Nitrogen-Stabilized DLC Coatings: Optimization of Properties and Deposition Parameters Using Randomized Tree and Neural Network Algorithms
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Abstract
Abstract This work discusses the predictable control of plasma-assisted physical vapor deposition (PVD) of coatings. The multiple process 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 (DLC) coatings to identify a limited set of adjustable process control parameters, 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, 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 flux 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).
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Research context
OpenAlex topics: Diamond and Carbon-based Materials Research · Metal and Thin Film Mechanics · Semiconductor materials and devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
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