Title: Optimal process planning for compound laser cutting and punch using Genetic Algorithms

Authors: S.Q. Xie, J. Gan, G. Gary Wang

Addresses: Department of Mechanical Engineering, The University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand. ' Department of Mechanical Engineering, The University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand. ' School of Engineering Science, Simon Fraser University, 250-13450 102 Ave., Surrey, BC V3T 0A3, Canada

Abstract: This paper investigates the nesting issue and the machining path planning issue for improving the sheet metal machining efficiency. The nesting issue is to maximise sheet metal material utilisation ratio by nesting parts of various shapes into the sheet. The path planning issue is to optimise machining sequence so that the total machining path distance and machining time are minimised. This work investigates the two issues by using Genetic Algorithms (GA). The proposed GA approach uses a genetic encoding scheme and a genetic reproduction strategy to reach an optimum solution. Case studies are carried out to test the GAs. The effectiveness of the GA path planning approach is compared with the Ant Colony (AC) algorithm (Wang and Xie, 2005). The results show that GA achieves better performances in path planning than the AC algorithm.

Keywords: path planning; nesting; GAs; genetic algorithms; ant colony algorithm; compound machines; process planning; optimisation; laser cutting; punching; sheet metal.

DOI: 10.1504/IJMMS.2009.024346

International Journal of Mechatronics and Manufacturing Systems, 2009 Vol.2 No.1/2, pp.20 - 38

Published online: 01 Apr 2009 *

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