Title: Parameter analysis and modelling of grinding special-shaped granite by diamond tools based on a robot stone machining system

Authors: Jing Wang; Shengui Huang; Jixiang Huang; Xipeng Xu; Changcai Cui

Addresses: Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China ' Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China; College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China ' Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China ' Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China; College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China ' Institute of Manufacturing Engineering, Huaqiao University, Xiamen 361021, China; College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China

Abstract: The processing parameters of special-shaped granite processing by robots are still determined artificially and the machining efficiency and quality are limited. To address this challenge, in this paper, the grinding process was divided into rough machining and finish machining with different diamond tools and parameters. The relationships among processing parameters, forces and errors of the cutting depth in rough machining and the relationships among processing parameters, contour errors and surface roughness in finish machining were investigated. Based on the experiments, the artificial neural networks of the two processes were conducted separately, having high precision with errors less than 9% and 6%, which can be used to direct the selection of the parameters. This research can be applied directly during production to improve the processing efficiency and quality.

Keywords: special-shaped granite; robot machining; processing parameters; artificial neural networks; ANNs.

DOI: 10.1504/IJAT.2020.109617

International Journal of Abrasive Technology, 2020 Vol.10 No.1, pp.62 - 82

Received: 20 Jul 2019
Accepted: 24 May 2020

Published online: 10 Sep 2020 *

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