Analysis of input variables on the tool life in machining of alloy steel under controlled environment

Main Article Content

Article Sidebar

Published Nov 5, 2021
Talvinder Singh Vijay Kumar Sharma Mohit Rana Abhineet Saini

Abstract

High carbon alloy steels viz. EN31, AISI 4140, EN8 etc. typically established their applications in industry for various purposes. However, the machining of these hard materials found to be challenging owing to their corrosion and wear resistant properties [1]. These properties could result in immense temperature generation and severe vibrations during the machining which directly affects the life of the tool material [2]. Here in machining of EN31 alloy steel, an attempt has been made to find out an optimal solution of input parameters for maximum tool life and moreover to do a comprehensive analysis of temperature and tool vibrations for tool life. For experimentation work, controlled machining environment is adopted in terms of minimum quantity lubrication. Taguchi based orthogonal array is selected for experimentation while grey relational analysis technique is used for optimization.  The process variables chosen are cutting speed, feed rate, and cutting depth as machining parameters while lubricant supply rate and lubricant concentration are selected as MQL (minimum quantity lubrication) parameters. After analysis, depth of cut is termed as highly influencing factor for aforementioned conditions. Optimal conditions achieved are speed 1000 rpm, feed rate 120 mm/min, depth 0.2 mm, lubricant concentration 60 % and lubricant supply rate 180 ml/hr.

How to Cite

Singh, T. ., Sharma, V. K. ., Mohit Rana, & Saini, A. . (2021). Analysis of input variables on the tool life in machining of alloy steel under controlled environment. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/3152
Abstract 24 |

Article Details

Keywords

Temperature; Tool vibration; Tool life; Grey relational analysis

References
[1] Sukhdev S. Bhogal, Charanjeet Sindhu, Sukhdeep S. Dhami, B. S. Pabla Journal of Optimization, 2015. http://dx.doi.org/10.1155/2015/192030
[2] Subramanian M, Sakthivel M, Sooryaprakash K, Sudhakaran R. Measurement. 2013 Dec 1;46(10):4005-22, https://doi.org/10.1016/j.measurement.2013.08.015
Section
Gupta