Optimisation of frontal impact force scheme to accelerate the target setting process for development of front-end structures in vehicles.

A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and positional parameters of the components, along with their force-displacement curves, to estimate the vehicle's crash pulse. In this work, we have implemented PINNs to generate an optimized force scheme using the historical CAE test data. Using this approach promises to cut short the time and cost that goes into the conventional process of target setting.