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Condition Monitoring and Control for Intelligent Manufacturing

:	Lihui Wang, Robert X. Gao
:	Lihui Wang, Robert X Gao
:	Condition Monitoring and Control for Intelligent Manufacturing
:	Springer-Verlag
:	9781846282690
:	1
:	CHF 287.50
:

:	Allgemeines, Lexika
:	English

:	400
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

Condition modelling and control is a technique used to enable decision-making in manufacturing processes of interest to researchers and practising engineering.Condition Monitoring and Control for Intelligent Manufacturing will be bought by researchers and graduate students in manufacturing and control and engineering, as well as practising engineers in industries such as automotive and packaging manufacturing.

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 have been 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.

	Preface	6
	Contents	10
	List of Contributors	18
	1 Monitoring and Control of Machining --- A. Galip Ulsoy	21
	1.1 Introduction	21
	1.2 Machining Processes	26
	1.3 Monitoring	30
	1.3.1 Tool Failure	30
	1.3.2 Tool Wear	32
	1.4 Servo Control	35
	1.5 Process Control	37
	1.6 Supervisory Control	43
	1.7 Concluding Remarks	45
	Acknowledgment	47
	References	47
	2 Precision Manufacturing Process Monitoring with Acoustic Emission --- D.E. Lee, Inkil Hwang, C.M.O. Valente, J.F.G. Oliveira and David A. Dornfeld	53
	2.1 Introduction	53
	2.2 Requirements for Sensor Technology at the Precision Scale	55
	2.3 Sources of AE in Precision Manufacturing	57
	2.4 AE-based Monitoring of Grinding Wheel Dressing	59
	2.4.1 Fast AE RMS Analysis for Wheel Condition Monitoring	60
	2.4.2 Grinding Wheel Topographical Mapping	61
	2.4.3 Wheel Wear Mechanism	62
	2.5 AE-based Monitoring of Face Milling	63
	2.6 AE-based Monitoring of Chemical Mechanical Planarization	64
	2.6.1 Precision Scribing of CMP-treated Wafers	65
	2.6.2 AE-based Endpoint Detection for CMP	66
	2.7 AE-based Monitoring of Ultraprecision Machining	68
	2.7.1 Monitoring of Precision Scribing	68
	2.7.2 Monitoring of Ultraprecision Turning of Single Crystal Copper	69
	2.7.3 Monitoring of Ultraprecision Turning of Polycrystalline Copper	72
	2.8 Conclusions	72
	References	73
	3 Tool Condition Monitoring in Machining --- Mo A. Elbestawi, Mihaela Dumitrescu and Eu-Gene Ng	75
	3.1 Introduction	75
	3.2 Research Issues	76
	3.2.1 Sensing Techniques	77
	3.2.2 Feature Extraction Methods	81
	3.2.3 Decision-making Methods	82
	3.3 Neural Networks for Tool Condition Monitoring	83
	3.3.1 Structure of MPC Fuzzy Neural Networks	84
	3.3.2 Construction of MPC Fuzzy Neural Networks	85
	3.3.3 Evaluation of MPC Fuzzy Neural Networks	86
	3.3.4 Fuzzy Classification and Uncertainties in Tool Condition Monitoring	87
	3.4 Case Studies	88