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An investigation on swarm intelligence methods for the optimization of complex part programs in CNC turning

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Abstract

Automation of engineering procedures for the development of new manufacturing processes is of great importance in modern competitive conditions. For example, metalworking companies would greatly benefit from the development of methods for automatic generation, testing and optimization of part programs for machining operations. Indeed, the generation of part programs—even by using CAM software—does still require strong human intervention and it is basically a best guess approach with minimum optimization. Moreover, further refinement and correction of the part program on the machine tool is often necessary. Machining operations are generally based on a large number of parameters and therefore optimization strategies should be able to deal with high-dimensional spaces and disjoint domains. In this paper, two swarm intelligence optimization algorithms—particle swarm optimization (PSO) and artificial bee colony (ABC)—have been applied for optimizating the part program of a complex turning part. The optimizers were implemented in a framework for automatic part program generation, realistic simulation, and feasibility analysis. The results evidenced that both approaches were capable of optimizing efficiently the part program, and that the optimization time of the PSO approach on modern computers may be suitable for application in production.

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Authors and Affiliations

  1. Department of Electrical, Management and Mechanical Engineering DIEGM, University of Udine, via delle Scienze 206, Udine, Italy

    M. Sortino, S. Belfio, G. Totis, L. Di Gaspero & M. Nali

Authors
  1. M. Sortino
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  2. S. Belfio
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  3. G. Totis
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  4. L. Di Gaspero
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  5. M. Nali
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Correspondence to M. Sortino.

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Sortino, M., Belfio, S., Totis, G. et al. An investigation on swarm intelligence methods for the optimization of complex part programs in CNC turning. Int J Adv Manuf Technol 80, 657–672 (2015). https://doi.org/10.1007/s00170-015-7011-8

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  • DOI: https://doi.org/10.1007/s00170-015-7011-8

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