Transactions on Intelligent Welding Manufacturing : Volume IV No. 2 2020

Shanben Chen

ISBN 10: 9811961514 / ISBN 13: 9789811961519

Published by Springer Nature Singapore, Springer Nature Singapore, 2023

About this Item

Druck auf Anfrage Neuware - Printed after ordering - The primary aim of this volume is to provide researchers and engineers from both academic and industry with up-to-date coverage of new results in the field of robotic welding, intelligent systems and automation. The book is mainly based on papers selected from the 2020 International Conference onRobotic Welding, Intelligence and Automation (RWIA'2020)in Shanghai and Lanzhou, China. The articles show that the intelligentized welding manufacturing (IWM) is becoming an inevitable trend with the intelligentized robotic welding as the key technology. The volume is divided into four logical parts: Intelligent Techniques for Robotic Welding, Sensing of Arc Welding Processing, Modeling and Intelligent Control of Welding Processing, as well as Intelligent Control and its Applications in Engineering.

Seller Inventory # 9789811961519

Bibliographic Details

• Title: Transactions on Intelligent Welding Manufacturing : Volume IV No. 2 2020
• Publisher: Springer Nature Singapore, Springer Nature Singapore
• Publication Date: 2023
• Language: English
• ISBN 10: 9811961514
• ISBN 13: 9789811961519
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

The primary aim of this volume is to provide researchers and engineers from both academic and industry with up-to-date coverage of new results in the field of robotic welding, intelligent systems and automation. The book is mainly based on papers selected from the 2020 International Conference on Robotic Welding, Intelligence and Automation (RWIA’2020) in Shanghai and Lanzhou, China. The articles show that the intelligentized welding manufacturing (IWM) is becoming an inevitable trend with the intelligentized robotic welding as the key technology. The volume is divided into four logical parts: Intelligent Techniques for Robotic Welding, Sensing of Arc Welding Processing, Modeling and Intelligent Control of Welding Processing, as well as Intelligent Control and its Applications in Engineering.

About the Author

Dr. Shanben Chen (S B 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, SJTU. 

 Currently, Prof. Chen is a senior member of the IEEE; Chair of the Robotics & Automation Committee of the Chinese Welding Society (CWS); and a standing member of the Board of Directors, CWS.

Yuming Zhang - FAWS, FASME, FSME, SIEEE, Professor and James Boyd Professor in Electrical Engineering - has been with the University of Kentucky, Lexington, USA since 1991, and became a Full Professor in 2005. He received his BS and MS degrees in control theory and application from Harbin Institute of Technology (HIT), China, where he completed his PhD degree in welding in 1990. He has published 180 peer-reviewed journal papers and holds 8 US patents. 

Dr. Zhili Feng leads the Materials Joining Team, and is a Distinguished R&D Staff at Oak Ridge National Laboratory, where he manages 10 scientists and supporting staff, conducting both fundamental and applied R&D and pursuing technological innovations for diverse interdisciplinary subjects related to materials joining and materials manufacturing processes, with an annual R&D budget of $15 million.