Open AccessModelling of cutting forces and surface roughness evolutions during straight turning of Stellite 6 material based on response surface methodology, artificial neural networks and support vector machine approaches
Author(s) -
Brahim Ben Fathallah,
Riadh Saidi,
Salim Belhadi,
Mohamed Athmane Yallese,
Tarek Mabrouki
Publication year - 2021
Publication title -
journal of mechanical engineering and sciences
Language(s) - English
Resource type - Journals
eISSN - 2231-8380
pISSN - 2289-4659
DOI - 10.15282/jmes.15.4.2021.07.0673
Subject(s) - surface roughness , artificial neural network , support vector machine , response surface methodology , mechanical engineering , surface finish , radius , machining , stellite , engineering , structural engineering , computer science , materials science , artificial intelligence , machine learning , metallurgy , composite material , microstructure , computer security
The present research work proposes an experimental investigation helping to comprehend fundamental impacts of operating conditions during the cutting of cobalt alloys (Stellite 6). Thus, an experimental design was adopted to allow to build predicted mathematical models for the outputs, which are the average peak-to-valley profile roughness (Rz) and the tangential cutting force (Ft). Artificial neural network (ANN), support vector machine (SVM) and response surface methodology (RSM) were exploited to model the pre-cited outputs according to operation parameters. As a result, it has been highlighted that both feed rate and cutting depth, considerably, affect tangential cutting force evolution. Moreover, results show that both the insert feed rate and nose radius, are higher. This means the average peak-to-valley profile roughness is higher. In order to put out the effect of operating parameters on cutting outputs, Analysis of variance (ANOVA) method has been employed. This has allowed the detection of significant cutting conditions affecting average peak-to-valley profile roughness and tangential cutting force. In fact, to highlight the performance of adopted mathematical approaches, a comparison between RSM, ANN, and SVM has been also established in this study.