Modelling of Surface Roughness in Ultra-High Precision Turning of an RGP Contact Lens Polymer

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Contact lens manufacture requires high accuracy and surface integrity. Surface roughness an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. This research work is therefore aimed at developing a predictive surface roughness model and investigate a finish cutting conditions of ONSI-56 contact lens polymer with a monocrystalline diamond cutting tool. In this work, a novel surface roughness prediction model, in which the feed rate, cutting speed and depth of cut are considered is developed. This combined process was successfully modeled using a Box–Behnken design (BBD) with response surface methodology (RSM). The effects of feed rate, cutting speed and depth of cut were investigated. Analysis of variance (ANOVA) showed that the proposed quadratic model effectively interpreted the experimental data with coefficients of determination of R2 = 0.89 and adjusted R2 = 0.84. The worse surface value was obtained at high feedrate and low spindle speed.

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183-187

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August 2017

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© 2017 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. Bolat, Machining Of Polycarbonate For Optical Applications, Middle East Technical University, (2013).

Google Scholar

[2] V. Saini, D. Sharma, S. Kalla, T. Chouhan, Optimisation of process parameter in ultra-precision diamond turning of polycarbonate material, in Proceedings of the International Conference on Manufacturing Excellence MANFEX, (2012).

Google Scholar

[3] T. Özel, Y. Karpat, Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks, Int. J. Mach. Tools Manuf. 45(4-5) (2005) 467-479.

DOI: 10.1016/j.ijmachtools.2004.09.007

Google Scholar

[4] N. Khatri, V. Mishra, R. G. V. Sarepaka, Optimization of process parameters to achieve nano level surface quality on polycarbonate, Int. J. Comput. Appl. 48(13) (2012) 39-44.

DOI: 10.5120/7412-0507

Google Scholar

[5] B. Goel, S. Singh, R. V. Sarepaka, Optimizing Single Point Diamond Turning for Mono-Crystalline Germanium Using Grey Relational Analysis, Mater. Manuf. Process. 30(8) (2015) 1018-1025.

DOI: 10.1080/10426914.2014.984207

Google Scholar

[6] G. P. H. Gubbels, Diamond turning of glassy polymers, Eindhoven University of Technology, (2006).

Google Scholar

[7] P. Benardos, G. C. Vosniakos, Predicting surface roughness in machining: a review, Int. J. Mach. Tools Manuf. 43(8) (2003) 833-844.

DOI: 10.1016/s0890-6955(03)00059-2

Google Scholar

[8] D. Singh, P. V. Rao, A surface roughness prediction model for hard turning process, Int. J. Adv. Manuf. Technol. 32(11) (2007) 1115-1124.

DOI: 10.1007/s00170-006-0429-2

Google Scholar

[9] B. Fnides, M. A. Yallese, T. Mabrouki, J. -F. Rigal, Surface roughness model in turning hardened hot work steel using mixed ceramic tool, Mechanika, 77(3) (2009) 68-73.

DOI: 10.1007/s12046-011-0007-7

Google Scholar

[10] Lagado Premium Gp Materials Available: https: /www. lagadocorp. co/products/premium-gp-materials.

Google Scholar

[11] J. Segurola, N. S. Allen, M. Edge, A. Mc Mahon, Design of eutectic photoinitiator blends for UV/visible curable acrylated printing inks and coatings, Progress Org. Coat. 37(1-2) (1999) 23-37.

DOI: 10.1016/s0300-9440(99)00052-1

Google Scholar

[12] M. Burton, K. Kurien, Effects of solute concentration in radiolysis of water, J. Phys. Chem. 63(6) (1959) 899-904.

DOI: 10.1021/j150576a031

Google Scholar

[13] A. Joglekar, A. May, Product excellence through design of experiments, Cereal Foods World, 32 (1987) 857.

Google Scholar