| 09:00 | Welcome coffee | |
| 09:20 | Keynote Speaker
Gian-Marco Rignanese
UC Louvain
From high-throughput ab initio calculations to machine learning: the new era of materials informatics | |
| 10:00 | Keynote Speaker
Ge Lei
Imperial College London
From trust to action: large language models for scientific discovery and decision support | |
| 10:40 | Coffee break | |
| 11:10 | Axel Pantzare
Viking Analytics
Automatic fault detection in metal manufacturing via wireless vibration monitoring using behaviour-based analytics | Quality Assurance & Safety |
| 11:30 | Daniel Bartz
Aurubis AG
From pixels to PLC: architecting safe computer vision at the industrial edge | Quality Assurance & Safety |
| 11:50 | Jan Fransens
Apixa NV
Learning normality: anomaly detection as a scalable strategy for metallurgical surface inspection | Quality Assurance & Safety |
| 12:10 | Olivier Herbiet
CRM Group
Zinc coating monitoring at hot dip galvanising | Quality Assurance & Safety |
| 12:30 | Lunch | |
| 13:30 | Partnership Pitch: SWECO | |
| 13:45 | Robby Neven
Aperam
Preventing melt failures and enhancing ferro-alloy yield via automated raw material monitoring | Supply Chain Optimization |
| 14:05 | David Waroquiers
Matgenix
Accelerating alloy and coating development using AI: A case study combining active learning and atomistic simulations | Alloy Development & Material Innovation |
| 14:25 | Pengru Zhao
Université de Lorraine, CNRS, Université de Montpellier
Deep learning-based grain boundary segmentation in backscattered electron images | Alloy Development & Material Innovation |
| 14:45 | Antoine Hilhorst
UCLouvain, WEL Research Institute
From data to discovery of TWIP alloys by linking stacking energies, composition, and mechanical properties | Alloy Development & Material Innovation |
| 15:05 | Jasper Somers
Aluminium Duffel BV
Integrating AI image analysis into filiform corrosion assessment of aluminium body sheet | Alloy Development & Material Innovation |
| 15:25 | Coffee break | |
| 15:55 | Partnership Pitch: Bekaert | |
| 16:00 | Pascal Jacques
UCLouvain
Machine learning approach for the development of new β-metastable Ti alloys best-suited for additive manufacturing | Alloy Development & Material Innovation |
| 16:20 | Michael Sluydts
ePotentia, UGent
Predicting real properties with fake data: how GenAI can help complete materials datasets | Alloy Development & Material Innovation |
| 16:40 | Alexis Miroux
Aluminium Duffel BV
Predicting the properties of aluminium rolled products from full‑scale production data | Alloy Development & Material Innovation |
| 17:00 | Nicolas Dubois
Nyrstar
Harnessing data science for zinc industry cellhouse – building a model that quantifies the impact of influencing factors on the current efficiency | Supply Chain Optimization |
| 18:30 | Conference dinner | |
