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Optimal material selection for a part becomes quite challenging with dynamically changing data from various sources. Multiple manufacturing locations with varying supplier capabilities add to the complexity. There is need to balance product attribute requirements with manufacturing feasibility, cost, sourcing, and vehicle program strategies. The sequential consideration of product attribute, manufacturing, and sourcing aspects tends to result in design churns. Ford R&A is developing a web based material recommender tool to help engineers with material selection integrating sourcing, manufacturing, and design considerations. This tool is designed to filter the list of materials for a specific part and provide a prioritized list of materials; and allow engineers to do weight and cost trade-off studies. The initial implementation of this material recommender tool employs simplified analytical calculators for evaluation of structural performance metrics of parts.

Typical automotive body structures are assemblies of stamped steel parts. The stamping process work hardens and thins the parts. The work hardening effects are more pronounced for advanced high strength steels such as DP600. It is now widely accepted in the industry that forming effects must be incorporated into the product attribute models to improve simulation accuracy. This paper investigates some of the challenges in incorporating the forming effects into product attribute models during the automotive product development process and presents solutions. It also investigates how the significance of the coupled forming to attribute CAE method varies based on the initial design thickness of a part. The paper concludes by reviewing component and vehicle level results achieved by the incorporation of the coupled process.

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.