Multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance

Abstract

In this investigation, the multi-objective selection and optimization of a gantry machine tool is achieved by analytic hierarchy process, multi-objective genetic algorithm, and Pareto-Edgeworth-Grierson–multi-criteria decision-making method. The objectives include maximum static deformation, the first four natural frequencies, mass, and fabrication cost of the gantry. Further structural optimization of the best configuration was accomplished using multi-objective genetic algorithm to improve all objectives except cost. The result of sensitivity analysis reveals the major contribution of columns of gantry with respect to the crossbeam’s contribution. After determining the most effective geometrical parameters using sensitivity analysis, multi-objective genetic algorithm was performed to obtain the Pareto-optimal solutions. In order to choose the final configuration, Pareto-Edgeworth-Grierson–multi-criteria decision-making was applied. The procedure outlined in this article could be used for selection and optimization of gantry as quantitative method as opposed to traditional qualitative method exploited in industrial application for design of gantry.