| Abstrakt: |
In hard coating materials, Titanium Nitride (TiN) is widely used as a surface coating material due to its excellent properties. In machining processes, it is crucial to optimize the coating process for better parameter selection. This optimization could enhance the performance of cutting tools. In this paper, TiN is coated on tungsten carbide tools using the Physical Vapor Deposition (PVD) method. Coating surface thickness is investigated as an output function that primarily depends on the Nitrogen gas pressure, Argon gas pressure, and turntable speed. The Particle Swarm Optimization algorithm (PSO) is utilized as an efficient metaheuristic technique for optimization purposes. PSO is integrated with Response Surface Methodology (RSM), which functions as a modeling method to generate the objective function for coating thickness. RSM also analyzes the effect of input parameters on the produced film thickness. Finally, Prediction Interval (PI) and Residual Error (e) are used to validate the RSM model. The results show that the actual value of film thickness falls within 95% accuracy and exhibits very low error. While PSO is found to be a powerful technique for optimizing coating thickness, it has reduced the ratio of the average experimental value by 75%. [ABSTRACT FROM AUTHOR] |
