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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

V. V. K. Lakshmi, K. V. Subbaiah, K. Arun Vikram, K. Suresh, R. D. V. Prasad
Copyright: © 2021 |Volume: 10 |Issue: 1 |Pages: 17
ISSN: 2160-9772|EISSN: 2160-9799|EISBN13: 9781799858966|DOI: 10.4018/IJSDA.2021010103
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MLA

Lakshmi, V. V. K., et al. "Tool Wear and Temperatures Analysis While Machining Ti-6Al-4V in MQCL-MIST Environment." IJSDA vol.10, no.1 2021: pp.31-47. http://doi.org/10.4018/IJSDA.2021010103

APA

Lakshmi, V. V., Subbaiah, K. V., Vikram, K. A., Suresh, K., & Prasad, R. D. (2021). Tool Wear and Temperatures Analysis While Machining Ti-6Al-4V in MQCL-MIST Environment. International Journal of System Dynamics Applications (IJSDA), 10(1), 31-47. http://doi.org/10.4018/IJSDA.2021010103

Chicago

Lakshmi, V. V. K., et al. "Tool Wear and Temperatures Analysis While Machining Ti-6Al-4V in MQCL-MIST Environment," International Journal of System Dynamics Applications (IJSDA) 10, no.1: 31-47. http://doi.org/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%.