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Meeting	MS&T22: Materials Science & Technology
Symposium	Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
Presentation Title	Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds
Author(s)	Moses Obiri, Alejandro Ojeda, Deborah Fagan, Keerti Kappagantula, Hassan Ghassemi-Armaki, Blair Carlson
On-Site Speaker (Planned)	Alejandro Ojeda
Abstract Scope	Resistance spot welding (RSW) is a welding technique used to join resistive metals such as aluminum and low carbon steel by applying pressure and heat from an electric current to the weld area. The properties of RSW of aluminum to steel are being studied to reduce vehicle weight and thus increase fuel efficiency. Previous research has described the properties of microstructure variables (fracture mode, hardness, and thickness) in the intermetallic layer formed by RSW of Al – steel welds. Joint performance is also well known to be dependent on the spot weld attributes developed during processing, and several factors influence the weld attributes during processing. The relationship between microstructure variables and joint performance, on the other hand, has yet to be thoroughly investigated. We categorize hardness and thickness profile curves using machine learning techniques and identify profiles that result in optimal weld performance.

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Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds

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