Multi-objective selection and structural optimization of the gantry in a gantry machine tool for improving static, dynamic, and weight and cost performance
Document Type
Article
Publication Date
1-1-2016
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.
Keywords
Gantry machine tool, Structural optimization, Multi-criteria decision-making, Multi-objective genetic algorithm, Pareto-Edgeworth-Grierson–multi-criteria decision-making
Divisions
fac_eng
Funders
University of Malaya Research Grant (UMRG) no. RP001B-13AET,High Impact Research (HIR) grant no. HIR-MOHE-16001-00-D000001
Publication Title
Concurrent Engineering
Volume
24
Issue
1
Publisher
SAGE Publications (UK and US)