Descripción
Project Description Background Laser Ultrasonics (LUS) is a powerful non-contact technique for in-situ characterization of metallic materials at elevated temperatures. In hot strip mill environments, LUS enables real-time monitoring of grain size evolution through analysis of ultrasonic wave attenuation and velocity, providing critical insight into recrystallization, grain growth, and phase transformations. This information is highly valuable for advanced process control strategies aimed at improving product quality, consistency, and resource efficiency. The NAICE project (Non-destructive AI enhanced online grain size evaluation) addresses a key challenge in fossil free steel production: today’s steel mills lack real time information about the microstructure that governs material quality, leading to unnecessary energy use, scrap, and slow process adjustments. By upgrading the world’s only industrial laser ultrasonic grain size gauge and collecting a unique large dataset, the project will train models to enable AI enhanced measurement of the grain size and microstructure in real-time. This enables smarter process control, higher yield, and reduced emissions. This thesis is the first of two planned master thesis works. The first will evaluate the best options using historical data sets, whereas the second will focus on using the full dataset and optimizing the model for industrial implementation. If you want to know more about the LUS technology and the installation of the world’s only grain size gauge there is a recorded webinar here: https://www.youtube.com/watch?v=ZOpXUAPhEv0&t=665s Thesis Scope The thesis will focus on the application of machine learning and AI techniques to Laser Ultrasonic (LUS) data acquired in a hot strip mill environment. The available dataset consists of: Raw LUS A-scan signals with multiple backwall echoes (20 µs duration, 1 ns resolution) Grain size values calculated using an established conventional algorithm (serving as reference values)