HSLAi: When HSLA meets AI

Our expectations for this project are really high. This project can be a huge leap forward in the way metallurgical research is conducted – and so, it should allow us to unravel advanced correlations in the P-S-P triangle to a much higher degree than ever before.

Arunim Ray & Jan Rens & John Vande Voorde

Strength Model

Within the context of the digitalisation of our metallurgical research, a project proposal for important regional funding was submitted to VLAIO together with ArcelorMittal Belgium. This project targets the investigation of novel Process-microStructure-Property correlations for future high-strength low-alloy (HSLA) metallurgies by combining our unique bulk combinatorial experimentation with the use of Artificial Intelligence (AI) and Machine Learning (ML). After receiving highly positive comments from the expert reviewers, the 3-year project has been approved by the VLAIO evaluation committee.

Models linking processing parameters and material properties exist, and even in a rather advanced state. However, taking additional information from microstructure images should allow us to uncover hitherto unknown effects and correlations. To this end, combinatorial experimentation and high-throughput sample processing will be married to the latest machine learning and artificial intelligence algorithms. This way, an extensive and novel metallurgical dataset – several thousand HSLA materials and tens of thousands of microstructural images – will be generated to construct a very-high-dimensional parameter space. Modern image processing techniques, utilising machine learning and deep learning, are being developed to extract as much information as possible from the microstructure images and the time series characterising the processing and the material properties. This will reveal additional data compared to what is realised today via manual and semi-automatic analysis and human interpretation. This multitude of features will then be fed into different machine-learning and deeplearning algorithms, such as artificial neural networks, to hone more advanced models linking processing, microstructure and properties. The developments in this project can be used to develop the next generation of HSLA steel grades, and they can also be used to assist the plants in their quality control. This project very much represents a leap into the future, in terms of both knowledge-building and streamlining experimentation.