Genetically optimized diamond-like carbon thin film coatings

Diamond-like carbon (DLC) thin films find extensive applications in manufacturing, aerospace, and medical sector and thus, a huge need exists to optimize its deposition process. However, the experimental complexity and high costs involved limits the conduction of exhaustive experiments to determine the best process combinations. Consequently, in this research, a predictive modeling approach is coupled with global optimization procedures to find the optimum combination of deposition parameters. The prediction model is based on response surface methodology modeling of 20 LPCVD deposition experiments carried out as per central composite design. Scanning electron microscopy is used for morphology characterization of the DLCs and nanohardness is measured by nanoindentation technique. Raman spectroscopy and X-ray diffraction is used for characterizing the structural properties. Using a genetic algorithm, single- and multiobjective Pareto optimal solutions are reported which are validated experimentally. It is seen that for single-objective optimization, the best-predicted solutions for maximized hardness and maximized Young’s modulus are only 2% and 12% away from their true experimental values. Similarly, the most-desirable solution selected by TOPSIS from a Pareto optimal solution of 96 nondominated solutions (simultaneously maximized Young’s modulus and hardness) is only 2% away from their respective true values.