Smart material data enrichment using AI, experimental data, and material science

This webinar aims at demonstrating our approach that enables the enrichment of an initial material database by combining advanced material modelling and AI

On the one hand, advanced material modelling, through multiscale material modelling, and embedded material science laws, bring accuracy and domain knowledge. On the other hand, AI brings efficiency, portability and quantification of the accuracy of the predictions. The database of filled and unfilled plastics is enriched for different temperatures, filling amounts, strain rates and loading angles. The targeted performances are stress strain responses until failure.

Moncef Salmi – Business Enablement Lead for AI at Hexagon

Moncef Salmi is a PhD, engineer and MBA in data science applied to material science. He is graduated jointly from ENSAIT and ENSAM ParisTech in France. He earned his PhD degree at the Blaise Pascal University in Clermont-Ferrand in France in 2012. He earned his MBA at the University of Northampton in England in 2019. He joined Hexagon upon the completion of his PhD in 2012. As Business Enablement Lead in charge of AI for materials, Moncef has expertise in the full stack designing, development, deployment and commercialisation of highly applied AI based solutions for material and process modelling.