Abstract
Composites based on agricultural residues are extensively used in engineering applications because of high mechanical strength accompanied by the low weight factor. Drilling is the universally used machining process in automobile and structural industries. The drilling in polymeric composites is an unavoidable operation for facilitating the assembly parts due to the reason that gluing is quite complex and non metallic nature of materials. The objective of this study is to measure and analyze the cutting conditions which influence the thrust force, torque and delamination factor in drilling of the zea fiber reinforced polyester composites. The parameters considered are spindle speed, feed rate and drill bit diameter. The drilling experiments were performed based on a full factorial design of experiments and artificial neural network model was developed to predict the influence of cutting parameters on thrust force, torque and delamination factor.
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References
Ndazi B, Tesha J V, and Bisanda E T N, J Mater Sci 41 (2006) 6984.
Ashori A, and Nourbakhsh A, Waste Manag 30 (2010) 680.
Nourbakhsh A, and Ashori A, Bioresour Technol 101 (2010) 2525.
Balaji N S, and Jayabal S, Proc Inst Mech Eng Part E (2014). doi:10.1177/0954408914539939.
Arul S, Vijayaraghavan L, Malhotra S K, and Krishnamurthy R, Int J Mach Tools Manuf 46 (2006) 252.
Khashaba U A, Seif M A, and Elhamid M A, Compos Part A 38 (2007) 61.
Abrao A M, Faria P E, Rubio J C, Reis P, and Davim J P, J Mater Process Technol 186 (2007) 1.
Bajpai P K, and Singh I, J Reinf Plast Compos (2013). doi:10.1177/0731684413492866
Jayabal S, and Natarajan U, Bull Mater Sci 34 (2011) 563.
Jayabal S, Natarajan U, and Sekar U, Int J Adv Manuf Technol 55 (2011) 263.
Valarmathi T N, Palanikumar K, and Latha B, Measurement, 46 (2013) 1220.
Zhang Z, and Friedrich K, Compos Sci Technol 63 (2003) 2029.
Hayajneh M T, Hassan A M, and Mayyas A T, J Alloys Compd 478 (2009) 559.
Jayabal S, Rajamuneeswaran S, Ramprasath R, and Balaji N S, Trans Indian Inst Metals 66 (2013) 247.
Chakraborty D, Mater Des 26 (2005) 1.
Jain S, and Yang D C H, J Manuf Sci Eng 115 (1993) 398.
Koenig W, Wulf C, Grass P, and Willerscheid H, CIRP Ann Manuf Technol 34 (1985) 537.
Balaji N S, Jayabal S, Kalyana Sundaram S, Rajamuneeswaran S, and Suresh P, Adv Mater Res 984 (2014) 185.
Mishra R, Malik J, Singh I, and Davim J P, Mater Des 31 (2010) 2790.
Gaitonde V N, Karnik S R, and Davim J P, Mater Manuf Process 23 (2008) 377.
Rajamurugan T V, and Shanmugam K, J Emerg Sci Technol 2 (2011) 31.
Palanikumar K, Prakash S, and Shanmugam K, Mater Manuf Process 23 (2008) 858.
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The authors have to thank Prof. P. Thirumal and S. Ananthakumar of Government College of Engineering, Bargur, Tamilnadu, India for providing experimental and lab supports.
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Balaji, N.S., Jayabal, S. & Kalyana Sundaram, S. A Neural Network Based Prediction Modeling for Machinability Characteristics of Zea Fiber-Polyester Composites. Trans Indian Inst Met 69, 881–889 (2016). https://doi.org/10.1007/s12666-015-0571-3
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DOI: https://doi.org/10.1007/s12666-015-0571-3