Application of artificial intelligence technologies for analysis of polymer composite materials in production conditions

This article addresses the development and research of a modern information system aimed at effective recognition and comprehensive analysis of composite material particles. The proposed solution is based on advanced artificial intelligence (AI) technologies, including the application of deep convolutional neural networks (CNNs) for high-accuracy image classification of particles. System integration is achieved through Internet of Things (IoT) technologies that ensure interaction with modern measurement equipment used in industrial processes. A key component of the developed system is an expert evaluation module based on fuzzy logic inference mechanisms. This component is designed to enhance analysis accuracy in situations characterized by uncertainty or incomplete initial data. A created knowledge base containing production rules and specialized membership functions, also plays a crucial role. It allows for adequate processing of material property descriptions using linguistic variables. The implementation of the proposed approach has been carried out on the Python platform, widely used in software development due to its rich capabilities provided by libraries for machine learning and web programming. The user interface is presented as a convenient web portal, allowing users to upload images of samples under investigation, configure analysis process parameters and obtain final results in a user-friendly format, including graphs, tables and intuitive visualizations. The practical application of this information system significantly reduces time spent on analyzing composite materials, improves microstructural feature recognition quality and increases overall productivity typical of Industry 4.0 processes. It particularly contributes to the development of additive manufacturing technologies by enabling substantial improvement in product quality control, cost reduction and increased efficiency of production operations. Therefore, this development becomes an essential element of intelligent infrastructure for modern industrial enterprises, contributing to improved economic performance and product competitiveness. The study demonstrates the prospects of approaches combining AI methods and new information technologies, opening new horizons for automation and optimization of technological processes in industry.