A new conventional criterion for the performance evaluation of gang saw machines
Available online 20 June 2019 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 mod...
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Online Access: | http://hdl.handle.net/2440/125723 https://doi.org/10.1016/j.measurement.2019.06.031 |
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ftunivadelaidedl:oai:digital.library.adelaide.edu.au:2440/125723 2023-05-15T18:12:22+02:00 A new conventional criterion for the performance evaluation of gang saw machines Shaffiee Haghshenas, S. Shirani Faradonbeh, R. RezaMikaeil, R. Taheri, A. Saghatforoush, A. AlirezaDormishi, A. 2019 application/pdf http://hdl.handle.net/2440/125723 https://doi.org/10.1016/j.measurement.2019.06.031 en eng Elsevier Measurement, 2019; 146:159-170 0263-2241 1873-412X http://hdl.handle.net/2440/125723 doi:10.1016/j.measurement.2019.06.031 Shirani Faradonbeh, R. [0000-0002-1518-3597] Taheri, A. [0000-0003-4176-5379] © 2019 Elsevier Ltd. All rights reserved. https://www.journals.elsevier.com/measurement/ Gang saw machine Carbonate rocks Cutting dimension stones Maximum electrical current Gene expression programming Multiple linear regression Journal article 2019 ftunivadelaidedl https://doi.org/10.1016/j.measurement.2019.06.031 2023-02-05T19:24:56Z Available online 20 June 2019 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. Sina Shaffiee Haghshenas, Roohollah Shirani Faradonbeh, Reza Mikaeil, Sami Shaffiee Haghshenas, Abbas Taheri, Amir Saghatforoush, Alireza Dormishi Article in Journal/Newspaper sami The University of Adelaide: Digital Library Measurement 146 159 170 |
institution |
Open Polar |
collection |
The University of Adelaide: Digital Library |
op_collection_id |
ftunivadelaidedl |
language |
English |
topic |
Gang saw machine Carbonate rocks Cutting dimension stones Maximum electrical current Gene expression programming Multiple linear regression |
spellingShingle |
Gang saw machine Carbonate rocks Cutting dimension stones Maximum electrical current Gene expression programming Multiple linear regression Shaffiee Haghshenas, S. Shirani Faradonbeh, R. RezaMikaeil, R. Taheri, A. Saghatforoush, A. AlirezaDormishi, A. A new conventional criterion for the performance evaluation of gang saw machines |
topic_facet |
Gang saw machine Carbonate rocks Cutting dimension stones Maximum electrical current Gene expression programming Multiple linear regression |
description |
Available online 20 June 2019 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. Sina Shaffiee Haghshenas, Roohollah Shirani Faradonbeh, Reza Mikaeil, Sami Shaffiee Haghshenas, Abbas Taheri, Amir Saghatforoush, Alireza Dormishi |
format |
Article in Journal/Newspaper |
author |
Shaffiee Haghshenas, S. Shirani Faradonbeh, R. RezaMikaeil, R. Taheri, A. Saghatforoush, A. AlirezaDormishi, A. |
author_facet |
Shaffiee Haghshenas, S. Shirani Faradonbeh, R. RezaMikaeil, R. Taheri, A. Saghatforoush, A. AlirezaDormishi, A. |
author_sort |
Shaffiee Haghshenas, S. |
title |
A new conventional criterion for the performance evaluation of gang saw machines |
title_short |
A new conventional criterion for the performance evaluation of gang saw machines |
title_full |
A new conventional criterion for the performance evaluation of gang saw machines |
title_fullStr |
A new conventional criterion for the performance evaluation of gang saw machines |
title_full_unstemmed |
A new conventional criterion for the performance evaluation of gang saw machines |
title_sort |
new conventional criterion for the performance evaluation of gang saw machines |
publisher |
Elsevier |
publishDate |
2019 |
url |
http://hdl.handle.net/2440/125723 https://doi.org/10.1016/j.measurement.2019.06.031 |
genre |
sami |
genre_facet |
sami |
op_source |
https://www.journals.elsevier.com/measurement/ |
op_relation |
Measurement, 2019; 146:159-170 0263-2241 1873-412X http://hdl.handle.net/2440/125723 doi:10.1016/j.measurement.2019.06.031 Shirani Faradonbeh, R. [0000-0002-1518-3597] Taheri, A. [0000-0003-4176-5379] |
op_rights |
© 2019 Elsevier Ltd. All rights reserved. |
op_doi |
https://doi.org/10.1016/j.measurement.2019.06.031 |
container_title |
Measurement |
container_volume |
146 |
container_start_page |
159 |
op_container_end_page |
170 |
_version_ |
1766184911154184192 |