Streamlining automotive product development using neural networks

Abstract

To remain competitive in the global market, automotive companies scrutinise product development processes for time and cost savings incurred bringing new vehicles to market. As a result, virtual durability simulation can be utilised early in the design process to reduce the number of costly physical prototypes, assuming that high fidelity models are used. The compromise between model accuracy and computational efficiency presents a challenge that will be addressed by the authors. Neural networks, as computationally efficient mathematical models, will be shown as a viable tool for development of high fidelity models of nonlinear hysteretic components within a virtual durability simulation.