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• W2893554076 abstract "Optimization of coating parameters is important in the machining process, as an optimized coating layer enhances efficient machining result by providing high wear resistance, saving time and effort, reducing cost, increasing productivity and leading to extend tool life for better surface finish. In terms of tool wear resistance, tools with suitable film coating are forty times better than uncoated tools. Cutting tools are usually coated by Titanium Nitride (TiN), since it has properties such as resistance to wear and hardness resulting in increased cutting performance. Genetic algorithms (GAs) belong to the powerful artificial intelligence optimization family of evolutionary algorithm-based metaheuristic techniques. Since their inception, they have been successfully applied in numerous areas of industrial sectors as material design, tools coating and cutting, alloy design, and so on. In this research work, predicting thin film coating thickness of titanium nitride (TiN) on tungsten carbide (WC) inserts is presented. First, (WC) inserts were coated by TiN in different conditions using Physical Vapor Deposition (PVD), and the surface profilometer was used to measure thickness of the WC coated specimens. Second, Response Surface Methodology (RSM) was used to generate the objective function which represents the process parameters and coating thickness. Third, using a fitness function model, GAs were established to optimize the thickness value of TiN layer with respect to changes in three process parameters, namely Nitrogen gas pressure (N2), Argon gas pressure (Ar), and Turntable Speed (TT). Finally, for performance measurement, three real experimental tests were carried out in terms of Prediction Interval (PI) and Residual Error (e) to validate the RSM model. The actual coating thickness of validation data fell within the 95% (PI) and the (e) values percentage were very low. In terms of optimization, GAs is capable optimizing thickness better than the average experimental value, with a reduction ratio of 73.4%." @default.
• W2893554076 created "2018-10-05" @default.
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• W2893554076 date "2018-01-01" @default.
• W2893554076 modified "2023-10-16" @default.
• W2893554076 title "Minimizing thin film thickness in TiN coatings using genetic algorithms" @default.
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• W2893554076 doi "https://doi.org/10.1063/1.5055463" @default.
• W2893554076 hasPublicationYear "2018" @default.
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