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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Elsevier Ltd
2012
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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 |
_version_ |
1809677611478024192 |