Selection of Optimal Machining Parameters by Taguchi-FIS Simulation during Machining of High Tensile Low Alloy Steel

Article Preview

Abstract:

The dry turning was done on the high tensile low alloy steel. The Indoloy carbide tool was used. The input parameters were speed, feed, and depth of cut (DOC, d). The chip reduction coefficient (CRC) and von Mises stress (VMS) were the output responses. Universal tensile testing was done to find out the strength coefficient (K) and strain hardening exponent (n). "K" and "n" were incorporated to obtain the von Mises stress (VMS). The experiments were performed following the L9 array (Taguchi). The analysis of variance (ANOVA) was done for the CRC, with the lower the better condition. The ANOVA was done for VMS with the lower-better condition. The ANOVA was done by developing a MATLAB program. The feed contributed strongly to both the CRC (80.1381% contribution) and VMS (33.1490% contribution) minimizations. A Taguchi-Fuzzy inference system (FIS) simulation was done (MATLAB software) to select optimal parameters. CRC and VMS were the inputs, and MPCI (multi performance characteristic index) was the output for the simulation. The simulation was done based on the rules. The optimal parameters were found at moderate speed, high feed, and moderate DOC. Machining chips were collected for different experimental conditions. The chip form study was done. The chip surfaces were examined in the scanning electron microscope (SEM). The simulation result was validated by chip form study and SEM observation of chip.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

45-51

Citation:

Online since:

September 2023

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2023 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] V. Sivaramana, S. Sankaran, L. Vijayaraghavan, Machinability of Multiphase Microalloyed Steel, Procedia CIRP. 2 (2012) 55-59.

DOI: 10.1016/j.procir.2012.05.039

Google Scholar

[2] A. Iqbal, J. Zaini, M. M. Nauman, Machinability of Alloy Steel and Titanium Alloy under Carbon Dioxide Snow, Micro-lubrication and Hybrid Lubro-Cooling, IOP Conf. Series Materials Science and Engineering. 521 (2019)

DOI: 10.1088/1757-899X/521/1/012003

Google Scholar

[3] R. Lalbondre, P. Krishna, G. C. Mohankumar, Machinability Studies of Low alloy steels by Face Turning Method. An Experimental Investigation, Procedia Engineering. 64 (2013) 632 – 641

DOI: 10.1016/j.proeng.2013.09.138

Google Scholar

[4] N. A. Raof, J. A. Ghani, C.H. CheHaron, Machining-induced grain refinement of AISI 4340 alloy steel under dry and cryogenic conditions, Journal of Materials Research and Technology. 8 5 (2019) 4347-4353.

DOI: 10.1016/j.jmrt.2019.07.045

Google Scholar

[5] H. Jiang, C. Wang, Z. Ren, Y. Yi, L. He, X. Zhao, Influence of cutting velocity on gradient microstructure of machined surface during turning of high-strength alloy steel, Materials Science and Engineering A, 819 (2021)

DOI: 10.1016/j.msea.2021.141354

Google Scholar

[6] S.M. Agrawal, N.G. Patil, Experimental study of non edible vegetable oil as a cutting fluid in machining of M2 Steel using MQL, Procedia Manufacturing. 20 (2018) 207-212.

DOI: 10.1016/j.promfg.2018.02.030

Google Scholar

[7] A. Bhattacharyya, Metal cutting, Theory and practice, New Central Book Agency, Calcutta, India, 1984.

Google Scholar

[8] V.P. Astakhov, Tribology of metal cutting, Tribology and interface engineering series, ELSEVIER 52, Editor: B. J. Briscoe.

Google Scholar

[9] Milton C Shaw, Metal cutting principles, second edition, Oxford series on advanced manufacturing, Oxford University Press. 2009.

Google Scholar

[10] G Boothroyd, Fundamentals of metal machining and machine tools, International student edition, McGraw Hill International Book Company.

Google Scholar

[11] A Bhattacharyya, Metal cutting Theory and practice, New central.

Google Scholar