Structural optimization of the brake pedal by artificial intelligence

In recent years, manufacturers in the automotive sector have focused on making improvements to achieve fuel efficiency and enhance vehicle performance. One promising strategy for improvement is reducing the weight of the vehicles. Manufacturers aim to produce cost-effective, lightweight vehicles that comply with environmental standards without compromising quality. Weight reduction efforts are possible through optimization. Optimization methods are used to ensure optimal material distribution in designed vehicle parts. The main goal of structural optimization methods is to optimize existing designs in terms of size, shape, and topology. In this study, weight reduction was achieved on a brake pedal through structural optimization methods. The objective was to find the most suitable material distribution for the brake pedal and develop an optimal design. Static analyses were conducted on the brake pedal, followed by topological optimization for weight reduction. The new brake pedal design was created after the topological optimization. Subsequently, shape optimization was performed using artificial intelligence optimization techniques (genetic algorithm, grey wolf optimization algorithm, and particle swarm optimization algorithm) on the new design part. As a result, a weight reduction of approximately 25.4% was achieved compared to the initial model after topological optimization. Furthermore, optimization using the genetic algorithm led to around 31.8% weight reduction compared to the initial model, while the particle swarm optimization algorithm and the grey wolf optimization algorithm resulted in weight reductions of 32.8% and 32.9%, respectively.