Multi-objective optimization of a flexible slider-crank mechanism synthesis, based on dynamic responses

ABSTRACT

This work deals with a multi-body system synthesis. A flexible slider crank mechanism has been investigated as an illustrative application. The main interest is focused on the mechanism design variables’ identification based on its dynamic responses. Three responses have been involved such as the slider velocity, the slider acceleration and the mid-point transversal deflection of the flexible connecting rod. Each of these responses has been embroiled separately in a mono-objective optimization. Subsequently, the multi-objective optimization subsuming these responses has been established. Two different optimization methods have been studied namely the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. It has been proved that the multi-objective optimization presents more accurate results beside the mono-objective optimization. Compared to the GA, the PSO is more powerful and is able to identify the mechanism design variable with better accuracy, in spite of the affordable computational time allowed with the GA optimization.

KEYWORDS:

• Multi-body system synthesis
• genetic algorithm
• particle swarm optimization
• mono-objective optimization
• multi-objective optimization

Disclosure statement

No potential conflict of interest was reported by the authors.