{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:09:54Z","timestamp":1774631394265,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC\u2013C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.<\/jats:p>","DOI":"10.3390\/s22197152","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:07:55Z","timestamp":1663888075000},"page":"7152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Machine Learning-Based Prediction of Specific Energy Consumption for Cut-Off Grinding"],"prefix":"10.3390","volume":"22","author":[{"given":"Muhammad Rizwan","family":"Awan","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Universitat Politecnica De Catalunya (UPC), 08034 Barcelona, Spain"},{"name":"Department of Mechanical Engineering, The Superior University, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8911-0115","authenticated-orcid":false,"given":"Hern\u00e1n A.","family":"Gonz\u00e1lez Rojas","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Universitat Politecnica De Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6997-5189","authenticated-orcid":false,"given":"Saqib","family":"Hameed","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Universitat Politecnica De Catalunya (UPC), 08034 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6468-614X","authenticated-orcid":false,"given":"Fahid","family":"Riaz","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahzaib","family":"Hamid","sequence":"additional","affiliation":[{"name":"Department of Research and Development, AI Studio, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8545-0478","authenticated-orcid":false,"given":"Abrar","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Tallinn University of Technology, Ehitajate Tee 5, 19086 Tallinn, Estonia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.apenergy.2019.04.158","article-title":"Resources value mapping: A method to assess the resource efficiency of manufacturing systems","volume":"249","author":"Papetti","year":"2019","journal-title":"Appl. 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