GA-AHP Method to Support Robotic Polishing Process Planning

Authors

  • Ke Wang Bristol Robotics Laboratory (BRL), Stoke Gifford, Bristol, United Kingdom
  • Lian Ding School of Engineering, University of the West of England, Coldharbour Lane Bristol, United Kingdom
  • Farid Dailami Bristol Robotics Laboratory (BRL), Stoke Gifford, Bristol, United Kingdom
  • Jason Matthews School of Engineering, University of the West of England, Coldharbour Lane Bristol, United Kingdom

DOI:

https://doi.org/10.37256/dmt.2220221513

Keywords:

computer-aided process planning, polishing rules, genetic algorithm, robotic, AHP

Abstract

The finishing stage of mould manufacturing is generally completed via mechanical polishing and manually conducted. Not only is this the most expensive phase of the process but also is currently struggling with a deficit of skilled workers. To address these issues, and support the wider needs of Industry 4.0, the manufacturing community has investigated robotic technologies to support the polishing process. The work reported here, investigating the polishing process planning and optimization for mould manufacture is part of a larger project aiming to automate 80% of the current manual process. Presented in this article is an optimization strategy for robotic polishing process sequencing aiming at satisfying polishing sequence rules and the shortest polishing time simultaneously. A hybrid approach combining both genetic algorithm (GA) and analytical hierarchical process (AHP) is proposed based on the specific characteristics of polishing process sequencing. A multi-objective fitness function is defined using AHP including the calculation of polishing time and evaluation of polishing process rules. The proposed GA-based process sequencing has been successfully demonstrated on test piece examples.

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Published

2022-08-08

How to Cite

1.
Wang K, Ding L, Dailami F, Matthews J. GA-AHP Method to Support Robotic Polishing Process Planning. Digit. Manuf. Technol. [Internet]. 2022 Aug. 8 [cited 2024 Mar. 29];2(2):23-44. Available from: https://ojs.wiserpub.com/index.php/DMT/article/view/dmt.v2i22022.23-44