Parametric study of rock cutting with SMART∗CUT picks

Shao, Wen, Li, Xingsheng, Sun, Yong and Huang, Han (2017) Parametric study of rock cutting with SMART∗CUT picks. Tunnelling and Underground Space Technology, 61 134-144. doi:10.1016/j.tust.2016.09.012

Author Shao, Wen
Li, Xingsheng
Sun, Yong
Huang, Han
Title Parametric study of rock cutting with SMART∗CUT picks
Journal name Tunnelling and Underground Space Technology   Check publisher's open access policy
ISSN 0886-7798
Publication date 2017-01-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.tust.2016.09.012
Open Access Status Not yet assessed
Volume 61
Start page 134
End page 144
Total pages 11
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
Subject 2215 Building and Construction
1909 Geotechnical Engineering and Engineering Geology
Abstract The severe abrasive wear of the current cemented tungsten carbide (WC) tools is a “bottleneck” that limits the usage of machinery in hard rock mines. To address this issue, a revolutionary thermally stable diamond composite (TSDC) based cutting tool, also called Super Material Abrasive Resistant Tool (SMART∗CUT) was developed by CSIRO. Before this novel tool is employed for practical rock cutting, the effects of the cutting parameters on the performance of the SMART∗CUT picks must be determined and the cutting forces of the picks have to be estimated as they directly affect the capability and efficiency of the selected cutterhead and hence the excavation machine. In this study, rock cutting tests based on Taguchi’s L25 orthogonal array were conducted to analyze the cutting parameters. The signal-to-noise (S/N) ratios and the analysis of variance (ANOVA) were applied to investigate the effects of depth of cut, attack angle, spacing and cutting speed on mean cutting and normal forces during the rock cutting process. Empirical models for predicting the cutting forces on SMART∗CUT picks were developed using multiple linear regression (MLR) and artificial neural network (ANN) techniques. Parametric combinations for minimizing the cutting forces and the statistical significance of process factors were successfully determined by using the Taguchi technique. Good prediction capabilities with acceptable errors were achieved by the developed MLR and ANN models. However, the ANN models offered better accuracy and less deviation.
Keyword Cutting forces
Multiple linear regression
Neural network
Rock cutting
Taguchi method
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mechanical & Mining Engineering Publications
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