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Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization

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Intelligent Computing Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 627))

Abstract

3D printing is a relatively new group of manufacturing technologies, methods and processes that produce parts through material addition. 3D printing technologies are mainly employed for the fabrication of prototypes and physical models during product design and development; however as they continuously improve in terms of accuracy and range of raw materials they are increasingly employed in the actual manufacturing process. This puts a new emphasis on the study of some of the process planning problems and issues that are related with the cost efficient use of 3D printing systems and the quality of their products. Among the most crucial process planning problems are: (i) the selection of fabrication orientation and parameters which is by definition a multi-criteria optimization problem in which the operator seeks to achieve the optimum trade-off between cost and quality, under given fabrication constraints and requirements, and (ii) the batch selection/planning or “packing” problem, at which the selection and placement of various different parts inside the machine workspace is considered. As such, the primary goal of the chapter is to present the effective utilization of Genetic Algorithms, which are a particular class of Evolutionary Computing, as a means of optimizing the 3D printing process planning.

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Notes

  1. 1.

    Support structures are aiding structures which secure part stability during fabrication. They are virtually constructed by special software and they are physically removed in post-processing phase. 3DP technologies that require support structures belong usually to the liquid and solid filament categories.

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Authors and Affiliations

  1. Laboratory of Advanced Manufacturing Technologies and Testing, Department of Industrial Management and Technology, University of Piraeus, 80 Karaoli & Dimitriou str., 18534, Piraeus, Greece

    Vassilios Canellidis, John Giannatsis & Vassilis Dedoussis

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  1. Vassilios Canellidis
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  2. John Giannatsis
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  3. Vassilis Dedoussis
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Correspondence to John Giannatsis .

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Editors and Affiliations

  1. Dept of Informatics, University of Piraeus, Piraeus, Greece

    George A. Tsihrintzis

  2. Department of Informatics, University of Piraeus, Piraeus, Greece

    Maria Virvou

  3. Data Science Institute, Faculty of Scien, ce & technology, Bournemouth University, Adelaide, Australia

    Lakhmi C. Jain

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Canellidis, V., Giannatsis, J., Dedoussis, V. (2016). Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization. In: Tsihrintzis, G., Virvou, M., Jain, L. (eds) Intelligent Computing Systems. Studies in Computational Intelligence, vol 627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49179-9_13

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  • DOI: https://doi.org/10.1007/978-3-662-49179-9_13

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  • Print ISBN: 978-3-662-49177-5

  • Online ISBN: 978-3-662-49179-9

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