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A virus-evolutionary multi-objective intelligent tool path optimization methodology for 5-axis sculptured surface CNC machining

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Abstract

Sculptured surface machining is a material removal operation essentially adopted to manufacture complex products. Computing optimal tool paths with reference to ideally designed CAD models is indispensable to be able to suggest machining improvements in terms of high quality and productivity. The present paper proposes a new methodology based on a virus-evolutionary genetic algorithm for enhancing sculptured surface tool path planning through an automated selection of values for standard 5-axis end milling strategies’ machining parameters to be decided upon in the context of a simulation-based; software-integrated, multi-objective optimization problem. The problem involves surface machining error as the first quality objective represented via the mean value of chordal deviations that tool path interpolation yields and effective radius of inclined tools that affects scallop. Machining time is the second quality objective entering the problem to assess productivity; whereas the number of cutter location points created for each tool path evaluation is also considered. Tool type, tool axis inclination angles as well as longitudinal and transversal steps are considered as the independent parameters in the case of 5-axis machining. Results obtained by conducting evaluation experiments and simulation tests accompanied by an actual machining process provided significant insight concerning the methodology’s efficiency and ability of suggesting practically viable results.

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Authors and Affiliations

  1. Laboratory of Manufacturing Processes and Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), N. Heraklion, 141 21, Athens, Greece

    N. A. Fountas & N. M. Vaxevanidis

  2. Department of Mechanical Engineering, Piraeus University of Applied Sciences (AEI), 122 44, Egaleo, Greece

    C. I. Stergiou

  3. Faculty of Science, Engineering and Computing, Kingston University, Roehampton Vale Campus, Friars Avenue, Kingston Upon Thames, London, SW15 3DW, UK

    N. A. Fountas & R. Benhadj-Djilali

Authors
  1. N. A. Fountas
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  2. N. M. Vaxevanidis
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  3. C. I. Stergiou
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  4. R. Benhadj-Djilali
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Corresponding author

Correspondence to N. M. Vaxevanidis.

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Fountas, N.A., Vaxevanidis, N.M., Stergiou, C.I. et al. A virus-evolutionary multi-objective intelligent tool path optimization methodology for 5-axis sculptured surface CNC machining. Engineering with Computers 33, 375–391 (2017). https://doi.org/10.1007/s00366-016-0479-5

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  • DOI: https://doi.org/10.1007/s00366-016-0479-5

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