Multiple Sensing-Driven Feature Extraction and Defect Detection - Hardcover

Über die Autorin bzw. den Autor

Dr. Zhifen Zhang is Associate Professor in Department of Mechanical Engineering, Xi’an Jiaotong University (XJTU), Shaanxi, China. She has authored more than ten magazine articles, including those published in IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, Mechanical Systems and Signal Processing, and Journal of Materials Processing Technology. Her research interests include intelligent manufacturing, monitoring and defect prediction of the welding process, laser shock peening, multisensory data fusion and modeling, mechanical Fault detection and detection. 

Dr. Guangrui Wen is currently a Full Professor of the School of Mechanical Engineering and the Executive Dean of the School of International Education in XJTU, China. He is also the Vice Dean of the Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, and the Vice Dean of National & Local Joint Engineering Research Center for Equipment Operation Safety Assurance and Intelligent Monitoring, China. Dr. Wen is a member of IEEE, the Chinese Mechanical Engineering Society (CIME), the Chinese Society for Vibration Engineering (CSVE), and the Executive Director and Deputy Secretary-General of the Dynamic Test Professional Committee of CSVE.

Dr.Wenjing Ren is currently an associate researcher at the School of Materials Science and Engineering, Tianjin University, Tianjin, China. She has published more than 30 academic papers in journals including IEEE Transactions on Industrial Informatics, Additive Manufacturing, Materials Science and Engineering: A. Her research interests include automatic welding, in-situ monitoring and defect prediction of welding, and metal additive manufacturing.

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This book reports the recent study results in applying online signals and artificial intelligence for welding and laser additive manufacturing (AM). It provides insights on the fundament of quality monitoring and intelligent defect detections for welding and laser AM using arc sound and plasma spectral signals collected during the manufacturing processes. Analyzing respectively from a global and local perspective establishes a knowledge base of features characterizing welding statuses. Researchers, scientists, and engineers in intelligent welding and laser AM can benefit from the book. As such, this book provides valuable knowledge, valuable methods, and practical algorithms that can be considered in real-time defect detection for welding and laser AM.