Open AccessASSESEMENT OF MACHINING PARAMETERS ON EN-24 STEEL UNDER DRY, WET AND MQL CONDITION USING MULTI COATED INSERTS
Author(s) -
S. Das Gupta,
Vaibhav Chandra,
Praveen Kumar
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1240/1/012141
Subject(s) - lubrication , mist , surface roughness , taguchi methods , machining , orthogonal array , grey relational analysis , surface finish , materials science , mechanical engineering , coolant , process engineering , metallurgy , composite material , engineering , mathematics , physics , mathematical economics , meteorology
The minimum quantity lubrication is an interesting possibility for an economical and environmentally compatible production, which unifies the functionality of coolants and lubricants with mightily less consumption of lubricants. In this experiment, minimum quantity lubrication setup on turning machine was design and developed for comparison between dry and wet conditions and their effects are considered under different parameters. This setup uses mist/flood formation of lubricants with the help of minimum quantity lubrication. This strategy is used to improve machining of the product surface quality during turning operation. The surface roughness value has been taken as an output response; the surface roughness value of the machined part was evaluated using Talysurf surface roughness tester. The design of experiment was enacted on Taguchi’s orthogonal array L9 to optimize the selected process parameters during turning of EN-24 under different conditions. The Grey relational method was also applied for optimizing purposes. The optimum conditions include a cut 0.6 mm deep, a nose radius of 0.8mm and a cutting speed of 575 rpm and the depth of cut was found out to be most dominant parameter. MQL shows better results in terms of surface roughness as compared to dry and wet conditions.