TY - JOUR AU - Kumar, R. AU - Modi, A. AU - Panda, A. AU - Sahoo, A. K. AU - Deep, A. AU - Behra, P. K. AU - Tiwari, R. PY - 2019/12/30 Y2 - 2024/03/29 TI - Hard Turning on JIS S45C Structural Steel: An Experimental, Modelling and Optimisation Approach JF - International Journal of Automotive and Mechanical Engineering JA - Int. J. Automot. Mech. Eng. VL - 16 IS - 4 SE - Articles DO - 10.15282/ijame.16.4.2019.10.0544 UR - https://journal.ump.edu.my/ijame/article/view/1856 SP - 7315-7340 AB - <p>The present research is performed while turning of JIS S45C hardened structural steel with the&nbsp;multilayered (TiN-TiCN-Al2O3-TiN) CVD coated carbide insert by experimental, modelling&nbsp;and optimisation approach. Herein, cutting speed, feed rate, and depth of cut are regarded as&nbsp;input process factors whereas flank wear, surface roughness, chip morphology are considered&nbsp;to be measured responses. Abrasion and built up-edge are the more dominant mode of tool-wear&nbsp;at low and moderate cutting speed while the catastrophic failure of tool-tip is identified at higher&nbsp;cutting speed condition. Moreover, three different Modelling approaches namely regression,&nbsp;BNN, and RNN are implemented to predict the response variables. A Back-propagation neural&nbsp;network with a 3-8-1 network architecture model is more appropriate to predict the measured&nbsp;output responses compared to Elman recurrent neural network and regression model. The&nbsp;minimum mean absolute error for VBc, Ra and CRC is observed to be as 1.36% (BNN with 3-&nbsp;8-1 structure), 1.11% (BNN with 3-8-1 structure) and 0.251 % (RNN with 3-8-1 structure). A&nbsp;multi-performance Optimisation approach is performed by employing the weighted principal&nbsp;component analysis. The optimal parametric combination is found as the depth of cut at level 2&nbsp;(0.3 mm)-feed at level 1 (0.05 mm/rev) – cutting speed at level 2 (120 m/min) considered as&nbsp;favourable outcomes. The predicted results were validated through a confirmatory trial&nbsp;providing the process efficiency. The significant improvement for S/N ratio of CQL is observed&nbsp;to be 9.3586 indicating that the process is well suited to predict the machining performances. In&nbsp;conclusion, this analysis opens an avenue in the machining of medium carbon low alloy steel to&nbsp;enhance the machining performance of multi-layered coated carbide tool more effectively and&nbsp;efficiently.</p> ER -