Hybrid neural network for predicting coating characteristics in flame spraying

Antonov I.V., Bruttan I.V., Gorelov M.A., Iakovlev I.S.
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The paper presents a hybrid artificial neural network model based on an architecture that incorporates a convolutional image encoder (CNN) and an attention module (Attention-based Multiple Instance Learning, Attention MIL). This module aggregates informative features from a sequence of frames capturing the flame spraying process. Additional technological parameters—air pressure, propane pressure, and standoff distance — are integrated into the model via a tabular channel, enabling it to account for the relationship between visual data and numerical process regime characteristics. The software implementation was developed using the Streamlit platform and the PyTorch library. It features an interactive interface for model training and result visualization, analysis of attention weights across frames, and a prediction mode for output characteristics: surface roughness ($R_a$) and the mass of the deposited coating ($m$). Experimental studies were conducted on data from real-world technological processes, and a comparative analysis of the accuracy of various model configurations was performed. The results demonstrate that the hybrid neural network, which combines visual and tabular features, achieves higher prediction accuracy compared to models using only a single modality. Furthermore, when comparing different implementations of the hybrid network, it was established that using the attention mechanism to process the series of flame spray images provides a significant increase in accuracy over a simple averaging of features without attention. The application includes an attention visualization module that creates a montage of the most significant frames and displays their attention weights, allowing users to identify which frames had the greatest influence on the prediction. The model’s capability for export to the ONNX format for integration into process control systems is also demonstrated. The proposed approach showcases the effectiveness of fusing visual and tabular information for manufacturing process monitoring tasks. The model can serve as a foundation for developing a decision support system or an automated quality control system for coatings produced by flame spraying. The limitations of the implemented model and prospects for its further development are also considered.

Keywords: flame spraying, forecasting, hybrid neural network, Attention MIL, computer vision, Streamlit, ONNX, coating quality control
Citation in English: Antonov I.V., Bruttan I.V., Gorelov M.A., Iakovlev I.S. Hybrid neural network for predicting coating characteristics in flame spraying // Computer Research and Modeling, 2026, vol. 18, no. 1, pp. 101-116
Citation in English: Antonov I.V., Bruttan I.V., Gorelov M.A., Iakovlev I.S. Hybrid neural network for predicting coating characteristics in flame spraying // Computer Research and Modeling, 2026, vol. 18, no. 1, pp. 101-116
DOI: 10.20537/2076-7633-2026-18-1-101-116
URL: http://crm-en.ics.org.ru/journal/article/3690/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Antonov I.V., Bruttan I.V., Gorelov M.A., Iakovlev I.S.

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