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Improving the predictive accuracy of artificial neural network (ANN) approach in a mild steel turning operation

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Abstract

Improving and increasing metal cutting process efficiency requires an in-depth knowledge of the cutting process, as well as the development/application of an efficient modeling technique, capable of forming the core tools for planning, scheduling, and integration of the machining processes. This study evaluates the application of Levenberg Marquardt (LM) and Scaled Conjugate Gradient (SCG) training algorithms of artificial neural networks in the modeling and prediction of metal removal rate (MRR) and average surface roughness (Ra) in the turning of cylindrical mild steel. The neural network models were trained using a set of experimental data consisting of the input parameters: cutting speed, depth of cut, and feed rate. Based on the graphical and statistical results, the LM offered the best accuracy in terms of the model fitting, RMSE and R2 obtained. For the LM algorithms, the least RMSE values of 1.7809 × 10−1, 2.6805 × 10−1, and 1.5815 × 10−1 were obtained for the training, validation, and test data respectively, and for the SCG algorithm, least RMSE values of 1.7809 × 10−1, 2.6805 × 10−1, and 1.5815 × 10−1 were recorded. Comparing both algorithms, the Levenberg Marquardt algorithm recorded the least average in terms of the number of the epoch, and RMSE value and at the same time the best performance in terms of good fitting.

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Acknowledgments

I would like to appreciate the staffs of mechanical workshop, Petroleum Training Institute, Warri, Delta State, for their moral support and guidance all through the experimental process.

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  1. Department of Mechanical & Production Engineering, Faculty of Engineering, Oleh Campus, Delta State University, Oleh, Nigeria

    Samuel O. Sada

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  1. Samuel O. Sada
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Correspondence to Samuel O. Sada.

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Sada, S.O. Improving the predictive accuracy of artificial neural network (ANN) approach in a mild steel turning operation. Int J Adv Manuf Technol 112, 2389–2398 (2021). https://doi.org/10.1007/s00170-020-06405-4

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  • DOI: https://doi.org/10.1007/s00170-020-06405-4

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