Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC) |
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| Affiliation: | 1. Department of Business Administration, University of Illinois at Urbana-Champaign, USA;2. School of Mechanical and Aerospace Engineering, Queen''s University, Belfast BT95AH, UK;3. Technical and Vocational Training Corporation, Riyadh College of Technology, P.O. Box: 42826, Riyadh 11551, Saudi Arabia;1. School of Mechanical and Aerospace Engineering, Queen?s University, Belfast BT95AH, UK;2. Department of Business Administration, University of Illinois at Urbana-Champaign, USA;3. School of Engineering, Robert Gordon University, Garthdee Road, Aberdeen AB107GJ, UK;1. Tecnalia R&I, Advanced Manufacturing Department, Paseo Mikeletegi 7, 20009 San Sebastián, Spain;2. Department of Mechanical Engineering, University of the Basque Country, Faculty of Engineering of Bilbao, Alameda de Urquijo s/n, 48013 Bilbao, Spain;3. GKN Aerospace Engine Systems AB, 46181 Trollhättan, Sweden;1. School of Mechanical Engineering, Shandong University of Technology, 266 West Xincun Road, Zibo 255000, China;2. School of Mechanical Engineering, Shandong University, 17923 Jingshi Road, Jinan 250061, China |
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| Abstract: | In this study, 39 sets of hard turning (HT) experimental trials were performed on a Mori-Seiki SL-25Y (4-axis) computer numerical controlled (CNC) lathe to study the effect of cutting parameters in influencing the machined surface roughness. In all the trials, AISI 4340 steel workpiece (hardened up to 69 HRC) was machined with a commercially available CBN insert (Warren Tooling Limited, UK) under dry conditions. The surface topography of the machined samples was examined by using a white light interferometer and a reconfirmation of measurement was done using a Form Talysurf. The machining outcome was used as an input to develop various regression models to predict the average machined surface roughness on this material. Three regression models – Multiple regression, Random forest, and Quantile regression were applied to the experimental outcomes. To the best of the authors’ knowledge, this paper is the first to apply random forest or quantile regression techniques to the machining domain. The performance of these models was compared to ascertain how feed, depth of cut, and spindle speed affect surface roughness and finally to obtain a mathematical equation correlating these variables. It was concluded that the random forest regression model is a superior choice over multiple regression models for prediction of surface roughness during machining of AISI 4340 steel (69 HRC). |
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| Keywords: | Hard turning Regression modelling Random forest regression Quantile regression |
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