NITROGEN-STABILIZED DLC COATINGS: OPTIMIZATION OF PROPERTIES AND DEPOSITION PARAMETERS USING RANDOMIZED TREE AND NEURAL NETWORK ALGORITHMS

Abstract

This work discusses the predictable control of coating deposition by vacuum ion plasma technology. The multiple technological parameters and the instability of the nonequilibrium ion plasma system create substantial obstacles to the wide industrial application of promising multicomponent functional coatings. Here we propose a solution to this problem, which includes: creation of a database of diamond-like carbon coatings (DLC) in order to identify a limited number of adjustable control parameters of the technology, determination of how these parameters affect the coating properties, analysis of the revealed effects using statistical methods and neural network algorithms, and use of the results for the predictable tuning of specified coating properties. The object of research is original DLC coatings whose structure is stabilized with nitrogen instead of conventionally used hydrogen. The experimental database of DLC coatings is created based on our previous studies and includes structural, morphological and architectural characteristics of coatings, various types of substrates and sublayers, physical, mechanical and tribological properties, and various combinations of coating deposition parameters. A specific problem is solved to determine the influence of deposition parameters such as chamber pressure P, stabilizer content (% nitrogen), ion flow rate (coil current λ) and deposition time t on hardness H and elastic modulus E of coatings. Based on the results obtained, the deposition parameters are optimized so as to obtain predictable strength values of the formed carbon coating. The optimization procedure is developed using both classical statistical methods and modern algorithms of ridge regression, randomized trees (ExtraTrees), and a fully connected neural network (multilayer perceptron MLP).