Abstract
Lightweight metals have been used to manufacture the body panels of cars to reduce the weight of car bodies. Typically, aluminum sheets are welded together, with a focus on weld quality assurance. A weld quality prediction system for the laser welding of aluminum was developed in this research to maximize welding production. The behavior of the plasma was also analyzed, dependent on various welding conditions. The light intensity of the plasma was altered with heat input and wire feed rate conditions, and the strength of the weld and sensor signals correlated closely for this heat input condition. Using these characteristics, a new algorithm and program were developed to evaluate the weld quality. The design involves a combinatory algorithm using a neural network model for the prediction of tensile strength from measured signals and a fuzzy multi-feature pattern recognition algorithm for the weld quality classification to improve predictability of the system.
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Jiyoung Yu received his B.S. and Ph.D. in Mechanical Engineering from Hanyang University, Korea, in 2007 and 2013. He has worked for Hyundai Steel Company in Hyundai Motors Group as a Principal Research Engineer from 2013 to 2016. His research interests are welding and joining technologies for materials in automotive.
Yongho Sohn received his B.S. and M.S. from Worcester Polytechnic Institute and Ph.D. in School of Materials Engineering from Purdue University, U.S.A. in 1991, 1993 and 2000. He has worked for University of Central Florida in Orlando, U.S.A as a Professor of department of material science and engineering from 2001 and advanced materials processing and analysis center (AMPAC) as a Director from 2006. His research interests are failure mechanisms, non-destructive inspection (NDI) techniques, and improved durability/reliability of thermal barrier coatings (TBCs).
Young Whan Park received his B.S., M.S. and Ph.D. in Mechanical Engineering from Hanyang University, Korea, in 1999, 2001 and 2006. He worked in POSCO research center and now he is working in Pukyong National University as an Associate Professor form 2008. His research interests are automation and monitoring of welding and joining process with artificial intelligent, and laser material processing.
Jae-Seob Kwak received his M.S. and Ph.D. in Precision Mechanical Engineering from Pusan National University, Korea, in 1996 and 2000. He is working in the Department of Mechanical Engineering, Pukyong National University, Korea. He awarded the Outstanding Teaching Award at Pukyong National University in 2013, and Most Cited Article Award at International Journal of Precision Engineering and Manufacturing in 2012.
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Yu, J., Sohn, Y., Park, Y.W. et al. The development of a quality prediction system for aluminum laser welding to measure plasma intensity using photodiodes. J Mech Sci Technol 30, 4697–4704 (2016). https://doi.org/10.1007/s12206-016-0940-9
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DOI: https://doi.org/10.1007/s12206-016-0940-9