Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/125723
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dc.contributor.authorShaffiee Haghshenas, S.en
dc.contributor.authorShirani Faradonbeh, R.en
dc.contributor.authorRezaMikaeil, R.en
dc.contributor.authorShaffiee Haghshenas, S.en
dc.contributor.authorTaheri, A.en
dc.contributor.authorSaghatforoush, A.en
dc.contributor.authorAlirezaDormishi, A.en
dc.date.issued2019en
dc.identifier.citationMeasurement, 2019; 146:159-170en
dc.identifier.issn0263-2241en
dc.identifier.issn1873-412Xen
dc.identifier.urihttp://hdl.handle.net/2440/125723-
dc.descriptionAvailable online 20 June 2019en
dc.description.abstractThe 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.en
dc.description.statementofresponsibilitySina Shaffiee Haghshenas, Roohollah Shirani Faradonbeh, Reza Mikaeil, Sami Shaffiee Haghshenas, Abbas Taheri, Amir Saghatforoush, Alireza Dormishien
dc.language.isoenen
dc.publisherElsevieren
dc.rights© 2019 Elsevier Ltd. All rights reserved.en
dc.source.urihttps://www.journals.elsevier.com/measurement/en
dc.subjectGang saw machine; Carbonate rocks; Cutting dimension stones; Maximum electrical current; Gene expression programming; Multiple linear regressionen
dc.titleA new conventional criterion for the performance evaluation of gang saw machinesen
dc.typeJournal articleen
dc.identifier.doi10.1016/j.measurement.2019.06.031en
pubs.publication-statusPublisheden
dc.identifier.orcidShirani Faradonbeh, R. [0000-0002-1518-3597]en
dc.identifier.orcidTaheri, A. [0000-0003-4176-5379]en
Appears in Collections:Aurora harvest 4
Civil and Environmental Engineering publications

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