This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement nº 825103. The CUSTODIAN project is an initiative of the Photonics Public Private Partnership.
This European project is dedicated to the field of laser technology application, that seeks to develop a laser device which is adaptable to each application to achieve defectless processing. The project aims to develop a methodology of application-driven laser beam tailoring of the material microstructure and deploy this beam to solve hotcracking in Laser Powder Bed Fusion (LPBF) and Laser Beam Welding (LBW) and to increase efficiency in Laser Cutting (LC).
The methodology includes determining the optimal thermal cycle for each material/application, developing a photonic system that emits a reconfigurable laser beam that enables reproduction of the optimal thermal cycle and the design of a real-time monitoring and control system. This will enable any laser process to be designed exactly in accordance with the thermal transformation required by the material, which will benefit productivity, quality and cost reduction.
The project contemplates two specific industrial applications: Laser welding for the automotive sector, tested by the company Marelli, and additive laser manufacturing for the aeronautical sector, tested by GFM. However, the methodology will be extendable to any application or sector that uses laser technology for processing all types of mechanical material. The CUSTODIAN consortium, led by AIMEN, is completed by the following entities: AIDIMME, NIT, secpho, Politecnico di Milano, Marelli, GFM, CAILabs, TU Wien and Precitec.
More information about the project can be found here: https://shapeyourlaser.eu/
The CUSTODIAN project policy will be to produce data freely available without embargo period whenever possible. In addition, partners will be committed to generate and deliver valuable and high-quality datasets. The whole Data Management Plan (DMP) and its procedures are aligned with data quality assurance.