Abstract
"The current paper explores a novel approach for determining temperature variations by integrating the modal parameters and AI techniques. The research focuses on the development of a comprehensive dataset for training an AI model encompassing an analytical method that considers thermal conditions and natural frequencies. Traditional methods of temperature measurement, like infrared and platinum resistance thermometers, often face limitations in terms of accuracy, especially in complex or dynamic environments having an uncertainty of ±3.6 °C [1], respectively ±0.2 °C [2]. In this study, we propose a methodology that harnesses the inherent relationship between axial loads caused by temperature variations and the change in natural frequencies of a double clamped steel beam. The measured natural frequency data is collected and fed into the AI model, specifically, for a robust temperature estimation, obtaining a maximum predicted temperature deviation of 0.386 °C. Keywords: temperature, natural frequency, artificial intelligence, finite element method, thermal condition"
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