Abstract
The present paper highlights the prediction of tool wear through artificial neural network (ANN) and regression methodology during machining of AISI 4340 steel. Abrasive nature of wear dominates more during experimental investigation. Flank wear model through quadratic regression yields high correlation coefficient (R2 = 0.961 close to unity) which indicates the goodness of fit of the model. Contour plots determine optimal parameters of low levels such as 0.4 mm depth of cut of 0.04 mm/rev feed and 50 m/min cutting speed to obtain minimal flank wear in machining. The average percentage of error between experimental to ANN is found to be only 2.02 whereas this error is maximum in regression model (18.94). Hence, model developed by ANN is found to be effective during machining study for flank wear prediction.
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Sahoo, A.K., Panda, A., Kumar, R. (2022). On Tool Wear Prediction Using Artificial Neural Network and Regression Methodology During Machining. In: Pattnaik, P.K., Sain, M., Al-Absi, A.A. (eds) Proceedings of 2nd International Conference on Smart Computing and Cyber Security. SMARTCYBER 2021. Lecture Notes in Networks and Systems, vol 395. Springer, Singapore. https://doi.org/10.1007/978-981-16-9480-6_30
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DOI: https://doi.org/10.1007/978-981-16-9480-6_30
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