Multi-Objective Optimal Synthesis of Four-Bar Mechanisms with Probabilistic Uncertainties using a Genetic Algorithm

Robust synthesis of mechanisms addresses the effect of uncertainties on the mechanism’s trajectory tracking performance. In this paper, probabilistic metrics, instead of deterministic metrics, are used for multi-objective Pareto optimum robust synthesis of a four-bar mechanism for path generation having parameters withprobabilistic uncertainties. The objective functions that have been simultaneously considered in this work are, namely, mean of pathtracking error (MTE) and variance of path tracking error (VTE). The multi-objective uniform-diversity genetic algorithm (MUGA)is used for Pareto optimum robust design of a four-bar linkage. In this way, a Pareto front of optimum four-bar mechanisms are firstobtained considering probabilistic uncertainties in their parameters using the statistical moments of those objective functions through a Monte Carlo Simulation (MCS) approach. The robustness of the design obtained using such probabilistic approach is shown and compared with that of the design obtained using deterministic approach

Keywords:

Genetic Algorithms , Multi-Objective , Uncertainties , Robust , Mechanisms