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CuttingEdge

CuttingEdge4.0 – A research project facing edge crackin in AHSS

The aim of this project is to develop tools and methods to predict and prevent edge cracking of sheet metal parts during manufacturing. Luleå University of Technology is one of several international participants that forms the CuttingEdge4.0 consortium.

The majority of the automotive components manufactured today consists of Advanced High Strength Steel (AHSS). AHSS is a group of multi-phase steel that trough precise metallurgical process acquire tailor made phase compositions thus can be optimised towards high tensile strength and ductility. High strength and ductility enables weight reduction of crash components as the gauge thickness of the sheet metal can be reduced while retaining the energy absorbing properties of the structure. AHSS are also affordable options to lightweight materials such as carbon reinforced fibre composites and aluminium.

Limited formability

However, AHSS have limited formability and crack resistance compared to conventional steel. These limitations are related to the shear cutting process that proceeds forming and can trigger sudden crack formation from stretched edges. The edge-cracking phenomena can cause costly halts in production, but can also affect the crashworthiness of crash components in AHSS if it occurs during a crash event.

Novel tools and methods

To increase the knowledge in edge-cracking of AHSS the research project CuttingEdge4.0 was initiated. The aim of the project is to develop tools and methods that are able to predict, thus prevent, edge-cracking of stretched edges during manufacturing of AHSS sheet metal parts. Furthermore, this knowledge will be transferred to the sheet manufacturing industry to enhance the use of AHSS as lightweight materials. Within the project are experimental and numerical methods developed for edge-cracking assessment related to the damage induced by the manufacturing process of various AHSS grades.  These methods are incorporated in an Industry4.0 framework where machine-learning systems optimises the manufacturing process parameters trough in-line measurements and digital twins in order to reduce defects. Luleå University of Technology leads the work package which purpose is to develop digital twins of the shear cutting process that precedes the forming operations.

Olle Sandin, PhD Student

Phone: +46 (0)920 492502
Organisation: Solid Mechanics, Solid Mechanics, Department of Engineering Sciences and Mathematics
Pär Jonsén

Pär Jonsén, Professor and Head of Subject, Head of Division

Phone: +46 (0)920 493460
Organisation: Solid Mechanics, Solid Mechanics, Department of Engineering Sciences and Mathematics
Daniel Casellas

Daniel Casellas, Adjunct Professor

Organisation: Solid Mechanics, Solid Mechanics, Department of Engineering Sciences and Mathematics