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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/21696

Title: Development of an intelligent automated polishing system
Authors: Kalt, Eugene B.F.
Monfared, Radmehr P.
Jackson, Michael R.
Keywords: Automated Polishing
Robotic grinding
Manual operation
Data capture through mechatronic devices
Issue Date: 2016
Publisher: Euspen
Citation: KALT, E., MONFARED, R. and JACKSON, M.R., 2016. Development of an intelligent automated polishing system. IN: Proceedings of the 16th International Conference of the European Society for Precision Engineering and Nanotechnology (EUSPEN), East Midlands Conferences, University of Nottingham, May 30th-June 3rd.
Abstract: In high-value manufacturing sectors, many manufacturing processes are still performed manually, such as polishing operations for small metallic parts. Increasing volume, the need for consistency in quality, and health and safety issues are some of the reasons for industry to search urgently for alternative solutions for manual polishing processes. This article reports the development of an intelligent automated polishing system to achieve consistent surface quality and removal of superficial defects from high-value components, such as those used in aerospace industry. The article reports an innovative method to capture manual polishing processes by skilled operators. The captured polishing parameters are then used to develop and control a robotic polishing system that can adopt various polishing patterns. A brief summary of existing fully and semi-automated polishing systems and their inadequacy for industrial applications are discussed. The need for building automation system based on manual operations are explained and a systematic data capturing process for a specific aerospace-based component is defined. The development of the process capturing device is explained, the data analysis and interpretations are discussed and the migration from manual operation to an automated polishing system is reported. Further detailed information is given in relation with combining data from various sensors and building of an automated system based on learning from manual operations. The research results are also briefly discussed and conclusions are drawn regarding applicability of automated systems for highly skilled manual operations.
Description: This is a conference paper.
Version: Published
URI: https://dspace.lboro.ac.uk/2134/21696
Publisher Link: http://www.euspen.eu/OurEvents/Nottingham2016.aspx
Appears in Collections:Conference Papers and Presentations (Mechanical, Electrical and Manufacturing Engineering)

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