Design car side impact using machine learning

Abstract

In this paper, the design of vehicle door weight is minimized using a newly developed machine learning algorithm that is referred to as Grey Wolf optimizer (GWO). GWO is a metaheuristic technique that show good and robust performance in solving optimization applications. It is a nature-inspired algorithm that is based on the hunting grey wolf while hunting and catching the prey. The algorithm is known to have simple, yet efficient, structure. On the other hand, the design of the car's door during an impact is known to be a multi-objective optimization problem that is based the European Enhanced Vehicle-Safety Committee. The algorithm needs to minimize the door weight will be satisfying several constrains. The design depends on 11 parameters including the B-pillar inner, B-pillar reinforcement, floor side inner, cross members, door beam, door beltline reinforcement, roof rail, materials of B-pillar inner floor side inner barrier height and hitting position. Monte Carlo simulation is used to test the method and its robustness and stability.