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The automotive industry has achieved remarkable advances in passenger car safety systems to mitigate the risk of injuries and fatalities caused by road accidents. However, to further improve vehicle safety, it is essential to have a deeper understanding of real-world accidents and the true safety benefits of various safety systems in the field. This requires a framework to evaluate the effectiveness of safety systems in reducing occupant injury and fatalities. This study aims to use machine-learning techniques to predict occupant injury severity by considering accident parameters and safety systems, using the Road Accident Sampling System - India (RASSI) real-world accident data. The RASSI database contains comprehensive accident data, including various factors that contribute to occupant injury. The study focused on fifteen accident parameters that represent key aspects of crash scenarios such as vehicle type, accident type, vehicle speed, and occupant details.

Spot-weld joinery plays a major role in maintaining structural integrity of vehicle during an accident scenario. Robust failure definitions are important for accurate prediction of spot-weld failure in crash safety simulations. Spot welds have a complex metallurgical structure, consisting of fusion and heat affected zones. Identifying material failure definitions for huge number of spot-weld joint combinations in a typical Body in White (BIW) of a vehicle is highly challenging. In conventional LS-DYNA-MAT100 material model, spot-weld failure prediction accuracy is limited under complex crash loading scenarios, especially angular and bending load conditions. In order to enhance the failure predictions, a novel mathematical failure model is developed by considering instantaneous resultant loading along with bending moment as a key failure parameter to determine spot weld joint failure.

As per WHO 2018 report, pedestrian fatalities account for 23% of world road accident fatalities. Every day 850 pedestrians lose their lives in the world. As per MoRTH 2018 report, 16% of road accident fatalities are of pedestrians in India. Everyday 64 pedestrians lose their lives in India. Based on accident data, one of the most common reason for the pedestrian fatality is head injury due to primary contact from vehicle front-end structure. Pedestrian head injury performance highly depends on front-end styling, bonnet stiffness, clearance with aggregates underneath the bonnet and hard contact points. During concept stage of vehicle development, safety recommendation on front-end design is provided based on geometric assessment of the class A surface.

Aluminum alloy wheels are being widely used in the automotive industry since the last decade due to its superior styling and performance. Alloy wheel rim is one of the critical components and plays an important role in a frontal crash scenario. The wheel rim failure prediction in safety simulation is essential to ensure robust safety performance. Determining failure characteristics of an alloy wheel poses many difficulties considering its brittle nature, porosity and inhomogeneity in material properties across different regions of wheel rim due to mold design, cooling rate and other process parameters of the low-pressure die casting process. This paper describes the modelling and simulation methodology developed to predict accurate wheel behavior. The methodology addresses two distinct areas of challenges such as alloy wheel rim failure prediction and associated tire blow out.

Structural adhesive is a good alternative to provide required strength at joinery of similar and dissimilar materials. Adhesive joinery plays a critical role to maintain structural integrity during vehicle crash scenario. Robust adhesive failure definitions are critical for accurate predictions of structural performance in crash Computer Aided Engineering (CAE) simulations. In this paper, structural adhesive material characterization challenges like comprehensive In-house testing and CAE correlation aspects are discussed. Considering the crash loading complexity, test plan is devised for identification of strength and failure characteristics at 0°, 45°, 75°, 90°, and Peel loading conditions. Coupon level test samples were prepared with high temperature curing of structural adhesive along with metal panels. Test fixtures were prepared to carryout testing using Instron VHS machine under quasi-static and dynamic loading.

Spot welding is the primary joining method used in automobiles. Spot-weld plays a major role to maintain vehicle structural integrity during impact tests. Robust spot weld failure definitions is critical for accurate predictions of structural performance in safety simulations. Spot welds have a complex metallurgical structure, mainly consisting of fusion and heat affected zones. For accurate material property definitions in simulation models, huge number of inputs from test data is required. Multiple tests, using different spot weld joinery configurations, have to be conducted. In order to accurately represent the spot-weld behavior in CAE, detailed modeling is required using fine mesh. The current challenge in spot-weld failure assessment is developing a methodology having a better trade-off between prediction accuracy, testing efforts and computation time. In view of the above, cohesive zone models have been found to be very effective and accurate.