Papers by Author: Habibollah Haron

Abstract: In this paper, modeling of Titanium Nitrite (TiN) coating thickness using Response Surface Method (RSM) is implemented. Insert cutting tools were coated with TiN using Physical Vapor Deposition (PVD) sputtering process. N₂ pressure, Argon pressure and turntable speed were selected as process variables while the coating thickness as output response. The coating thickness as an important coating characteristic was measured using surface profilometer equipment. Analysis of variance (ANOVA) was used to determine the significant factors influencing TiN coating thickness. Then, a polynomial linear model represented the process variables and coating thickness was developed. The result indicated that the actual validation data fell within the 90% prediction interval (PI) and the percentage of the residual errors were low. Findings from this study suggested that Argon pressure, N₂ pressure and turntable speed influenced the TiN coating thickness.

Abstract: In the experiment, 24 samples of data has been tested in real machining by using uncoated, TiAlN coated, and SN_TR coated cutting tools of titanium alloy (Ti-6Al-4v). The fuzzy rule-based model is developed using MATLAB fuzzy logic toolbox. Rule-based reasoning and fuzzy logic are used to develop a model to predict the surface roughness value of end milling process. The process parameters considered in this study are cutting speed, feed rate, and radial rake angle, each has five linguistic values. Nine linguistic values and twenty four IF-THEN rules are created for model development. Predicted result of the uncoated, TiAlN coated, and SN_TR coated has been compared to the experimental results, and it gave a good agreement with the correlation 0.9842, 0.9378 and 0.9845, respectively. The differences of the uncoated, TiAlN coated, and SN_TR coated between experimental results and predicted results have been proven with estimation error value 0.00025, 0.0015 and 0.0008, respectively. It was found that by applying SN_TR coated cutting tools with the recommended combination of linguistic values might gave best surface roughness values.

Abstract: In this paper, an approach in modeling surface roughness of Titanium Nitrite (TiN) coating using Response Surface Method (RSM) is implemented. The TiN coatings were formed using Physical Vapor Deposition (PVD) sputtering process. N₂ pressure, Argon pressure and turntable speed were selected as process variables. Coating surface roughness as an important coating characteristic was characterized using Atomic Force Microscopy (AFM) equipment. Analysis of variance (ANOVA) is used to determine the significant factors influencing resultant TiN coating roughness. Based on that, a quadratic polynomial model equation represented the process variables and coating roughness was developed. The result indicated that the actual coating roughness of validation runs data fell within the 90% prediction interval (PI) and the residual errors were very low. The findings from this study suggested that Argon pressure, quadratic term of N₂ pressure, quadratic term of turntable speed, interaction between N₂ pressure and turntable speed, and interaction between Argon pressure and turntable speed influenced the TiN coating surface roughness.