Ford Motor Company is joining forces with Detroit-based Wayne State University in order to develop a new, more reliable method of predicting material weldability (how well two materials will bond) for Resistance Spot Welding (RSW) processes.
That’s according to a release from WSU, which just received a $1.7 million grant from the Digital Manufacturing and Design Innovation Institute (DMDII) – a federally-funded research and development organization – for the project. According to Kyoung-Yun Kim, Ph.D., Associate Professor of Industrial and Systems Engineering at Wayne State University, “resistance spot-welding processes and parameters are complex due to coating conditions and surface roughness.”
Those complexities mean that oftentimes, OEMs and systems integrators require time-consuming physical tests to be carried out in order to assess the feasibility of joining two metallic pieces with RSW techniques. At present, “data-driven weldability prediction [could] improve product design efficiency, but is underutilized because of existing data inconsistences,” says Kim.
So, Ford and WSU are starting work on a new, web-based tool that can accurately predict the RSW weldability of one or more materials with a given set of parameters. “This prediction tool will ultimately improve product quality through the utilization of advanced materials, allow users to rapidly assess weldment feasibility, and reduce the amount of physical testing required for new material candidates,” says Kim. Ford will serve as the guinea pig for the proof-of-concept “Virtually Guided Weldability Prediction” tool, using it in automotive body structure joining.
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