Optimization of Controlling Factors on Tool Wear When Coolant Mixed with Additives on Turning of Mg-Y Alloy
K. Ramadoss1, R. Elansezhian2, S. Jayabal3

1K. Ramadoss, Research Scholar, Department of Mechanical Engineering, Pondicherry Engineering College, Puducherry, India.
2R. Elansezhian, Associate Professor, Department of Mechanical Engineering, Pondicherry Engineering College, Puducherry, India.
3S. Jayabal, Associate Professor, Department of Mechanical Engineering, Alagappa Chettiar Government College of Engineering and Technology, Karaikudi (Tamil Nadu), India.

Manuscript received on 18 August 2018 | Revised Manuscript received on 27 August 2018 | Manuscript published on 30 August 2018 | PP: 21-25 | Volume-7 Issue-6, August 2018 | Retrieval Number: F5430087618/18©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The output parameters like surface roughness, the tool job interface temperature, metal removal rate, and tool wear during turning operation are depending upon the input factors of the turning operation. In this paper a magnesium alloy was subjected into turning operation on a medium speed lathe to observe the tool wear. The chance of fire hazard and sticking of tool with magnesium alloy is reduced by introducing Minimum quantity lubrication technique. The lubricoolents are mixed with nano sized additives of Silicon cabide, Coppr oxide and Titanium oxide. Since input factors are in more numbers, the initial optimization of feed rate, quantity of supplied coolent ,mixed nano additive with optimized quantity, the optimized nano Copper Oxide was further mixed with surfactants such as Sodium Dodecylsulfat (SDS). Cetyl Trimethyl Ammonium Bromide (CTAB) and Zwitterionic at a concentration of 1g/lit and 2g/lit. A new magnesium alloy yitrium and calcium composition was subjected to turning operation with different machining parameters and with different cutting tips for analysis of surface roughness, Temperature developed, Rate of metal removal and tool wear . Optimization was carried out by using Taguchi method. The optimized values of Speed, Feed rate, Type of nanoparticle with concentration, and Type of surfactant were obtained. The mixing of surfactant contributed considerably in reduction of nano particle usage. The model based predicted value and Experimental values are very close to each other.
Keywords: Surface Roughness, Metal Removal Rate, Magnesium Alloy, Nano Additives, Surfactants, Minimum Quantity Lubrication. Tool Wear.

Scope of the Article: Discrete Optimization