Hybrid RSM-fuzzy modeling for hardness prediction of TiAlN coatings
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...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/4322/1/Hybrid_RSM-fuzzy_modeling_for_hardness_prediction_of_TiAlN_coatings.pdf http://eprints.utem.edu.my/id/eprint/4322/ |
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| Summary: | 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 (R2) 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, R2 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. |
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