AI-based Prediction of Epoxy Acrylate UV Curing Kinetics and Their Mechanical Properties

This study develops an artificial intelligence (AI)-based model for predicting the curing kinetics and mechanical properties of UV-curable epoxy acrylate coatings. Traditional experimental approaches rely on repetitive trials, making optimization challenging. By leveraging AI, a machine learning model was trained using a dataset of ۹۰ experimental samples, incorporating photoinitiator concentration (۰.۵-۵ wt.%), UV intensity (۲-۲۰ W/cm²), and curing time (۰-۵۰۰ s). The AI model demonstrated high accuracy (R۲ = ۰.۹۷, mean error = ۳%) in predicting the curing process. Results showed that increasing photoinitiator concentration reduced curing time, while higher UV intensity accelerated polymerization. The predictive capability of AI enables optimization of coating formulations, reducing experimental costs and time. This approach provides a precise tool for predicting UV curing kinetics and mechanical performance, leading to enhanced efficiency in industrial applications such as automotive and electronics coatings. Future work will integrate additional material parameters to further improve predictive accuracy.

کلیدواژه ها:

Epoxy Acrylate ، UV Curing ، Machine Learning ، Mechanical Properties ، AI Prediction