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Flank wear prediction in high-speed face milling using Monte Carlo simulation method

In high-speed machining, Flank wear length is tough to predict due to significant dynamical change in the cutting zone. Therefore, using the traditional methods in predicting the flank wear may be varied from the accurate values. One of the practical alternatives is by using the Monte Carlo simulati...

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Main Authors: Al Hazza, Muataz Hazza F., Ali, Mohammad Yeakub, Omer, T.M., Juraimi, N. A, Adesta, Erry Yulian Triblas
Format: Conference or Workshop Item
Language:English
English
Published: IEOM Society 2020
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/87885/1/87885_Flank%20Wear%20Prediction%20in%20High-Speed%20Face%20Milling.pdf
http://irep.iium.edu.my/87885/2/87885_Flank%20Wear%20Prediction%20in%20High-Speed%20Face%20Milling%20SCOPUS.pdf
http://irep.iium.edu.my/87885/
http://www.ieomsociety.org/detroit2020/papers/732.pdf
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spelling my.iium.irep.878852021-01-19T01:08:28Z http://irep.iium.edu.my/87885/ Flank wear prediction in high-speed face milling using Monte Carlo simulation method Al Hazza, Muataz Hazza F. Ali, Mohammad Yeakub Omer, T.M. Juraimi, N. A Adesta, Erry Yulian Triblas T Technology (General) In high-speed machining, Flank wear length is tough to predict due to significant dynamical change in the cutting zone. Therefore, using the traditional methods in predicting the flank wear may be varied from the accurate values. One of the practical alternatives is by using the Monte Carlo simulation method. This research compares three different scenarios in predicting the flank wear in high-speed face milling under dry conditions. The experiments were conducted using Box Behnken Design (BBD) in dry machining in high-speed face milling of AISI 1050. Six scenarios have been implemented: 50, 100, 250, 500, 1000, and 2000 simulated runs. The results were analyzed and indicated that even with the complexity of the process, the Monte Carlo method gave results high accuracy as compared with the actual experimental results with an error of 0.24%, 0.16%, 0.11%, 0.09%, 0.046% and 0.047%. Determining the optimum number of runs that give the minimum number of runs is time effectiveness. Finally, the optimum number of simulation runs was determined. knowing the best number of simulations runs that sane time and increase the accuracy is essential to increase the advantages of using MC method. © IEOM Society International. IEOM Society 2020 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/87885/1/87885_Flank%20Wear%20Prediction%20in%20High-Speed%20Face%20Milling.pdf application/pdf en http://irep.iium.edu.my/87885/2/87885_Flank%20Wear%20Prediction%20in%20High-Speed%20Face%20Milling%20SCOPUS.pdf Al Hazza, Muataz Hazza F. and Ali, Mohammad Yeakub and Omer, T.M. and Juraimi, N. A and Adesta, Erry Yulian Triblas (2020) Flank wear prediction in high-speed face milling using Monte Carlo simulation method. In: 5th North American International Conference on Industrial Engineering and Operations Management, 10th-14h August 2020, Detroit, MI, USA. http://www.ieomsociety.org/detroit2020/papers/732.pdf
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Al Hazza, Muataz Hazza F.
Ali, Mohammad Yeakub
Omer, T.M.
Juraimi, N. A
Adesta, Erry Yulian Triblas
Flank wear prediction in high-speed face milling using Monte Carlo simulation method
description In high-speed machining, Flank wear length is tough to predict due to significant dynamical change in the cutting zone. Therefore, using the traditional methods in predicting the flank wear may be varied from the accurate values. One of the practical alternatives is by using the Monte Carlo simulation method. This research compares three different scenarios in predicting the flank wear in high-speed face milling under dry conditions. The experiments were conducted using Box Behnken Design (BBD) in dry machining in high-speed face milling of AISI 1050. Six scenarios have been implemented: 50, 100, 250, 500, 1000, and 2000 simulated runs. The results were analyzed and indicated that even with the complexity of the process, the Monte Carlo method gave results high accuracy as compared with the actual experimental results with an error of 0.24%, 0.16%, 0.11%, 0.09%, 0.046% and 0.047%. Determining the optimum number of runs that give the minimum number of runs is time effectiveness. Finally, the optimum number of simulation runs was determined. knowing the best number of simulations runs that sane time and increase the accuracy is essential to increase the advantages of using MC method. © IEOM Society International.
format Conference or Workshop Item
author Al Hazza, Muataz Hazza F.
Ali, Mohammad Yeakub
Omer, T.M.
Juraimi, N. A
Adesta, Erry Yulian Triblas
author_facet Al Hazza, Muataz Hazza F.
Ali, Mohammad Yeakub
Omer, T.M.
Juraimi, N. A
Adesta, Erry Yulian Triblas
author_sort Al Hazza, Muataz Hazza F.
title Flank wear prediction in high-speed face milling using Monte Carlo simulation method
title_short Flank wear prediction in high-speed face milling using Monte Carlo simulation method
title_full Flank wear prediction in high-speed face milling using Monte Carlo simulation method
title_fullStr Flank wear prediction in high-speed face milling using Monte Carlo simulation method
title_full_unstemmed Flank wear prediction in high-speed face milling using Monte Carlo simulation method
title_sort flank wear prediction in high-speed face milling using monte carlo simulation method
publisher IEOM Society
publishDate 2020
url http://irep.iium.edu.my/87885/1/87885_Flank%20Wear%20Prediction%20in%20High-Speed%20Face%20Milling.pdf
http://irep.iium.edu.my/87885/2/87885_Flank%20Wear%20Prediction%20in%20High-Speed%20Face%20Milling%20SCOPUS.pdf
http://irep.iium.edu.my/87885/
http://www.ieomsociety.org/detroit2020/papers/732.pdf
_version_ 1690370776143757312
score 13.211869

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