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Application of ANFIS in predicting TiAlN coatings flank wear

In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings were...

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Main Authors: S. Z. M., Hashim, H., Haron
Format: Conference or Workshop Item
Published: 2011
Subjects:
T58.5-58.64 Information technology
Online Access:http://eprints.utm.my/id/eprint/45596/
http://dx.doi.org/10.1109/CIMSim.2011.20
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spelling my.utm.455962017-09-04T05:08:00Z http://eprints.utm.my/id/eprint/45596/ Application of ANFIS in predicting TiAlN coatings flank wear S. Z. M., Hashim H., Haron T58.5-58.64 Information technology In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the flank wear as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy rule-based and RSM flank wear models in terms of the root mean square error (RMSE), co-efficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data. 2011 Conference or Workshop Item PeerReviewed S. Z. M., Hashim and H., Haron (2011) Application of ANFIS in predicting TiAlN coatings flank wear. In: Third International Conference on Computational Intelligence, Modelling & Simulation, 20-22 Sept. 2011, Langkawi, Malaysia. http://dx.doi.org/10.1109/CIMSim.2011.20
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 T58.5-58.64 Information technology
spellingShingle T58.5-58.64 Information technology
S. Z. M., Hashim
H., Haron
Application of ANFIS in predicting TiAlN coatings flank wear
description In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the flank wear as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy rule-based and RSM flank wear models in terms of the root mean square error (RMSE), co-efficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data.
format Conference or Workshop Item
author S. Z. M., Hashim
H., Haron
author_facet S. Z. M., Hashim
H., Haron
author_sort S. Z. M., Hashim
title Application of ANFIS in predicting TiAlN coatings flank wear
title_short Application of ANFIS in predicting TiAlN coatings flank wear
title_full Application of ANFIS in predicting TiAlN coatings flank wear
title_fullStr Application of ANFIS in predicting TiAlN coatings flank wear
title_full_unstemmed Application of ANFIS in predicting TiAlN coatings flank wear
title_sort application of anfis in predicting tialn coatings flank wear
publishDate 2011
url http://eprints.utm.my/id/eprint/45596/
http://dx.doi.org/10.1109/CIMSim.2011.20
_version_ 1643651787238932480
score 13.211869

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