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TiN/AlTiN multilayer coatings were deposited on tungsten carbide cutting tool by applying a direct current on a pulsed bias arc ion plating system. The effect of pulsed bias layer thickness on sample properties was investigated. The amount of grain size decreased with increasing layer thickness. The crystal structure of the coatings was determined using a D8 Advance Bruker X-ray diffractometer with CuKa (λ = 1.5405 Å) radiation. TiN/AlTiN multilayer coatings were crystallized with orientations in the (111), (200), (222), and (311) crystallographic planes, and the microstructure was enhanced with preferred orientation in the (111) plane. Compared with the substrate, all the specimens coated with TiN/AlTiN multilayer coatings exhibited better X-ray diffraction properties.

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The effect of substrate cleaning using ultrasonic cleaner on tungsten carbide was investigated. The surface energy of the substrate was measured using two liquids with dominant polar and dominant dispersion components which were distilled water (DI) and methylene iodide. Owens-Wendt method was carried out to calculate the surface energy of the substrate. The result showed that the cleaning process using solvent B (alkaline, DI, acid, DI, DI, alcohol) for 20 minutes without the wiping process led to the highest surface energy of 126.3399 dyne/cm with the polar component of 80.538 dyne/cm. Findings from this research suggested that type of solvent, cleaning time, and interactions among solvent type, cleaning time, and wiping process significantly influenced surface energy of the substrate.

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.