Optimization of Tool wear and Surface Roughness in Turning Titanium (Ti-6Al-4V) Alloy; NFMQCF Technique

Volume 9 Issue 2 February - April 2019

Research Paper

Optimization of Tool wear and Surface Roughness in Turning Titanium (Ti-6Al-4V) Alloy; NFMQCF Technique

Sivakoteswararao Katta*, G. Chaitanya**, B. Ravi Shankar ***

* Research Scholar, Department of Mechanical Engineering,Acharya Nagarjuna University, Guntur,Andhra Pradesh, India.

** Associate Professor, Department of Mechanical Engineering, RVR&JC College of Engineering, Guntur, Andhra Pradesh, India.

*** Associate Professor, Department of Mechanical Engineering, Bapatla Engineering College, Bapatla, Andhra Pradesh, India.

Katta, S., Chaitanya, G., and Shankar, B. R.(2018). Optimization of Tool wear and Surface Roughness in Turning Titanium (Ti-6Al-4V) Alloy; NFMQCF Technique.i-manager’s Journal on Mechanical Engineering, 9(2), 21-34. https://doi.org/10.26634/jme.9.2.14812

Abstract

In today's machining applications, nanofluids created a revolution by replacing the various metal cutting fluids used in manufacturing industries, due to its distinct properties such as high thermal conductivity and lubrication. The optimization was done based on the experimentation on surface roughness and tool wear. To get optimized results the technique used was Grey Rational Analysis (GRA), Principle Composite Analysis (PCA), and Response Surface Methodology (RSM) optimization techniques on the turning of Titanium (Ti-Al-4V) alloy with the Nanofluid based Minimum Quantity Cutting Fluid (NFMQCFT) Technique. Here, Graphene nanoparticles are used to mix with the vegetable oil based (Soya Bean) cutting fluid. The experiment has been done by using several machining parameters such as feed rate, cutting speed, depth of cut, etc. An analysis has been made to evaluate the machining parameters for surface roughness values (Ra) and Tool wear based on the actual series of experiments with uncoated carbide tool. The outcomes state that the feed rate has a greater influence on the values of surface roughness as compared to cutting speed. The predicted results are identical to the experimental values. Since this research has multi-objective, these developed models using response surface methodology, grey rational analysis, and principle composite analysis can be used for evaluation of surface roughness and tool wear.