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), coefficient
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
