STELLAR
laser surface texturing technology
The STELLAR project (Surface Texturing with Laser for Large Areas with Riblets) develops a fast, clean and environmentally friendly laser surface texturing technology for the mass production of “riblet”-type profiles (inspired by the scales of shark skin). Its objective is to drastically improve energy efficiency in naval and industrial pipeline applications by reducing friction. By combining high-power ultrafast femtosecond lasers with high-speed polygon scanners, the process can be scaled to large areas. In addition, data collected from the entire production process will be analysed using Artificial Intelligence (AI) and Machine Learning (ML) algorithms to predict, optimise and control manufacturing quality, thereby contributing to the European Green Deal and the United Nations Sustainable Development Goals (SDGs 7 and 9).
The STELLAR project (Surface Texturing with Laser for Large Areas with Riblets) develops a fast, clean and environmentally friendly laser surface texturing technology for the mass production of “riblet”-type profiles (inspired by the scales of shark skin). Its objective is to drastically improve energy efficiency in naval and industrial pipeline applications by reducing friction. By combining high-power ultrafast femtosecond lasers with high-speed polygon scanners, the process can be scaled to large areas. In addition, data collected from the entire production process will be analysed using Artificial Intelligence (AI) and Machine Learning (ML) algorithms to predict, optimise and control manufacturing quality, thereby contributing to the European Green Deal and the United Nations Sustainable Development Goals (SDGs 7 and 9).
Challenges
Demonstrate that riblet-shaped surfaces inspired by shark skin can significantly improve energy efficiency in cross-flow and rotating flow applications (such as pipelines and marine propellers) by reducing geometric friction.
Scale up the speed and performance of laser texturing to cover large industrial areas, combining for the first time high-power femtosecond lasers in burst mode with polygon scanning systems and spatial beam shaping.
Implement Machine Learning and Artificial Intelligence methods to process the Big Data generated during production, optimising laser process parameters and riblet performance.
Solution
Development of microstructured surfaces through two approaches: (i) direct laser engraving on components y (ii) laser engraving of inverse profiles on moulds, enabling large-scale plastic replication through injection moulding or hot pressing.
These moulds also incorporate nanoscale texturing to prevent defects during demoulding.
Functional performance is monitored using an AI-optimised database, providing a high-precision industrial solution for heavy machinery.
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Participating entities
Funding body
M-ERA.NET (Convocatoria 2022) (2022 Call) through regional/national agencies: SMWK (Germany), SEKUENS (Asturias, Spain) y RNAQ (France).
Collaborators
Hochschule Mittweida (Germany, coordinator)
AMPLITUDE (France)
Subcontractors
Bionic Surface Technologies (Austria)
University of Oviedo (Spain)