Modeling and analysis of process parameters in machining AISI 304 stainless steel using fuzzy logic

K. Rajasekhar*, N. Naresh**
* -** Assistant Professor, Department of Mechanical Engineering, N.B.K.R. Institute of Science and Technology, Vidyanagar, India.
Periodicity:February - April'2014
DOI : https://doi.org/10.26634/jme.4.2.2681

Abstract

Analysis of surface roughness and Material Removal Rate play vital roles in machining Austenitic steels. Austenitic stainless steels have high ductility, low yield stress and relatively high ultimate tensile strength, when compared to typical carbon steel. In the present work, prediction of surface roughness and material removal rate in milling of AISI 304 stainless steel is carried out using fuzzy logic. 27 experiments based on Taguchi's L orthogonal array were carried out 27 involving three machining parameters namely, cutting speed, feed rate and depth of cut, each defined at three levels. Subsequently, the prediction model was developed using triangular fuzzy logic membership function. It is found that the predicted values of proposed responses such as surface roughness and Material Removal Rate are very close to the experimental values within the chosen ranges of the process parameters. The statistical analysis using ANOVA on machining parameters are also presented and discussed.

Keywords

Milling, Modeling, Taguchi Orthogonal Array, Fuzzy Logic, Surface Roughness, MRR; Tungsten Carbide End Mill, ANOVA.

How to Cite this Article?

Rajasekhar, K., & Naresh, N. (2014). Modeling and Analysis of Process Parameters in Machining AISI 304 Stainless Steel Using Fuzzy Logic. i-manager's Journal on Mechanical Engineering, 4(2), 18-26. https://doi.org/10.26634/jme.4.2.2681

References

[1]. Novak, C.J., Peckner, D. and Bernstein, I.M. (1977). Handbook of stainless steels, McGraw-Hill, New York.
[2]. El-Tamimi, A.M. and El-Hossainy, T.M. (2008). “Investigating the tool life, cutting force components, and surface roughness of AISI 302 stainless steel material under oblique machining”, Materials and Manufacturing Processes, Vol. 23, pp. 427–438.
[3]. Ciftci, I. (2006). “Machining of austenitic stainless steels using CVD multi-layer coated cemented carbide tools”, Tribology International, Vol. 39, pp. 565–569.
[4]. Kulkarni, A., Joshi, G. and Sargade, V.G. (2013). ”Design optimisation of cutting parameters for turning of AISI 304 austenitic stainless steel using Taguchi method”, Indian Journal of Engineering & Materials Sciences, Vol. 20, pp. 252-258.
[5]. Korkut, I., Kasap, M., Ciftci, I. and Seker, U. (2004). “Determination of optimum cutting parameters during machining of AISI 304 austenitic stainless steel”, Materials and Design, Vol. 25, No. 4, pp. 303–305.
[6]. Lin, T. (2002). “Experimental design and performance analysis of TiN-coated carbide tool in face milling stainless steel”, Journal of Materials Processing Technology, Vol. 127, No. 1, pp. 1–7.
[7]. Abou-El-Hossein, K.A. and Yahya, Z. (2005). “Highspeed end-milling of AISI 304 stainless steels using new geometrically developed carbide inserts”, Journal of Materials Processing Technology, Vol. 162/163, pp. 596–602.
[8]. Chockalingam, P. and Hong Wee, L. (2012). “Surface roughness and tool wear study on milling of AISI 304 stainless steel using different cooling conditions”, International Journal of Engineering and Technology, Vol. 2, No. 8, pp. 1386-1391.
[9]. Nordin, M., Sundström, R., Selinder, T.I. and Hogmark, S. (2000). “Wear and failure mechanisms of multilayered PVD TiN/TaN coated tools when milling austenitic stainless steel”, Surface and Coating Technology, Vol. 133/134, pp. 240-246.
[10]. Tang, Y.S. and Wang, Y.S. (1994). “An adaptive fuzzy control system for turning operations”, International Journal of Machine Tools and Manufacture, Vol. 33, No. 6, pp. 761–771.
[11]. Fang, X.D. (1995). “Expert system support fuzzy diagnosis of finish turning process states”, International Journal of Machine Tools and Manufacture, Vol. 35, No. 6, pp. 913–924.
[12]. Ramesh, S., Karunamoorthy, L. and Palanikumar, K. (2008). “'Fuzzy modeling and analysis of machining parameters in machining titanium alloy”, Materials and Manufacturing Processes, Vol. 23, pp. 439–447.
[13]. Palanikumar, K., Karunamoorthy, L., Karthikeyan, R. and Latha, B. (2006). “Optimisation of machining parameters in turning GFRP composites using a carbide (K10) tool based on the Taguchi method with fuzzy logics”, Metals and Materials, Vol. 12, No. 6, pp. 483–491.
[14]. Jiao, Y., Lei, S., Pei, Z.J. and Lee, E.S. (2004). “Fuzzy adaptive networks in machining process modeling surface roughness prediction for turning operations”, International Journal of Machine Tools and Manufacture, Vol. 44, pp. 1643–1651.
[15]. Dweiri, F., Al-Jarrah, M. and Al-Wedyan, H. (2003). ”Fuzzy surface roughness modeling of CNC down milling of Alumic-79”, Journal of Materials Processing Technology, Vol. 133, No. 3, pp. 266–275.
[16]. Ronei Peres, C., Elias Haber Guerra, R., Haber Haber, R., A. and Ros, S. (1999). “Fuzzy model and hierarchical fuzzy control integration: an approach for milling process optimisation”, Computers in Industry, Vol. 39, No. 3, pp. 199-207.
[17]. Razali, S.Z., Wong, S.V. and Ismail, N. (2011). “Fuzzy Logic Modeling For Peripheral End Milling Process”, Materials Science and Engineering, Vol. 17, doi:10.1088 /1757-899X/17/1/012050.
[18]. Latha, B. and Senthilkumar, V.S. (2009). “Fuzzy rule based modeling of drilling parameters for delamination in drilling GFRP composites”, Journal of Reinforced Plastics and Composites, Vol. 28, pp. 951– 964.
[19]. Zafer, T. and Sezgin, Y. (2004). “Investigation of the cutting parameters depending on process sound during turning of AISI 304 austenitic stainless steel”, Materials and Design, Vol. 25, pp. 507–513.
[20]. Zadeh, L.A. (2008). “Is there a need for fuzzy logic?”', International Journal of Information Sciences, Vol. 178, pp. 2751-2779.
[21]. Kao, C.C., Shih, A.J. and Miller, S.F. (2008). “Fuzzy Logic Control of Micro-hole Electrical Discharge Machining”, Journal of Manufacturing Science and Engineering, Vol. 130, doi: 10.1115/1.2977827.
[22]. Tzeng, Y. and Chen, F. (2007). “Multi-objective optimisation of high-speed Electrical Discharge Machining process using a Taguchi fuzzy-based approach”, Materials and Design, Vol. 28, No. 4, pp. 1159–1168.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.