Predictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models


1 Research Scholar, Mechanical Engineering, Hindustan University, Chennai, India

2 Department of Mechanical Engineering, Hindustan University, Chennai, India

3 Department of Mechanical Engineering, Arunachala College of Engineering for Women, Manavilai, Kanyakumari, Tamilnadu, India

4 Department of Mechanical Engineering, Mar Ephraem College of Engineering and Technology, Elavuvilai, Tamilnadu, India


The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This   work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on Design of experiments (Response surface methodology).  The cutting speed, feed and depth of cut are taken as the inputs and the wear is the output. The results reveal that the ANN provides better accuracy when compared to Regression analysis.


1.     Aouici, H., Yallese, M.A., Chaoui, K., Mabrouki, T. and Rigal, J.-F., "Analysis of surface roughness and cutting force components in hard turning with cbn tool: Prediction model and cutting conditions optimization", Measurement,  Vol. 45, No. 3, (2012), 344-353.
2.     Aslan, E., "Experimental investigation of cutting tool performance in high speed cutting of hardened x210 cr12 cold-work tool steel (62 hrc)", Materials &Design,  Vol. 26, No. 1, (2005), 21-27.
3.     Suresh, R., Basavarajappa, S. and Samuel, G., "Some studies on hard turning of aisi 4340 steel using multilayer coated carbide tool", Measurement,  Vol. 45, No. 7, (2012), 1872-1884.
4.     Aouici, H., Yallese, M., Belbah, A., Ameur, M. and Elbah, M., "Experimental investigation of cutting parameters influence on surface roughness and cutting forces in hard turning of x38crmov5-1 with cbn tool", Sadhana,  Vol. 38, No. 3, (2013), 429-445.
5.     Sahin, Y., "Comparison of tool life between ceramic and cubic boron nitride (cbn) cutting tools when machining hardened steels", Journal of Materials Processing Technology,  Vol. 209, No. 7, (2009), 3478-3489.
6.     Rizal, M., Ghani, J.A., Nuawi, M.Z. and Haron, C.H.C., "Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system", Applied Soft Computing,  Vol. 13, No. 4, (2013), 1960-1968.
7.     Bouacha, K., Yallese, M.A., Mabrouki, T. and Rigal, J.-F., "Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of aisi 52100 bearing steel with cbn tool", International Journal of Refractory Metals and Hard Materials,  Vol. 28, No. 3, (2010), 349-361.
8.     Caydas, U., "Machinability evaluation in hard turning of aisi 4340 steel with different cutting tools using statistical techniques", Proceedings of the Institution of Mechanical Engineers,  Vol. 224, No. B7, (2010), 1043-1050.
9.     Zhang, J.Z. and Chen, J.C., "The development of an in-process surface roughness adaptive control system in end milling operations", The International Journal of Advanced Manufacturing Technology,  Vol. 31, No. 9-10, (2007), 877-887.
10.   Aslan, E., Camuscu, N. and Birgoren, B., "Design optimization of cutting parameters when turning hardened aisi 4140 steel (63 hrc) with Al2O3+ ticn mixed ceramic tool", Materials &Design,  Vol. 28, No. 5, (2007), 1618-1622.
11.   Ren, Q., Balazinski, M., Baron, L. and Jemielniak, K., "Tsk fuzzy modeling for tool wear condition in turning processes: An experimental study", Engineering Applications of Artificial Intelligence,  Vol. 24, No. 2, (2011), 260-265.
12.   Akkuş, H. and Asilturk, İ., "Predicting surface roughness of aisi 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression models", Scientific Research and Essays,  Vol. 6, No. 13, (2011), 2729-2736.
13.   Özel, T. and Karpat, Y., "Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks", International Journal of Machine Tools and Manufacture,  Vol. 45, No. 4, (2005), 467-479.
14.   Scheffer, C., Kratz, H., Heyns, P. and Klocke, F., "Development of a tool wear-monitoring system for hard turning", International Journal of Machine Tools and Manufacture,  Vol. 43, No. 10, (2003), 973-985.
15.   Rajeev, D., Dinakaran, D. and Singh, S., "Artificial neural network based tool wear estimation on dry hard turning processes of aisi4140 steel using coated carbide tool", Bulletin of the Polish Academy of Sciences Technical Sciences,  Vol. 65, No. 4, (2017), 553-559.
16.   Wang, X., Wang, W., Huang, Y., Nguyen, N. and Krishnakumar, K., "Design of neural network-based estimator for tool wear modeling in hard turning", Journal of Intelligent Manufacturing,  Vol. 19, No. 4, (2008), 383-396.