NDLI: Hybrid RSM-fuzzy modeling for hardness prediction of TiAlN coatings

Content Provider	IEEE Xplore Digital Library
Author	Jaya, A.S.M. Muhamad, M.R. Rahman, M.N.A. Napiah, Z.A.F.M. Hashim, S.Z.M. Haron, H.
Copyright Year	2011
Description	Author affiliation: Fac. of Manuf. Eng., Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia (Rahman, M.N.A.) \|\| Fac. of Info. & Comm. Tech., Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia (Jaya, A.S.M.) \|\| Fac. of Electronic & Comp. Eng. Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia (Napiah, Z.A.F.M.) \|\| Soft Computing Research Group (SCRG), Fac. of Computer Sciences & Information System, Universiti Teknologi Malaysia, 81310, UTM, Skudai, Johor, Malaysia (Hashim, S.Z.M.; Haron, H.) \|\| Centre of Graduate Studies, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia (Muhamad, M.R.)
Abstract	In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using hybrid RSM-fuzzy model is implemented. TiAlN coatings are usually used in high-speed machining due to its excellent surface hardness and wear resistance. The TiAlN coatings were produced using Physical Vapor Deposition (PVD) magnetron sputtering process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The fuzzy rules were constructed using actual experimental data. Meanwhile, the hardness values were generated using the RSM hardness model. Triangular shape of membership functions were used for inputs as well as output. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the coating hardness as an output of the process. The results of hybrid RSM-fuzzy model were compared against the experimental result and fuzzy single model based on the percentage error, mean square error (MSE), co-efficient determination $(R^{2})$ and model accuracy. The result indicated that the hybrid RSM-fuzzy model obtained the better result compared to the fuzzy single model. The hybrid model with seven triangular membership functions gave an excellent result with respective average percentage error, MSE, $R^{2}$ and model accuracy were 11.5%, 1.09, 0.989 and 88.49%. The good performance of the hybrid model showed that the RSM hardness model could be embedded in fuzzy rule-based model to assist in generating more fuzzy rules in order to obtain better prediction result.
Starting Page	313
Ending Page	318
File Size	792327
Page Count	6
File Format	PDF
ISBN	9781457716768
ISSN	21647151
e-ISBN	9781457716768
DOI	10.1109/ISDA.2011.6121674
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-22
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	hardness PVD magnetron sputtering Predictive models TiAlN coatings Response surface methodology Coatings Mathematical model Substrates Sputtering Surface treatment hybrid RSM-fuzzy modeling
Content Type	Text
Resource Type	Article

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