Key Technologies of Intelligentized Welding Manufacturing: The Spectral Diagnosis Technology for Pulsed Gas Tungsten Arc Welding of Aluminum Alloys

Author:   Yiming Huang ,  Shanben Chen
Publisher:   Springer Verlag, Singapore
Edition:   1st ed. 2020
ISBN:  

9789811375484


Pages:   173
Publication Date:   14 May 2019
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Key Technologies of Intelligentized Welding Manufacturing: The Spectral Diagnosis Technology for Pulsed Gas Tungsten Arc Welding of Aluminum Alloys


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Overview

This book presents the recent research results of the application of arc spectrum in the welding process. It sheds light on the fundamentals of monitoring welding quality using arc spectral information. By analyzing the topic both from a global and local perspective, it establishes a knowledge base of features characterizing welding statuses. Researchers, scientists and engineers in the field of intelligent welding can benefit from the book. As such, this book provides valuable knowledge, useful methods, and practical algorithms that are applicable in real-time detection of welding defects.

Full Product Details

Author:   Yiming Huang ,  Shanben Chen
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2020
Weight:   0.454kg
ISBN:  

9789811375484


ISBN 10:   9811375488
Pages:   173
Publication Date:   14 May 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Introduction.- Monitoring principle of welding process based on arc emission spectrum.- Experimental setup made of welding and spectral acquisition subsystems.- Prediction of welding status based on features extracted from the whole arc spectrum.- Prediction of welding status based on features extracted from spectral lines of interest.- The control of welding quality based on spectral features.- Summary and conclusions.

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Author Information

Dr. Yiming  Huang received his B.S. degree and Ph.D. degree in Materials Processing Engineering from the School of Materials Science and Engineering, Shanghai Jiao Tong University, in 2011 and 2017 respectively. He joined the School of Materials Science and Engineering, Tianjin University in 2017, and he is currently as a lecturer. His research interests include welding process modeling, machine learning, and defect detection. He has published several papers in international conferences and reputed journals in his research area. Dr. Shanben Chen received his BS degree in industrial automation from Dalian Railway Institute (Dalian Jiao Tong University) in 1982, and received his MS and PhD in control theory and application from Harbin Institute of Technology, China, in 1987 and 1991, respectively. He worked as a postdoctoral fellow at the National Key Laboratory of Advanced Welding Production of China in Harbin Institute of Technology (HIT) from 1993 to 1995, and as a professor from 1995 to 2000. From 2000 to present, he has served as the Special Professor, Cheung Kong Scholar Program of the Ministry of Education of China & Li Ka Shing Foundation, Hong Kong, and engaged at Shanghai Jiao Tong University, China, where he is also director of the Intelligentized Robotic Welding Technology Laboratory. Prof. Chen has also been a visiting professor at the University of Western Sydney (UWS) in connection with the ARC Linkage collaboration since 2009. Currently, Prof. Chen is a senior member of the IEEE; a member of the American Welding Society; Chair of the Robotics & Automation Committee of the Chinese Welding Society (CWS); Deputy Secretary-General of the Chinese Welding Society; and a standing member of the Board of Directors, CWS.

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