Abstract
Process planning has become an important stage for OKP companies as global competition has intensified the need to reduce the cost of products. This chapter uses sheet metal work as an example to discuss how new algorithms can be used to improve efficiency and to reduce cost. An optimal process planner can maximise the utilisation of costly raw material resources, improve machining efficiency, and hence reduce product cost. However, two problems must be overcome before such an optimal process planner can be developed; nesting and machining path planning. The nesting requirement is to maximise sheet metal material utilisation ratio by nesting parts of various shapes into the sheet. The path planning requirement is to optimise machining sequence so that the total machining path distance and machining time are minimised. This work investigates the two problems using genetic algorithms. The proposed genetic algorithm approach uses a genetic encoding scheme and a genetic reproduction strategy to reach an optimum solution. Case studies are carried out to test the genetic algorithms. The effectiveness of the genetic algorithm path planning approach is compared with that of the “ant colony” algorithm (Wang and Xie 2005). The results show that the genetic algorithm achieves better performances for path planning than the ant colony algorithm.
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Xie, S., Tu, Y. (2011). Optimal Process Planning for Compound Laser Cutting and Punching Using Genetic Algorithms. In: Rapid One-of-a-kind Product Development. Springer, London. https://doi.org/10.1007/978-1-84996-341-1_15
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DOI: https://doi.org/10.1007/978-1-84996-341-1_15
Publisher Name: Springer, London
Print ISBN: 978-1-84996-340-4
Online ISBN: 978-1-84996-341-1
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