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Fuzzy rule-based model to estimate surface roughness and wear in hard coatings

n this paper, a new approach in predicting the surface roughness and flank wear of hard coatings using fuzzy rule-based model is implemented. Hard coatings is important for cutting tool due to its excellent performances in 800°C temperature during high speed machining. The coating process were run u...

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Main Authors:	Jaya, A.S.M., Hashim, S.Z.M., Haron, H., Muhamad, M.R., Rahman, M.N.A.
Format:	Article
Published:	2012
Subjects:	Q Science
Online Access:	http://eprints.utm.my/id/eprint/47017/ http://dx.doi.org/10.1109/ICSMC.2012.6377873
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spelling	my.utm.470172017-08-06T03:47:08Z http://eprints.utm.my/id/eprint/47017/ Fuzzy rule-based model to estimate surface roughness and wear in hard coatings Jaya, A.S.M. Hashim, S.Z.M. Haron, H. Muhamad, M.R. Rahman, M.N.A. Q Science n this paper, a new approach in predicting the surface roughness and flank wear of hard coatings using fuzzy rule-based model is implemented. Hard coatings is important for cutting tool due to its excellent performances in 800°C temperature during high speed machining. The coating process were run using Physical Vapor Deposition (PVD) magnetron sputtering process. An experiment matrix called Response Surface Methodology (RSM) was used to collect data based on optimized data point. Sputtering power, substrate bias voltage and substrate temperature were used as the variables, and coating roughness and flank wear as the output responses of the coating process. The collected experimental data were used to develop fuzzy rules. Five triangular membership functions (MFs) for input variables and nine MFs for output responses were used in constructing the models. The results of fuzzy rule-based models were compared against the experimental result based on the percentage error, co-efficient determination (R2) and model accuracy. The rule-based model for coating roughness showed an excellent result with respective smallest percentage error, R2 and model accuracy were 0.85%, 0.953 and 89.20% respectively. Meanwhile, the fuzzy flank wear model indicated 6.38%, 0.91 and 81.79% for smallest percentage error, R2 and model accuracy. Thus, fuzzy logic can be a good alternative in predicting coating roughness and flank wear in hard coatings. 2012 Article PeerReviewed Jaya, A.S.M. and Hashim, S.Z.M. and Haron, H. and Muhamad, M.R. and Rahman, M.N.A. (2012) Fuzzy rule-based model to estimate surface roughness and wear in hard coatings. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics . ISSN 1062-922X http://dx.doi.org/10.1109/ICSMC.2012.6377873 10.1109/ICSMC.2012.6377873
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	Q Science
spellingShingle	Q Science Jaya, A.S.M. Hashim, S.Z.M. Haron, H. Muhamad, M.R. Rahman, M.N.A. Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
description	n this paper, a new approach in predicting the surface roughness and flank wear of hard coatings using fuzzy rule-based model is implemented. Hard coatings is important for cutting tool due to its excellent performances in 800°C temperature during high speed machining. The coating process were run using Physical Vapor Deposition (PVD) magnetron sputtering process. An experiment matrix called Response Surface Methodology (RSM) was used to collect data based on optimized data point. Sputtering power, substrate bias voltage and substrate temperature were used as the variables, and coating roughness and flank wear as the output responses of the coating process. The collected experimental data were used to develop fuzzy rules. Five triangular membership functions (MFs) for input variables and nine MFs for output responses were used in constructing the models. The results of fuzzy rule-based models were compared against the experimental result based on the percentage error, co-efficient determination (R2) and model accuracy. The rule-based model for coating roughness showed an excellent result with respective smallest percentage error, R2 and model accuracy were 0.85%, 0.953 and 89.20% respectively. Meanwhile, the fuzzy flank wear model indicated 6.38%, 0.91 and 81.79% for smallest percentage error, R2 and model accuracy. Thus, fuzzy logic can be a good alternative in predicting coating roughness and flank wear in hard coatings.
format	Article
author	Jaya, A.S.M. Hashim, S.Z.M. Haron, H. Muhamad, M.R. Rahman, M.N.A.
author_facet	Jaya, A.S.M. Hashim, S.Z.M. Haron, H. Muhamad, M.R. Rahman, M.N.A.
author_sort	Jaya, A.S.M.
title	Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
title_short	Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
title_full	Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
title_fullStr	Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
title_full_unstemmed	Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
title_sort	fuzzy rule-based model to estimate surface roughness and wear in hard coatings
publishDate	2012
url	http://eprints.utm.my/id/eprint/47017/ http://dx.doi.org/10.1109/ICSMC.2012.6377873
_version_	1643652210298454016
score	13.211869

Fuzzy rule-based model to estimate surface roughness and wear in hard coatings

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