Novel material characterisation and data driven analytics to predict and avoid edge-cracking in safety related automotive parts.
Advanced High Strength Steels (AHSS) have become the dominant material choice for lightweight construction in the automotive and transport sector for its good in-service performance, manufacturability and recyclability. However, in these kind of materials some limitations in cracking resistance and formability appear, which may trigger edge-cracking problems that compromise part quality.
CuttingEdge4.0 project develops experimental tools and digital twins for the cutting process and incorporate Industry 4.0 data driven analytics based on Artificial Intelligence and machine learning solutions. The final aim is to transfer to the automotive industry tools and methodologies to predict edge-cracking in the early part design stages, detect edge-cracking defects and assure part quality during forming, boosting the applicability of AHSS-based automotive lightweight parts.
The solutions proposed by CuttingEdge4.0 will increase safety in automotive industry through the development of high performance parts and will influence positively in the employability and sustainability of the automotive industry and steel sector.
The consortium of the project is formed by Eurecat, Luleå University of Technology, Voestalpine, Faurecia, Centro Ricerche Fiat, TATA STEEL and PWO.