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Due to the lack of biofidelity seen in GHBMC M50-O in rear-facing impact simulations involving interaction with the seat back in an OEM seat, it is important to explore how the boundary conditions might be affecting the biofidelity and potentially formulate methods to improve biofidelity of different occupant models in the future while also maintaining seat validity. This study investigated the influence of one such boundary condition, which is the seat back foam material properties, on the thorax and pelvis kinematics and injury outcomes of the GHBMC 50th M50-O model in a high-speed rear-facing frontal impact scenario, which involves severe occupant loading of the seat back. Two different seat back foam materials were used – a stiff foam with high densification and a soft foam with low densification. The peak magnitudes of the T-spine resultant accelerations of the GHBMC M50-O increased with the use of soft foam as compared to stiff foam.

During high-speed rear impacts with delta-V > 25 km/h, the front seats may rotate rearward due to occupant and seat momentum change leading to possibly large seat deflection. One possible way of limiting this may be by introducing a structure that would restrict large rotations or deformations, however, such a structure would change the front seat occupant kinematics and kinetics. The goal of this study was to understand the influence of seat back restriction on head, neck and torso responses of front seat occupants when subjected to a moderate speed rear-impact. This was done by simulating a rear impact scenario with a delta-V of 37.4 km/h using LS-Dyna, with the GHBMC M50 occupant model and a manufacturer provided seat model. The study included two parts, the first part was to identify worst case scenarios using the simplified GHBMC M50-OS, and the second part was to further investigate the identified scenarios using the detailed GHBMC M50-O.

Experimentally derived brain response envelopes are needed to evaluate and validate existing finite element (FE) head models. Motion of the brain relative to the skull during rotational input was measured using high-speed biplane x-ray. To generate repeatable, reproducible, and scalable data, methods were developed to reduce experimental variance. An “extreme-energy” device was developed to provide a controlled input that is unaffected by specimen characteristics. Additionally, a stereotactic frame was used to deploy radiopaque markers at specific, pre-determined locations within the brain. One post-mortem human surrogate (PMHS) head specimen was subjected to repeat tests of a half-sine rotational speed pulse in the sagittal plane. The desired pulse had a peak angular speed of 40 rad/s and duration of 30 ms. Relative motion of the brain was quantified using radiopaque targets and high-speed biplane x-ray. Frontal and occipital intracranial pressure (ICP) were also measured.

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.

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.

Advanced High Strength Steels (AHSS) such as DP590 HDGI are helping automakers meet increasingly higher structural performance requirements while maintaining or reducing weight of the vehicle body structure [7]. One of the issues facing design engineers implementing new materials such as AHSS is the lack of understanding the expected material variability within a steel supplier and also from one steel supplier to another; and how the variability affects product attribute performances. In this paper, we present an analysis of the aggregated mechanical property variability data obtained from several steel suppliers for a popular AHSS grade and also present studies related to the effect of material variability on structural responses.