267
Views
19
CrossRef citations to date
0
Altmetric
Research Article

Machinability evaluation during machining of AISI 52100 steel with textured tools under Minimum Quantity Lubrication – A comparative study

, , &
Pages 1761-1768 | Received 18 Jul 2019, Accepted 02 Jul 2020, Published online: 12 Aug 2020
 

ABSTRACT

Metal cutting industries are facing problems like poor surface quality and high tool wear in dry turning of AISI 52100 steel. In the present study, Minimum Quantity Lubrication (MQL) cooling technique along with textured tools was investigated to address these problems in turning of AISI 52100 steel. In this work, textured tools performance was evaluated during turning of AISI 52100 steel under MQL and compared the obtained results with the untextured tools under MQL, conventional cooling techniques, respectively. From experimental results, it was found that surface textured tools under MQL condition significantly reduced the cutting zone temperature (Tm), tool flank wear (Vb), and surface roughness (Ra) when compared to the other cutting conditions, respectively. Surface textured tools combined with MQL cooling significantly reduced the ‘Tm’, ‘Vb’, and ‘Ra’ to a maximum of 25%, 40%, and 42%, respectively over the conventional tools with conventional cooling. Scanning Electron Microscope (SEM) analysis revealed that fewer surface defects on machined surface and less built-up-edge (BUE) were found in surface textured tools over the untextured tools, respectively.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 561.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.