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Tool Wear and Temperatures Analysis While Machining Ti-6Al-4V in MQCL-MIST Environment

Tool Wear and Temperatures Analysis While Machining Ti-6Al-4V in MQCL-MIST Environment
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Author(s): V. V. K. Lakshmi (Department of Mechanical Engineering, GITAM Institute of Technology, Visakhapatnam, India), K. V. Subbaiah (Department of Mechanical Engineering, Andhra University, India), K. Arun Vikram (Department of Mechanical Engineering, GITAM Institute of Technology, Visakhapatnam, India), K. Suresh (Department of Mechanical Engineering, GITAM Institute of Technology, Visakhapatnam, India)and R. D. V. Prasad (Department of Mechanical Engineering, GITAM Institute of Technology, Visakhapatnam, India)
Copyright: 2021
Volume: 10
Issue: 1
Pages: 17
Source title: International Journal of System Dynamics Applications (IJSDA)
Editor(s)-in-Chief: Ahmad Taher Azar (College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)
DOI: 10.4018/IJSDA.2021010103

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Abstract

Sustainable machining of titanium alloys have deficiency of studies on the built-up edges over the cutting tools and temperature correlation in minimum quantity cooling lubrication (MQCL) environment. Researchers focused on experimentation in dry, wet, and MQL (minimum quantity lubrication) conditions to analyze surface finish, cutting forces, and metal removal rates. This work focuses on the study of cutting parameters effects on temperatures and tool wear analysis by consideration of individual response and their optimality basing on signal-to-noise ratios. Efficacy of process parameters on wear of tool and temperatures requires a comprehensive understanding. An elaborated tool wear analysis is carried based on the microscopic flank wear investigations. Machining of Ti-6Al-4V alloy is carried in the environment of MQCL in form of mist using semi-synthetic fluid. Correlation study of tool wear with regard to temperatures is analyzed and regression models generated on tool wear and cutting temperatures individually showed 83% of goodness-of-fit and correlation regression is 85%.

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