RT Journal Article
SR Electronic
A1 Harhout, Riad
A1 Gaceb, Mohamed
A1 Haddad, Sofiane
A1 Aguib, Salah
A1 Bloul, Benattia
A1 Guebli, Adelhamid
T1 Predictive modelling and optimisation of surface roughness in turning of AISI 1050 steel using polynomial regression
JF Manufacturing Technology Journal
YR 2020
VO 20
IS 5
SP 591
OP 602
UL https://journalmt.com/artkey/mft-202005-0007.php
AB Surface integrity plays an important role in the functional performance of mechanical components and is one of the most particular consumer requirements in machined parts. Customarily, surface roughness is considered to be the principal parameter in evaluating surface integrity and surface quality on machined parts and has a significant effect on service reliability and component durability. It is dependent on a large number of machining parameters, such as tool geometry (i.e. nose radius, edge geometry, rake angle, etc.) and cutting conditions (feed, cutting speed, depth of cut). The effects of these parameters have not however been adequately quantified. So in order to identify the optimum combination of cutting conditions corresponding to better roughness, accurate predictive models for surface roughness must, as a first step, be constructed. An investigation in this regard has been conducted to address the surface integrity optimisation and prediction issue by applying the polynomial regression method for a variety of experiments and cutting conditions. A higher correlation coefficient (R?) was obtained with a cubic regression model, which had a value of 0.9480 for Ra. The use of the response surface optimisation and composite desirability show that the optimal set of machining parameters values are (250m/min, 0.2398 mm/rev and 2.3383 mm) for cutting speed, feed and depth of cut, respectively. The optimised surface roughness parameter and productivity are Ra =2.7567 ?m and Q = 95.341*103 mm3/ min, respectively. Results show that the models developed can accurately predict the roughness on the basis of measured cutting conditions as input parameters, and can also be used to control the surface roughness by making a comparison between measured and estimated values. Furthermore, operators can benefit from the proposed models if the aim is the reverse determination of the cutting conditions corresponding to the requested roughness profile.