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

In 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...

Full description

Saved in:
Bibliographic Details
Main Authors: Mohd. Hashim, Siti Zaiton, Jaya, A. S. M., Haron, H., Muhamad, M. R., Rahman, M. N. A.
Format: Conference or Workshop Item
Published: 2012
Online Access:http://eprints.utm.my/id/eprint/34091/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.34091
record_format eprints
spelling my.utm.340912017-09-07T04:20:33Z http://eprints.utm.my/id/eprint/34091/ Fuzzy rule-based model to estimate surface roughness and wear in hard coatings Mohd. Hashim, Siti Zaiton Jaya, A. S. M. Haron, H. Muhamad, M. R. Rahman, M. N. A. In 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 Conference or Workshop Item PeerReviewed Mohd. Hashim, Siti Zaiton and Jaya, A. S. 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. In: 2012 IEEE International Conference on Systems, Man & Cybernetics.
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/
description In 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 Conference or Workshop Item
author Mohd. Hashim, Siti Zaiton
Jaya, A. S. M.
Haron, H.
Muhamad, M. R.
Rahman, M. N. A.
spellingShingle Mohd. Hashim, Siti Zaiton
Jaya, A. S. M.
Haron, H.
Muhamad, M. R.
Rahman, M. N. A.
Fuzzy rule-based model to estimate surface roughness and wear in hard coatings
author_facet Mohd. Hashim, Siti Zaiton
Jaya, A. S. M.
Haron, H.
Muhamad, M. R.
Rahman, M. N. A.
author_sort Mohd. Hashim, Siti Zaiton
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/34091/
_version_ 1643649511077183488
score 13.211869