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Artificial Neural Network Modeling of the Effect of Cutting Conditions on Cutting Force Components during Orthogonal Turning


Author : Sanjib Banerjee, Angshuman Deka, Gitartha Dev Sarmah, Nitish Bhardwaj

Pages : 127-130, DOI:http://Dx.Doi.Org/10.14741/Ijcet/Spl.2.2014.23
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Abstract

Variation in cutting force components (FX, FY and FZ in three mutually perpendicular directions X, Y and Z) with cutting conditions viz. speed (v), feed (f) and depth of cut (d) during orthogonal turning of mild steel specimen using a HSS cutting tool was investigated in an automatic lathe machine. During the turning process, the cutting forces experienced insignificant variations with change in speed. Cutting force values were however observed to considerably increase with increase in feed and depth of cut. Subsequently these cutting forces could be modelled as function of the cutting conditions viz. v, f and d, by artificial neural network (ANN). The cutting force values modelled and subsequently predicted at various cutting conditions within the specified domain have been successfully correlated with the experimental results and literature review, with fairly good accuracy. During the validation process, it was possible to predict 100%, 94% and 100% of the cutting force values for FX, FY and FZ, respectively within a percentage deviation of ±10%. This observation highlights the superior prediction capability of ANN technique in the current research area.

Keywords: Cutting Forces; Cutting Conditions; Artificial neural network; Orthogonal Turning; Mild Steel.

Article published in International Conference on Advances in Mechanical Sciences 2014, Special Issue-2 (Feb 2014)

 

 

 

 

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