Investigation of Optimal Processing Condition for Abrasive Water Jet Machining for Stainless Steel AISI 304 Using Grey Relational Analysis Coupled with S/N Ratio

Abstract:

As the population of the world is continuously increasing, the demand of the mechanical manufactured products is also increasing. Machining is the most important process in any mechanical manufacturing, and in machining two factors i.e. Material removal rate (MRR) and Surface roughness (SR) are the most important responses. If the MRR will be high, the product will get desired shape in minimum time so the production rate will be high, but we could not scarify with the surface finishing also because in close tolerance limit parts like in automobile industry if the surface is rough exact fit cannot take place. So here aim is to maximise MRR and minimise surface roughness and process control variable are taken to be transverse speed, standoff distance, abrasive flow rate, and water pressure. Here Grey relational analysis is used to convert multi responses into single response and optimal parameter setting and most significant parameter is found with the help of S/N ratio.

• Periodical: Applied Mechanics and Materials (Volumes 592-594)
• Pages: 438-443
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.592-594.438
• Online since: July 2014
• Authors: Diksha Singh*, Vedansh Chaturvedi
• Keywords: AWJM, Grey Relational Analysis, MRR, S/N Ratio, SR, Stainless Steel AISI304

References

[1] Hashish, M., (1991). Advances in composite machining with Abrasive – Waterjets. Process. Manufacturing Comp. Mat. Volume 49 (27). pp.93-111.

[2] Momber, A.W., & Kovacevic, R. (1992). Principle of abrasive water jet machining, Springer Verlag limited. London.

[3] Ramulu, M. and Arola ,D. (1994). The influences of AWJM condition on the surface quality of Graphite . Int .J. Mach. tools manufacturing . Volume 34 (3). pp.295-313.

[4] Siores, E. Wong, W.C.K., Chen L., Wager, J.C. (1996). Enhancing abrasive water jet cutting of ceramic by head oscillation technique. Ann CIRP. Volume 45(1). pp.215-218.

DOI: 10.1016/s0007-8506(07)63073-x

[5] Wang, j. & Wong W.C.K. (1999). A study of water jet cutting of metallic coated sheet steels, international journal of machine tool and manufacturing. Volume (39). pp.855-870.

DOI: 10.1016/s0890-6955(98)00078-9

[6] Rowland's, H, Antony, J. and knowler, G., 2000. An application of experimental design for process optimization,. The TQM magazine, volume 12(2), PP 78-83.

[7] Al-Aomar Reid, 2002, ". A robust simulation-based Multicriterion optimization methodology; proceedings of the 2002 winter simulation conference.

DOI: 10.1109/wsc.2002.1166492

[8] Adnan Akkurt, Mustafa Kemal Kulekci, Ulvi Seker and Fevzi Ercan (2004). Effect of feed rate on surface roughness in abrasive water jet cutting application. Journal of material processing technology. Volume (147). pp.389-396.

DOI: 10.1016/j.jmatprotec.2004.01.013

[9] Azmir, M.A. and Ahsam, A.K., (2008). Investigation on glass/epoxy composite surface machined by abrasive water jet machining "journal of material processing technology. Volume 198 (1). pp.122-128.

DOI: 10.1016/j.jmatprotec.2007.07.014

[10] Ulas Caydas and Ahmet Hascalik (2008). A study on surface roughness in AWJM process using artificial neural network and regression analysis methodology. Journal of materials processing technology. Volume (202). pp.574-582.

DOI: 10.1016/j.jmatprotec.2007.10.024

[11] F.C. Tsai, B.H. Yan, C.Y. Kuan and F.Y. Huang (2008).

[12] Asif iqbal, Naeem V. Dar and Ghulam Hussain (2011). Optimization of abrasive water jet cutting of ductile materials. Journal of Wuhan University of Technology-Mater sci ed. Volume 26(1). pp.88-92.

DOI: 10.1007/s11595-011-0174-8

[13] I. Karakut, G. Aydin, and K. Aydiner (2011). A machinability study of granite using abrasive water jet cutting technology. Gazi University journal of science Volume (24) pp.143-151.