Investigation on the effect of process parameters and optimization using GRA under biodegradable oil based MQL in machining.

Improving the machining efficiency in form of better surface quality and improved tool life is always challenging. This is due to the high amount of heat produced during machining. Hence effective measures are required to control the heat generated. In this response, conventional coolants are applied to achieve cooling and lubricating effect in machining. However, these cutting fluids affect the operator's health and environmental resources. Considering this, the aim of the work is to assess the role of minimum quantity lubrication (MQL) compared to dry and flood cooling. The turning tests are performed under the defined machining environments to find the most suitable combination of parameters to minimize the tool wear and surface roughness. The Taguchi orthogonal arrays are incorporated to evaluate the 27 different combinations of variable working parameters and assess them. The lubricating conditions are varied as per the experimental design. Three different lubricating conditions are used: dry lubrication, flood lubrication and MQL. A statistical analysis of obtained results is performed to understand the effect of machining variables on output responses. A multi-response optimization is conducted to minimize surface roughness and tool wear by utilizing Grey relational analysis. Finally, the most suitable combination of parameters is suggested based on the obtained grey relational grade. [ABSTRACT FROM AUTHOR]