With case study presentation from Welding Alloys Group
Developing new materials with improved cost and performance is already a complex, multi-parameter optimisation problem. The increasing need to meet sustainability and net zero targets makes this task yet more challenging. Machine learning (ML) can help, extracting added value from all available data to propose new designs and processing options, and to guide experimental programmes. In this webinar, we discuss the use of ML for a range of sustainable materials design and development challenges, relevant to metals, polymers, composites, and ceramics. And we hear a feature case study presentation from Welding Alloys Group. The Intellegens team gives a demonstration of the Alchemite™ machine learning software for a materials application and there is a live Q&A.
Original broadcast: Tuesday, 5 Dec, 2023
Welding Alloys Group case study details – minimising the impact of degradation mechanisms including corrosion and multiple forms of wear is critical in a wide range of industry sectors to reduce component failures. For example, a mill roll could wear down due to abrasion and impact. A common solution is protection through wear-resistant coatings, usually in the form of heavily alloyed compositions, or metal matrix composites. Mario Cordero will explain how ML can be a powerful tool for study of these materials. ML is combined with fundamental scientific principles, know-how, and historical data to accelerate the optimisation of existing products and processes, or create new ones. Special consideration is given to sustainability and reduction of heavy metals used in the industry.
The speakers
Mario Cordero is Welding Alloys Group Innovation Director, with more than thirty years of experience in materials science in areas as diverse as primary metrology, electroceramics (including photocatalysis, piezoelectrics and superconductors), welding engineering and metallurgy. He holds multiple qualifications BEng(Hons), MPhil, PhD, CEng, IWE/EWE and Fellow of The Welding Institute from world class institutions such as NIST (USA), Cambridge University (UK), TWI (UK), etc. He has made important contributions in multiple sectors such as automotive, oil and gas, shipbuilding, energy, etc. He is a passionate professional currently leading a multidisciplinary team that maintains continuous innovation at Welding Alloys Group.
Tom Whitehead is Head of Machine Learning at Intellegens, managing the Alchemite™ Success Team of scientists that works closely with research teams at client organisations to apply machine learning methods to their data and R&D challenges. Tom has a PhD in theoretical physics from the University of Cambridge, and his work also involves developing a series of application-specific machine learning modules to address high-value data analysis bottlenecks.
