to Li, Guann-Pyng.
A contextual sensor system for non-intrusive machine status and energy monitoring
Event-driven contexts in manufacturing occur pervasively as a result of interactions among involved entities such as machines, workers, materials, and environment. One of the primary tasks in smart manufacturing is to derive a context-aware system conveniently incorporating worker knowledge for generating timely actionable intelligence for workers on factory floor and supervisors to respond. In this paper, we propose to design a human-and-machine interaction recognition framework by using a causality concept to collect contextual data for classifications of normal and abnormal machine operations. The causes and effects are between workers and machines for this initial research. To apply the causality to recognize worker interactions, initially a reliable way to identify the states of machines is necessary. The proposed contextual sensor system, consisting of a power meter for measuring machine operation conditions, a visual camera for capturing worker and machine interactions via a finite state machine model, and an algorithm for determining power signatures of individual components via energy disaggregation is implemented on semiconductor fabrication machines (manual or PLC controlled) each with multiple components. The experiment results demonstrate its context extraction capability such as components states and their corresponding energy usage in real time as well as its ability to identify anomalous operation conditions.
47 OTHER INSTRUMENTATION↗
Machine learning-based real-time monitoring system for smart connected worker to improve energy efficiency
Recent advances in machine learning and computer vision brought to light technologies and algorithms that serve as new opportunities for creating intelligent and efficient manufacturing systems. In this study, the real-time monitoring system of manufacturing workflow for the Smart Connected Worker (SCW) is developed for the small and medium-sized manufacturers (SMMs), which integrates state-of-the-art machine learning techniques with the workplace scenarios of advanced manufacturing systems. Specifically, object detection and text recognition models are investigated and adopted to ameliorate the labor-intensive machine state monitoring process, while artificial neural networks are introduced to enable real-time energy disaggregation for further optimization. The developed system achieved efficient supervision and accurate information analysis in real-time for prolonged working conditions, which could effectively reduce the cost related to human labor, as well as provide an affordable solution for SMMs. The competent experiment results also demonstrated the feasibility and effectiveness of integrating machine learning technologies into the realm of advanced manufacturing systems.
42 ENGINEERING↗
Characterizing SOI Wafers By Use Of AOTF-PHI
Developmental nondestructive method of characterizing layers of silicon-on-insulator (SOI) wafer involves combination of polarimetric hyperspectral imaging by use of acousto-optical tunable filters (AOTF-PHI) and computational resources for extracting pertinent data on SOI wafers from polarimetric hyperspectral images. Offers high spectral resolution and both ease and rapidity of optical-wavelength tuning. Further efforts to implement all of processing of polarimetric spectral image data in special-purpose hardware for sake of procesing speed. Enables characterization of SOI wafers in real time for online monitoring and adjustment of production. Also accelerates application of AOTF-PHI to other applications in which need for high-resolution spectral imaging, both with and without polarimetry.
Cheng, Li-Jen↗