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Fuel economy and federal safety regulations are driving automotive companies to use Dual Phase and other Advanced High Strength Steels (AHSS) in vehicle body structures. Joining and assembly plays a crucial role in the selection of these steels. Specifications are available for the resistance spot welding (RSW) of lower strength sheet steels, covering many aspects of the welding process from the stabilization procedure to endurance testing. Currently, specifications in the automotive industry for RSW with AHSS are limited. It is well known that welding of a thickness ratio greater than 1:2 poses a challenge. To utilize thinner gauge AHSS panels on body-in-white, welding schedules to join the thin to thick sheet steel stack-up are needed. Most of the existing published work was conducted on uncoated sheets and welded to the same thickness.

Typical automotive body structures use resistance spot welding for most joining purposes. New materials, such as Advanced High Strength Steels (AHSS) are increasingly used in the construction of automotive body structures to meet increasingly higher structural performance requirements while maintaining or reducing weight of the vehicle. One of the challenges for implementation of new AHSS materials is weldability assessment. Weld engineers and vehicle program teams spend significant efforts and resources in testing weldability of new sheet metal stack-ups. In this paper, we present a methodology to determine the weldability of sheet metal stack-ups using an Artificial Neural Network-based tool that learns from historical data. The paper concludes by reviewing weldability results predicted by using this tool and comparing with actual test results.