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A novel hybrid intelligent system for multi-objective machine parameter optimization

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Abstract

This multidisciplinary research presents a novel hybrid intelligent system to perform a multi-objective industrial parameter optimization process. The intelligent system is based on the application of evolutionary and neural computation in conjunction with identification systems, which makes it possible to optimize the implementation conditions in the manufacturing process of high precision parts, including finishing precision, while saving time, financial costs and/or energy. Empirical verification of the proposed hybrid intelligent system is performed in a real industrial domain, where a case study is defined and analyzed. The experiments are carried out based on real dental milling processes using a high precision machining centre with five axes, requiring high finishing precision of measures in micrometers with a large number of process factors to analyze. The results of the experiments which validate the performance of the proposed approach are presented in this study.

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Acknowledgments

This research is partially supported through projects of the Spanish Ministry of Economy and Competitiveness [ref: TIN2010-21272-C02-01 (funded by the European Regional Development Fund), TIN2008-06681-C06-04 and SA405A12-2 from Junta de Castilla y León]. The authors would also like to thank to ESTUDIO PREVIO (Madrid-Spain) for its collaboration in this research.

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Authors and Affiliations

  1. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Raquel Redondo

  2. Department of AI and Applied Electronics, Castilla y León Technological Institute, Burgos, Spain

    Javier Sedano

  3. Facultad de Odontología, UCM, Madrid, Spain

    Vicente Vera & Beatriz Hernando

  4. Department de Informática y Automática, Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

  5. IT4Innovations, Ostrava, Czech Republic

    Emilio Corchado

Authors
  1. Raquel Redondo
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  2. Javier Sedano
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  3. Vicente Vera
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  4. Beatriz Hernando
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  5. Emilio Corchado
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Corresponding author

Correspondence to Raquel Redondo.

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Redondo, R., Sedano, J., Vera, V. et al. A novel hybrid intelligent system for multi-objective machine parameter optimization. Pattern Anal Applic 18, 31–44 (2015). https://doi.org/10.1007/s10044-013-0345-7

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