Prediction of Forces Components During the Turning Process of Stellite 6 Material Based on Artificial Neural Networks

Saidi, Riadh; Fathallah, Brahim Ben; Mabrouki, Tarek; Belhadi, Salim; Yallese, Mohamed Athmane

doi:10.1007/978-3-030-27146-6_43

Cited by 2 publications

(1 citation statement)

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“…To attain production efficiency, various modelling methods and techniques have been applied to optimize cutting conditions [14,16,19]. Artificial neural networks (ANN), genetic algorithm (GA), genetically optimized neural network system (GONNS) and other modelling techniques are adopted to optimize machining operations with the response surface methodology (RSM) [24,25]. In order to compare the dry to cooling mode in turning Stellite 6, Sarikaya et al, [14,17] examined the influence of these parameters on tool wear based on Analysis of variance (ANOVA) and Taguchi methods to determine optimal surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Modelling 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

Fathallah

Saidi

Belhadi

et al. 2021

JMES

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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.

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