A hybrid approach to support robotic polishing process planning

Wang, K.; Ding, L.; Dailami, F.; Matthews, J.

Abstract

Currently, mechanical polishing and manual labour are used to complete the finishing stages of the manufacture of moulds. This stage of the procedure is not only the most expensive, but it is also currently experiencing a shortage of experienced people. The manufacturing industry has looked towards robotic technology to assist the polishing process in order to address these problems and serve the broader needs of Industry 4.0. One factor that needs to be considered when automating the polishing process is the sequence of machined features that need to be processed. The research presented here examines the planning and optimisation of the polishing process for the production of moulds and is a component of a bigger project that aims to automate 80% of the existing human process. This chapter describes an optimisation technique for robotic polishing process sequencing that aims to simultaneously satisfy polishing sequence criteria and minimize polishing time. A hybrid approach combining both genetic algorithms and analytical hierarchical processes 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 the evaluation of polishing process rules. The proposed process sequencing has been successfully demonstrated on test piece examples

Citation

Wang, K., Ding, L., Dailami, F., & Matthews, J. (2023). A hybrid approach to support robotic polishing process planning. In Y. X. Gan (Ed.), Research and Developments in Engineering Research Vol. 8 (193-217). B P International (a part of SCIENCEDOMAIN International). https://doi.org/10.9734/bpi/rader/v8/10312f

• Online Publication Date: Sep 23, 2023
• Publication Date: Sep 23, 2023
• Deposit Date: Oct 14, 2023
• Pages: 193-217
• Book Title: Research and Developments in Engineering Research Vol. 8
• ISBN: 9788119761425
• DOI: https://doi.org/10.9734/bpi/rader/v8/10312f
• Public URL: https://uwe-repository.worktribe.com/output/11177075