The Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore campus will organize a one day Symposium on Recent Trends in Signature Analysis of ARC WELDING on March 6, 2017.
Welding is one of the major metals joining process employed in industries for various fabrication purposes from small scale to a mass production. In an automated environment, developing a process monitoring and control system will ensure the weld quality and prevent the occurrence of defects. Signature analysis is the term which is used to correlate the condition of the weld and its current and voltage flow.
The signature of the current and voltage will get disturbed when a disturbance is introduced intentionally or unintentionally to the otherwise stable process leading to defective weld during the process. By monitoring and identifying the variations in the signature of current and voltage by using appropriate sensors,weld defects can be identified in real time using high speed data acquisition system. Off late, machine learning algorithms are becoming popular for developing a real-time weld monitoring system.
Practicing engineers from industry, Faculty members form academic institutions, Research scholars, Graduate & undergraduate students would like to pursue project work in the area of welding / signature analysis.
Interested external candidates can send the registration through email. Internal candidates are requested to register their names with organizing secretaries.
Professor, Leibniz University Hannover, Germany
Dr. Dietrich Rehfeldt is the scientific and technical consultant, coordinator, adviser and member of international research projects for Ukraine, Germany, India and China in the field of data acquisition and processing, on-line process monitoring, process simulation, evaluation of power sources, evaluation of consumables, and application of Artificial Intelligence.
Former AddL GM, Welding Research Institute (WRI) BHEL, Trichirapalli and Adjunct Professor, NOT, Trichy and Anna University
Dr. A. Raja has done his Ph.D. from IISc -Bangalore and was with Welding Research Institute (WRI), BHEL Trichy for a long time. He has several publications in National and International journals and having 20 patents in his name. He is also a consultant and advisor to many industries and organizations
Professor, Albstadt-Sigmaringen University, Germany
Dr. Markus Rehfeldt is a professor at the University of Albstadt-Sigmaringen, Germany. His research interests include CIS, information management, robotics and business informatics. He is a member of the Arbeitsgemeinschaft Fuzzy-Logic and Soft computing Norddeutschland (AFN).
Professor, Amrita School of Engineering, Coimbatore
Dr. K.I. Ramachandran currently serves as Professor at Department of Mechanical Engineering, School of Engineering and Professor at Center for Computational Engineering and Networking (CEN), Coimbatore Campus. His research interest includes Fault Diagnosis & Machine Learning and Signal processing He is instrumental in several funded project in the area of Fault Diagnosis & Machine Learning and also had good number of research papers in national and international journals.
Amrita School of Engineering (ASE), Coimbatore, established by Mata Amritanandamayi Math, is located on a sprawling 500 acre campus at the foot-hills of the Western Ghats. The school of Engineering was started in 1994 and is one of the engineering colleges under Amrita Vishwa Vidyapeetham. Amrita Vishwa Vidyapeetham is accredited with the highest grade of 'A' by the National Assessment and Accreditation Council (NAAC). Ministry of Human Resources Development, Govt. of India, listed Amrita Vishwa Vidyapeetham in category “A” along with 37 other premier institutions like IISc, Bangalore
The Department of Mechanical Engineering of ASE started in the year 1994 and offers B.Tech programme in Mechanical Engineering and M.Tech programmes in Engineering Design, Manufacturing Engineering and Automobile Engineering. The Department also offers PhD programme .
Integrated Machine Health Monitoring (IMHM) is one of the research groups in the Department of Mechanical Engineering. This research group is working in the field of machine learning based on condition monitoring.This application area includes,weld quality control,tool wear monitoring,prediction of surface roughness,rotating machinery fault diagnosis etc.,using signal and image processing techniques.