Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/125723
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Type: | Journal article |
Title: | A new conventional criterion for the performance evaluation of gang saw machines |
Author: | Shaffiee Haghshenas, S. Shirani Faradonbeh, R. RezaMikaeil, R. Shaffiee Haghshenas, S. Taheri, A. Saghatforoush, A. AlirezaDormishi, A. |
Citation: | Measurement, 2019; 146:159-170 |
Publisher: | Elsevier |
Issue Date: | 2019 |
ISSN: | 0263-2241 1873-412X |
Statement of Responsibility: | Sina Shaffiee Haghshenas, Roohollah Shirani Faradonbeh, Reza Mikaeil, Sami Shaffiee Haghshenas, Abbas Taheri, Amir Saghatforoush, Alireza Dormishi |
Abstract: | The process of cutting dimension stones by gang saw machines plays a vital role in the productivity and efficiency of quarries and stone cutting factories. The maximum electrical current (MEC) is a key variable for assessing this process. This paper proposes two new models based on multiple linear regression (MLP) and a robust non-linear algorithm of gene expression programming (GEP) to predict MEC. To do so, the parameters of Mohs hardness (Mh), uniaxial compressive strength (UCS), Schimazek’s F-abrasiveness factor (SF-a), Young’s modulus (YM) and production rate (Pr) were measured as input parameters using laboratory tests. A statistical comparison was made between the developed models and a previous study. The GEP-based model was found to be a reliable and robust modelling approach for predicting MEC. Finally, according to the conducted parametric analysis, Mh was identified as the most influential parameter on MEC prediction. |
Keywords: | Gang saw machine; Carbonate rocks; Cutting dimension stones; Maximum electrical current; Gene expression programming; Multiple linear regression |
Description: | Available online 20 June 2019 |
Rights: | © 2019 Elsevier Ltd. All rights reserved. |
DOI: | 10.1016/j.measurement.2019.06.031 |
Published version: | https://www.journals.elsevier.com/measurement/ |
Appears in Collections: | Aurora harvest 4 Civil and Environmental Engineering publications |
Files in This Item:
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hdl_125723.pdf | Accepted version | 1.87 MB | Adobe PDF | View/Open |
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