Methods and Algorithms for Automation of Process Control in the Formation of Composite Coatings

A methodology for intelligent control of the detonation-gas spraying (DGS) process of reactive Ni/Al composite coatings is presented, based on a Decision Support System (DSS) integrated with a digital twin of the technological process. The proposed hierarchical control architecture includes PLC-based sequencing, real-time stabilization of jet parameters using diagnostic data, predictive quality regulation, and a multi-objective optimization loop ensuring adaptive adjustment of process modes. The coating responses were evaluated from SEM image analysis: average pore area, number of unmelted particles, specific length of interphase boundaries, and fraction of pre-formed intermetallic phases. The relationships between technological parameters (barrel filling degree, C2H2/O2 ratio, spray distance, powder feed rate, gas temperature, and pressure) and structural characteristics were described using the Response Surface Methodology (RSM) with second-order regression models. Analysis of variance (ANOVA) confirmed the statistical significance of the factors and the adequacy of the model. Simulation and experimental verification demonstrated that gas temperature and spray distance exert the strongest influence on the formation of interphase boundaries. Optimization using the Harrington desirability function combined with a genetic algorithm enabled minimization of porosity and unmelted particles, while maximizing specific length of interphase boundaries. The mean prediction error across all structural metrics did not exceed 5—8 %. The developed DSS provides adaptive control and automatic optimization of DGS parameters, significantly reducing experimental workload and improving the reproducibility of coating structures. The methodology is suitable for integration into intelligent control systems and digital twin platforms for thermal spraying processes. Future work will focus on applying machine-learning-driven hybrid models combining empirical and physicochemical simulations for enhanced prediction accuracy and autonomy.

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