Experimental investigation on variation of output responses of as cast TiNiCu shape memory alloys using wire EDM

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2017

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Roy, A.
Narendra, Nath, S.
Nedelcu, D.

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Abstract

The present research study is emphasized on investigation of machining characteristics of TiNiCu shape memory alloys using Wire EDM and analyzes the effect of input process parameters on the quality of the machined surfaces. Complications associated with the machining process results in degradation of surface quality which notably is not avoidable. But proper optimization of the process parameters may help to achieve the desired or near-perfect quality of these machined products. Experiments were conducted as per Taguchi s L16 orthogonal array considering four input parameters at four levels of operation for Ti50Ni40Cu10, Ti45Ni45Cu10 and Ti40Ni50Cu10 shape memory alloys. Zinc coated brass wire is used as the wire electrode in this study. Material removal rate (MRR) and surface roughness (SR) of the machined samples are considered as the output responses of these alloys. Optimum MRR and SR achieved while machining these alloys are 6.9431 mm3/min and 1.060 m respectively for Ti50Ni40Cu10. Keeping in mind the productivity of the machining process, formation of recast layer of these alloys are also studied for optimum material removal rate, which led us to the conclusion that Ti45Ni45Cu10 alloy exhibits a surprising aversion towards formation of recast layer as compared to its counterparts. Also, Ti45Ni45Cu10 displayed least average recast layer thickness (RLT) of 9.16 m compared to 23.93 m and 21.65 m for Ti50Ni40Cu10 and Ti40Ni50Cu10 respectively. Pulse on time (120 s) and wire feed (10 m/min) are found as the most influential Wire EDM process parameters against pulse off time and wire feed for an optimum material removal rate and for a better surface finish, pulse on time (100 s) and pulse off time (30 s) against servo voltage and wire feed respectively. 2017, ModTech Publishing House. All rights reserved.

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International Journal of Modern Manufacturing Technologies, 2017, Vol.9, 1, pp.90-101

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