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- ItemImproved development procedure to enhance the stability of microstructures created by two-photon polymerization(Amsterdam : Elsevier, 2018) Purtov, Julia; Verch, Andreas; Rogin, Peter; Hensel, RenéNatural functional surfaces often rely on unique nano- and micropatterns. To mimic such surfaces successfully, patterning techniques are required that enable the fabrication of three-dimensional structures at the nanoscale. It has been reported that two-photon polymerization (TPP) is a suitable method for this. However, polymer structures fabricated by TPP often tend to shrink and to collapse during the fabrication process. In particular, delicate structures suffer from their insufficient mechanical stability against capillary forces which mainly arisein the fabrication process during the evaporation of the developer and rinsing liquids. Here, we report a modified development approach, which enables an additional UV-treatment to post cross-link created structures before they are dried. We tested our approach on nanopillar arrays and microscopic pillar structures mimicking the moth-eye and the gecko adhesives, respectively. Our results indicate a significant improvement of the me- chanical stability of the polymer structures, resulting in fewer defects and reduced shrinkage of the structures.
- ItemApplication of machine learning to object manipulation with bio-inspired microstructures(Rio de Janeiro : Elsevier, 2023) Samri, Manar; Thiemecke, Jonathan; Hensel, René; Arzt, EduardBioinspired fibrillar adhesives have been proposed for novel gripping systems with enhanced scalability and resource efficiency. Here, we propose an in-situ optical monitoring system of the contact signatures, coupled with image processing and machine learning. Visual features were extracted from the contact signature images recorded at maximum compressive preload and after lifting a glass object. The algorithm was trained to cope with several degrees of misalignment and with unbalanced weight distributions by off-center gripping. The system allowed an assessment of the picking process for objects of various mass (200, 300, and 400 g). Several classifiers showed a high accuracy of about 90 % for successful prediction of attachment, depending on the mass of the object. The results promise improved reliability of handling objects, even in difficult situations.