Reference : Artificial neural network to predict the weld status in laser welding of copper to al...
Scientific congresses, symposiums and conference proceedings : Paper published in a journal
Engineering, computing & technology : Multidisciplinary, general & others
Physics and Materials Science
http://hdl.handle.net/10993/50215
Artificial neural network to predict the weld status in laser welding of copper to aluminum
English
Mathivanan, Karthik mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
Plapper, Peter mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE) >]
2021
Procedia CIRP
Elsevier
Yes (verified by ORBilu)
International
2212-8271
Netherlands
9th CIRP Global Web Conference – Sustainable, resilient, and agile manufacturing and service operations : Lessons from COVID-19
26-10-2021 to 28-10-2021
[en] Laser welding of copper to aluminum is challenging due to the formation of complex intermetallic phases. More Al (~18.5 at. %) can be dissolved in Cu, in contrast to Cu (~2.5 at. %) in Al. Therefore, welding from copper side, large melting of Al can be achieved. However optimum Cu and Al must be melted for a strong joint. Finding the right amount is difficult and time consuming by tradition analysis technique like inspection by weld cross-sections. Considering the speed of the welding process and complexity of analysis involving with metallography cross-sections, alternative rapid method to qualify the welds are necessary. The acoustic emission during laser welding can give proportional information of the Al, Cu melted. With such an approach the weld status can be obtained in real time. In this paper the acoustic welding signal using an airborne sensor in the audible range of 20 Hz to 20 kHz, is correlated to the weld strength and material mixing (Al, Cu melt). Finally, the weld status is predicted by an artificial neural network based on the acquired signal.
http://hdl.handle.net/10993/50215
10.1016/j.procir.2021.10.009

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