novtexmechМехатроника, автоматизация, управлениеMekhatronika, Avtomatizatsiya, Upravlenie1684-64272619-1253Commercial Publisher «New Technologies»10.17587/mau.27.429-438novtexmech-2072Research ArticleАВТОМАТИЗАЦИЯ И УПРАВЛЕНИЕ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ И ПРОИЗВОДСТВАМИAUTOMATION AND CONTROL TECHNOLOGICAL PROCESSESМетоды и алгоритмы автоматизации управления технологическим процессом формирования композиционных покрытийMethods and Algorithms for Automation of Process Control in the Formation of Composite CoatingsГанигинС. Ю.GaniginS. Yu.

д-р техн. наук, доц., зав. каф. 

Самара

Samara, 443100 

ganigin.s.yu@yandex.ruКиященкоВ. В.KiyashchenkoV. V.

мл. науч. сотр. 

Самара

Kiyashchenko V. V., Junior Researcher, 

Samara, 443100 

vv.kiyashchenko@gmail.comКирсановА. С.KirsanovA. S.

канд. техн. наук, зав. каф. 

Москва

Moscow, 107028 

kas792@yandex.ruМироновА. В.MironovA. V.

аспирант 

Москва

Moscow, 107028 

artur.mironov3@gmail.comСамарский государственный технический университетРоссияSamara State Technical UniversityRussian FederationМосковский ПолитехРоссияMoscow PolytechRussian Federation202619082026278429438Copyright © Commercial Publisher «New Technologies», 20262026Commercial Publisher «New Technologies»Commercial Publisher «New Technologies»https://mech.novtex.ru/jour/about/submissions#copyrightNoticehttps://mech.novtex.ru/jour/article/view/2072

Представлена методика автоматизации управления процессом детонационно-газового напыления (ДГН) реакционноспособных композиционных покрытий системы Ni/Al на основе системы поддержки принятия решений (СППР) и цифрового двойника технологического процесса. Разработанная архитектура включает уровни низкоуровневого контроля с программируемым логическим контроллером, стабилизации параметров струи по данным диагностического комплекса, предиктивного регулирования качества и оптимизационного контура, реализующего многокритериальную адаптацию режимов напыления.В качестве выходных переменных модели использовались структурные метрики, полученные методом морфометрического анализа изображений электронной микроскопии: средняя площадь пор, число нерасплавленных частиц, удельная длина межфазных границ и доля предварительно образованных интерметаллидных фаз. Для описания взаимосвязей между технологическими параметрами (степень наполнения, соотношение газов, дистанция напыления, расход порошка, температура и давление газа) и структурными характеристиками применена методология поверхности отклика (RSM) с квадратичными регрессионными моделями. Дисперсионный анализ (ANOVA) подтвердил статистическую значимость факторов и адекватность моделей.Результаты численного моделирования и верификации показали, что температура газового потока и дистанция напыления оказывают наибольшее влияние на формирование межфазных границ. Оптимизация параметров методом желательности Харрингтона и с использованием генетического алгоритма позволила минимизировать пористость и число нерасплавленных частиц, одновременно максимизируя длину границ. Средняя ошибка прогноза по всем метрикам не превышает 5...8 %.Разработанная СППР обеспечивает адаптивный выбор и корректировку режимов ДГН, сокращает объем экспериментальных испытаний и обеспечивает воспроизводимость структуры покрытия. Методика может быть интегрирована в интеллектуальные системы автоматического управления и платформы цифровых двойников технологических процессов, а дальнейшее развитие связано с применением машинного обучения и гибридных физико-статистических моделей.

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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The authors declare that there are no conflicts of interest present.