Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel

This paper presents a study for the development the first and second order tool life models of micromilling hardened tool steel AISI D2 62 HRC. The models were developed in terms of cutting speed, feed per tooth and depth of cut, using response surface methodology. Central composite design (CCD) was...

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Published in:Procedia Engineering
Main Author: Saedon J.B.; Soo S.L.; Aspinwall D.K.; Barnacle A.; Saad N.H.
Format: Conference paper
Language:English
Published: Elsevier Ltd 2012
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886298812&doi=10.1016%2fj.proeng.2012.07.367&partnerID=40&md5=b99989be816bc40302b7a08b6a659af4
id 2-s2.0-84886298812
spelling 2-s2.0-84886298812
Saedon J.B.; Soo S.L.; Aspinwall D.K.; Barnacle A.; Saad N.H.
Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
2012
Procedia Engineering
41

10.1016/j.proeng.2012.07.367
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886298812&doi=10.1016%2fj.proeng.2012.07.367&partnerID=40&md5=b99989be816bc40302b7a08b6a659af4
This paper presents a study for the development the first and second order tool life models of micromilling hardened tool steel AISI D2 62 HRC. The models were developed in terms of cutting speed, feed per tooth and depth of cut, using response surface methodology. Central composite design (CCD) was employed in developing the tool life model in relation to independent variables as primary cutting parameters. All of the cutting tests were performed within specified ranges of parameters using ∅0.5 mm TiAlN microtools under dry condition. Tool life and dual-response contours of metal removal rate have been generated from these model equations. Tool life equation shows that cutting speed is the main influencing factor on the tool life, followed by feed per tooth and depth of cut. The results were presented in terms of mean values and confidence levels. The adequacy of the predictive model was verified using analysis of variance (ANOVA) at 5% significant level and found to be adequate. © 2012 The Authors.
Elsevier Ltd
18777058
English
Conference paper
All Open Access; Gold Open Access; Green Open Access
author Saedon J.B.; Soo S.L.; Aspinwall D.K.; Barnacle A.; Saad N.H.
spellingShingle Saedon J.B.; Soo S.L.; Aspinwall D.K.; Barnacle A.; Saad N.H.
Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
author_facet Saedon J.B.; Soo S.L.; Aspinwall D.K.; Barnacle A.; Saad N.H.
author_sort Saedon J.B.; Soo S.L.; Aspinwall D.K.; Barnacle A.; Saad N.H.
title Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
title_short Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
title_full Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
title_fullStr Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
title_full_unstemmed Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
title_sort Prediction and optimization of tool life in micromilling AISI D2 (∼62 HRC) hardened steel
publishDate 2012
container_title Procedia Engineering
container_volume 41
container_issue
doi_str_mv 10.1016/j.proeng.2012.07.367
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886298812&doi=10.1016%2fj.proeng.2012.07.367&partnerID=40&md5=b99989be816bc40302b7a08b6a659af4
description This paper presents a study for the development the first and second order tool life models of micromilling hardened tool steel AISI D2 62 HRC. The models were developed in terms of cutting speed, feed per tooth and depth of cut, using response surface methodology. Central composite design (CCD) was employed in developing the tool life model in relation to independent variables as primary cutting parameters. All of the cutting tests were performed within specified ranges of parameters using ∅0.5 mm TiAlN microtools under dry condition. Tool life and dual-response contours of metal removal rate have been generated from these model equations. Tool life equation shows that cutting speed is the main influencing factor on the tool life, followed by feed per tooth and depth of cut. The results were presented in terms of mean values and confidence levels. The adequacy of the predictive model was verified using analysis of variance (ANOVA) at 5% significant level and found to be adequate. © 2012 The Authors.
publisher Elsevier Ltd
issn 18777058
language English
format Conference paper
accesstype All Open Access; Gold Open Access; Green Open Access
record_format scopus
collection Scopus
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