z-logo
open-access-imgOpen Access
Optimization of cutting parameters and prediction of surface roughness during hard turning of H13 steel with minimal vegetable oil based cutting fluid application using response surface methodology
Author(s) -
Anil Raj,
K. Leo Dev Wins,
A. Varadarajan
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/577/1/012023
Subject(s) - surface roughness , response surface methodology , cutting fluid , mechanical engineering , surface finish , materials science , grinding , process (computing) , process engineering , metallurgy , computer science , composite material , machining , engineering , machine learning , operating system
The manufacturing industries in modern era are competing to reduce cost of production by employing innovative techniques, one being hard turning. In hard turning process, the work piece is heat treated to the required hardness in the initial stage itself and near net shape is arrived directly by hard turning process. Hard turning reduces manufacturing lead time by excluding the normal cost incurring processes such as, turning, heat treatment, finish grinding etc. In this experimental investigation hard turning process is assisted with minimal cutting fluid application technique, which reduces cutting fluid usage to a minimum of 6-8 ml/min. Soya bean oil based emulsion was used to make the hard turning environment friendly. The oil was prepared by adding additives, which will enhance the desirable properties of the oil for hard turning. Response surface methodology was used for optimization of cutting parameters and for the prediction of surface roughness. A central composite design was implemented to estimate the second-degree polynomial model. The cutting parameters considered for experimentation were cutting speed, feed rate and depth of cut. The surface roughness was considered parameter for prediction. Surface roughness predicted by the response Surface Methodology matched well with the experimental results.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here