A hybrid GRA-FIS based genetic algorithm and simulated annealing approach for camshaft material optimization using finite element analysis

Abstract

Failures in the camshaft drive can allow the valves to contact the piston crowns, which can result in significant internal damage. Most of the researchers have reported that aluminium–silicon carbide composite has not only proven to be lightweight but also resulted in lower deformation and better fatigue strength when compared to other alloys or steel. No predictive approach has been taken to predict more durable materials and the optimum working condition of camshafts using optimization methods. As a result, in order to fill such gaps, this study focuses on the design of camshafts made of various materials. In order to reduce the maximum stress, maximum deformation, and maximum strain obtained, this study focuses on optimising the camshaft by choosing the best material and loading conditions. The Taguchi L16 orthogonal array design and ANSYS 17.2's were used for the analysis. Finite element analysis was used for static structural analysis, and the findings produced by cast iron camshafts were compared to aluminium–silicon carbide composite composites. Later on, multi-objective optimization was tested using grey relational analysis in conjunction with a fuzzy inference system. The simulated annealing and genetic algorithm approaches were also used to forecast optimized results. It resulted that aluminium–silicon carbide composite 40% composite is the most suitable material. The most important factor was determined using an analysis of variance, followed by confirmatory tests. It showed that loading condition was the most significant factor.