Master Thesis: AI for Real-Time Microstructure Monitoring Using Laser Ultra

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)

The primary objective is to develop and evaluate AI-based approaches for:

- Grain size prediction directly from raw laser ultrasonic signals

- Microstructure state classification (e.g., recrystallized fraction, pancaking/aspect ratio)

A key part of the work will be to survey suitable AI architectures, such as time-series models, deep learning approaches, and alternative signal-processing-informed models, and assess their applicability to the LUS data.

Tasks

Survey and critically assess relevant AI/ML architectures for ultrasonic time-series data

Pre-process and structure high-resolution LUS A-scan datasets for model training

Develop and implement selected AI models

Perform benchmark comparisons between different approaches with respect to:

Prediction accuracy

Robustness to signal variability

Computational efficiency and suitability for real-time deployment

Analyze model interpretability in relation to known physical phenomena

Expected Outcomes

The thesis should deliver:

A structured comparison of candidate AI approaches for LUS-based grain size prediction

Prototype models demonstrating feasibility on the historical dataset

Recommendations for model selection in future industrial implementation

Desired Qualifications

We are looking for a motivated student with:

A background in computer engineering, engineering physics, materials science, applied mathematics, or similar

Strong interest in machine learning and signal processing

Experience with Python/Matlab and ML frameworks

Basic understanding of wave physics, ultrasonics, or materials science (meriting but not required)

Analytical mindset and ability to work with complex datasets

Additional Information

The work will be carried out in close collaboration with experts in Laser Ultrasonics and industrial process monitoring, providing a unique opportunity to contribute to cutting-edge development of AI-driven process control in steel manufacturing.

Project time

The project is intended for a master thesis (30hp), starting in spring 2027 or can be mutually decided through negotiations.

Further information

This project is intended to be performed at Swerim in Stockholm. Swerim rewards the student with 50 000 SEK for an approved master thesis (30hp).

Contacts

For further information please contact:

Mikael Malmström, mikael.malmstrom@swerim.se

Hampus Wikmark Kreuger, hampus.wikmark@swerim.se

Application

Apply by using the application function below. The application can be written in Swedish (or English). You will receive a confirmation that Swerim has received your application. Please note that we fill the position as soon as we find a suitable applicant, which means there is no explicit deadline.