Titanium nitride coatings are extensively adopted as an intermediate adhesion layer in the cutting tools because of its superior mechanical properties. The interdependence of each process parameter during the deposition of such a coating process is nonlinear, and hence, it becomes a challenge to determine the output responses without carrying out a wide range of experiments. So to minimize the experiments, Taguchi-based L9 design of experiments were employed in this study with three factors and three levels such as Argon (Ar): Nitrogen (N2) gas mixture, Pulsed direct current power, and deposition time for depositing titanium nitride thin films on silicon (100) and tungsten carbide substrates using Pulsed direct current magnetron sputtering technique, where conventional direct current magnetron sputtering cannot be deployed using titanium nitride target. Multiple output responses such as average thickness, surface roughness, nano-hardness, Young’s modulus, wear track deformation, and coefficient of friction were measured by carrying out the systematic investigations, and a single optimum solution was obtained using Grey relational analysis. From the Grey relational analysis, the optimum Ar:N2 gas flow mixture, Pulsed direct current power, and deposition time for improved titanium nitride adhesion layer are 300 W, 10:5 sccm, and 5 min, respectively. Further, grazing incidence x-ray diffractometer profiles of deposited films exhibits (111) and (200) reflections corresponding to the titanium nitride phase, and the morphological analysis also revealed the existence of strongly faceted nano-grains with a triangular-shaped morphology.
Manufacturing requires reliable models and methods for the prediction of output performance of machining processes as the demand for highly automated machine tools in the industry has been increased. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. In the present work, dry turning of austenitic Stainless Steel (SS303) using CVD multi-layer (TiN/Al2O3/TiCN) coated carbide insert has been investigated. Cutting speed, feed rate, and depth of cut have been considered as the input process variables for the dry turning process. Taguchi's L9 orthogonal array has been utilized for designing the experiments. A Grey-Fuzzy logic approach has been employed to investigate the multi-objective optimization of turning process parametric combination to provide minimum Surface Roughness (SR) with the maximum Material Removal Rate (MRR). Analysis of Variance (ANOVA) technique has been employed to identify the most influencing input process variable for achieving minimum SR and maximum MRR. Morphology analysis has been performed on the machined surfaces and the chips generated during the machining processes using a Scanning Electron Microscope (SEM) in order to relate the surface quality with the input factor setting.
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