CC BY
24LMI-I-9
High-Speed surface functionalization using interference-based laser processes - From prediction to technological applications
T. Kunze1, T. Steege1, S. Alamri1, B. Krupop1, A. Madelung1, A. Aguilar-Morales1, F. Schell1,
F. Hundertmark1, V. Lang1,2, A.F. Lasagni1,2
Fraunhofer IWS Dresden, Microtechnology, Dresden, Germany
2Technische Universität Dresden, Institute for Manufacturing Technology, Dresden, Germany
Functional laser surface texturing arose in recent years to a very powerful tool for tailoring the surface properties of parts and components. The design of these textured surfaces often follows a biomimetic approach motivated by living organisms. The laser-textured surfaces, typically exhibiting well-defined features (e.g. periodic structures), can show outstanding properties such as self-cleaning, optimized tribological properties as well as an increased biocompatibility. With the increasing capabilities in functional laser surface texturing, the prediction of surface properties become more and more important in order to reduce the development time of those functionalities. Therefore, advanced approaches for the prediction of the properties of laser-processed surfaces -the so called predictive modelling - are required and in the scope of current research activities. The industrialization of functional laser surface texturing necessitates an efficient production of laser-textured surfaces which still represents one of the greatest technical challenges. In this context, Direct Laser Interference Patterning (DLIP) has been identified as an outstanding technology for the efficient fabrication of tailored surface structures. The DLIP approach can show impressive processing speeds (up to 1 m2/min) as well as a superior flexibility in achieving extremely versatile surface structures.
This work gives an overview about recent developments of the DLIP technology by focusing on the topics: structure flexibility, prediction, process productivity, technical implementations and recent examples of achieved surface functionalities. The work especially focuses on novel ways to predict surface functionalities using machine learning approaches as well as on large-area structuring with surfaces exceeding that of an A4 sheet.
