Condition Monitoring and Control for Intelligent Manufacturing

Manufacturing systems and processes are becoming more complex, so more rational decision-making in process control is a necessity. Better information gathering and analysis techniques are needed and condition  monitoring is seen as a framework that will enable these improvements.

Condition Monitoring and Control for Intelligent Manufacturing brings together the world’s authorities on condition monitoring to provide a broad treatment of the subject accessible to researchers and practitioners in manufacturing industry.

The book presents a review of the key areas of research in machine condition monitoring and control, before focusing on an in-depth treatment of each important technique, from multi-domain signal processing for defect diagnosis to web-based information delivery for real-time control.

Researchers in manufacturing and control engineering, as well as practising engineers in industries from automotive to packaging manufacturing will find this book valuable.

Monitoring and Control of Machining.- Precision Manufacturing Process Monitoring with Acoustic Emission.- Tool Condition Monitoring in Machining.- Monitoring Systems for Grinding Processes.- Condition Monitoring of Rotary Machines.- Advanced Diagnostic and Prognostic Techniques for Rolling Element Bearings.- Sensor Placement and Signal Processing for Bearing Condition Monitoring.- Monitoring and Diagnosis of Sheet Metal Stamping Processes.- Robust State Indicators of Gearboxes Using Adaptive Parametric Modeling.- Signal Processing in Manufacturing Monitoring.- Autonomous Active-sensor Networks for High-accuracy Monitoring in Manufacturing.- Remote Monitoring and Control in a Distributed Manufacturing Environment.- An Intelligent Nanofabrication Probe for Surface Displacement/Profile Measurement.- Smart Transducer Interface Standards for Condition Monitoring and Control of Machines.- Rocket Testing and Integrated System Health Management.

Lihui Wang is a professor of virtual manufacturing at the University of Skövde’s Virtual Systems Research Centre in Sweden. He was previously a senior research scientist at the Integrated Manufacturing Technologies Institute, National Research Council of Canada. He is also an adjunct professor in the Department of Mechanical and Materials Engineering at the University of Western Ontario, and a registered professional engineer in Canada. His research interests and responsibilities are in web-based and sensor-driven real-time monitoring and control, distributed machining process planning, adaptive assembly planning, collaborative design, supply chain management, as well as intelligent and adaptive manufacturing systems. Dr. Robert X. Gao is an Associate Professor of Mechanical Engineering at the University of Massachusetts Amherst, USA. He received his B.S. degree from China, and his M.S. and Ph.D. from the Technical University Berlin, Germany, in 1982, 1985, and 1991, respectively. Since starting his academic career in 1992, he has been conducting research in the general area of embedded sensors and sensor networks, "smart" electromechanical systems, wireless data communication, and signal processing for machine health monitoring, diagnosis, and prognosis. Dr. Gao has published over 100 refereed papers on journals and international conferences, and has one US patent and two pending patent applications on sensing. He is an Associate Editor for the IEEE Transactions on Instrumentation and Measurement, and served as the Guest Editor for the Special Issue on Sensors of the ASME Journal of Dynamic Systems, Measurement, and Control, published in June, 2004. Condition-based Monitoring and Control for Intelligent Manufacturing has arisen from the Flexible Automation and Intelligent Manufacturing (FAIM 2004) conference, held in Toronto, Canada on July12-14 2004. Thirty papers have been selected out of 170 presented at the conference and the authors of these papers havebeen invited to submit extended updated versions of these papers in order to create a state of the art review of condition-based monitoring and control in manufacturing.