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An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence

Author

Listed:
  • N. A. Fountas

    (School of Pedagogical and Technological Education (ASPETE)
    Kingston University)

  • R. Benhadj-Djilali

    (Kingston University)

  • C. I. Stergiou

    (Piraeus University of Applied Sciences (PUAS))

  • N. M. Vaxevanidis

    (School of Pedagogical and Technological Education (ASPETE))

Abstract

Two critical objectives in sculptured surface tool path optimization are machining accuracy and process efficiency. The former objective is characterized as the combined effect of chord error and scallop height, known as surface machining error, whilst the latter may be reflected by the number of cutting tool locations constituting the tool path. These objectives are entirely depended on the values to be selected for computing tool paths under a given cutting strategy and preset tolerance. In order to determine optimal tool path parameters that will simultaneously satisfy the trade-off incurred between these objectives, a novel; generic and unbiased environment integrated with a virus-evolutionary heuristic for intelligent tool path optimization is presented. The proposed environment has been developed using the open architecture of a cutting edge CAM system whilst it deploys a set of interactive functions to straightforwardly assess criteria without the formal knowledge of any objective function; but directly from computer-aided manufacturing attributes; fully responsible to formulate efficient tool paths. A utility based on weighted summation for multi-objective optimization has been introduced to capture the direct output of globally optimized tool paths avoiding this way problem oversimplification and statistical errors that mathematical relations involve. Results have been rigorously validated both computationally and experimentally with the aid of a benchmark sculptured part that has been previously tested by several noticeable research contributions. Based on the quality of research outputs it is shown that the proposed framework for optimizing sculptured surface CNC tool paths may gain a prominent role for further extending the capabilities of current industrial strategies and be the flagship of allied; not-yet industrially interfaced approaches for deploying similar software integration tools to transfer significant results to production.

Suggested Citation

  • N. A. Fountas & R. Benhadj-Djilali & C. I. Stergiou & N. M. Vaxevanidis, 2019. "An integrated framework for optimizing sculptured surface CNC tool paths based on direct software object evaluation and viral intelligence," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1581-1599, April.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:4:d:10.1007_s10845-017-1338-y
    DOI: 10.1007/s10845-017-1338-y
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    References listed on IDEAS

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    1. Nurezayana Zainal & Azlan Mohd Zain & Nor Haizan Mohamed Radzi & Muhamad Razib Othman, 2016. "Glowworm swarm optimization (GSO) for optimization of machining parameters," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 797-804, August.
    2. Mohamed Arezki Mellal & Edward J. Williams, 2016. "Parameter optimization of advanced machining processes using cuckoo optimization algorithm and hoopoe heuristic," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 927-942, October.
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