Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert

The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining paramete...

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Main Authors: Equbal, Azhar, Equbal, Md Asif, Equbal, Md Israr, Ravindrannair, Pranav, Khan, Zahid A., Badruddin, Irfan Anjum, Kamangar, Sarfaraz, Tirth, Vineet, Javed, Syed, Kittur, M.
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Published: MDPI 2022
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Online Access:http://eprints.um.edu.my/46197/
https://doi.org/10.3390/ma15228051
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Institution: Universiti Malaya
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spelling my.um.eprints.461972024-10-25T01:16:38Z http://eprints.um.edu.my/46197/ Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert Equbal, Azhar Equbal, Md Asif Equbal, Md Israr Ravindrannair, Pranav Khan, Zahid A. Badruddin, Irfan Anjum Kamangar, Sarfaraz Tirth, Vineet Javed, Syed Kittur, M. TJ Mechanical engineering and machinery The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method. MDPI 2022-11 Article PeerReviewed Equbal, Azhar and Equbal, Md Asif and Equbal, Md Israr and Ravindrannair, Pranav and Khan, Zahid A. and Badruddin, Irfan Anjum and Kamangar, Sarfaraz and Tirth, Vineet and Javed, Syed and Kittur, M. (2022) Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert. MATERIAL, 15 (22). ISSN 1996-1944, DOI https://doi.org/10.3390/ma15228051 <https://doi.org/10.3390/ma15228051>. https://doi.org/10.3390/ma15228051 10.3390/ma15228051
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Equbal, Azhar
Equbal, Md Asif
Equbal, Md Israr
Ravindrannair, Pranav
Khan, Zahid A.
Badruddin, Irfan Anjum
Kamangar, Sarfaraz
Tirth, Vineet
Javed, Syed
Kittur, M.
Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
description The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.
format Article
author Equbal, Azhar
Equbal, Md Asif
Equbal, Md Israr
Ravindrannair, Pranav
Khan, Zahid A.
Badruddin, Irfan Anjum
Kamangar, Sarfaraz
Tirth, Vineet
Javed, Syed
Kittur, M.
author_facet Equbal, Azhar
Equbal, Md Asif
Equbal, Md Israr
Ravindrannair, Pranav
Khan, Zahid A.
Badruddin, Irfan Anjum
Kamangar, Sarfaraz
Tirth, Vineet
Javed, Syed
Kittur, M.
author_sort Equbal, Azhar
title Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
title_short Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
title_full Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
title_fullStr Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
title_full_unstemmed Evaluating CNC milling performance for machining AISI 316 stainless steel with carbide cutting tool insert
title_sort evaluating cnc milling performance for machining aisi 316 stainless steel with carbide cutting tool insert
publisher MDPI
publishDate 2022
url http://eprints.um.edu.my/46197/
https://doi.org/10.3390/ma15228051
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