A Physics Artificial Intelligence Framework for Durability Analysis of Motorcycle Brake Pedal

The brake pedal assembly is a critical load bearing automotive component that must perform reliably under severe service conditions, especially during emergency braking events. Structural assessment is commonly carried out using computationally intensive non-linear finite element analysis, which can limit rapid design iterations during early product development. This study presents a physics informed surrogate modelling framework for predicting displacement and von Mises stress responses of a brake pedal directly from unstructured three dimensional mesh geometry. A structured dataset was created through parametric morphing and a Design of Experiments approach by varying pedal arm angle, pedal moment length, flange/web thickness, and combined geometric conditions. The simulation database was developed using conventional solvers and the generated cases were used to train Physics Artificial Intelligence based on the Transformer Neural Simulator architecture. The trained model showed close agreement with conventional solver results for both peak response values and contour distributions. For representative testing cases, predicted displacement and stress responses remained within acceptable engineering tolerance compared with the finite element baseline. After training, the PhysicsAI workflow reduced the prediction turnaround time compared with the conventional solver workflow. The results suggest that Design of Experiments driven datasets combined with geometric deep learning can provide an efficient workflow for rapid brake pedal structural assessment during concept development and iterative design studies.