@article{HoltmannspoetterCzarneckiFeuchtetal., author = {Holtmannsp{\"o}tter, Jens and Czarnecki, J{\"u}rgen von and Feucht, Florian and Wetzel, Michael and Gudladt, Hans Joachim and Hofmann, Timo and Meyer, J. C. and Niedernhuber, Michal}, title = {On the Fabrication and Automation of Reliable Bonded Composite Repairs}, series = {Journal of adhesion}, volume = {91}, journal = {Journal of adhesion}, number = {1-2}, publisher = {Taylor\&Francis}, doi = {10.1080/00218464.2014.896211}, pages = {39 -- 70}, abstract = {For structures made of carbon fiber-reinforced plastics (CFRP), fast, robust, and reliable repair technologies are mandatory for economical usage. In this paper, the authors explain their strategy and experiences. An automated process is proposed to achieve the challenging goals. A general overview on the origin, effects, and analysis of contaminants in CFRP structures and the relationship to the achievable strength of adhesive bonds are given. For the repair of composite structures using adhesive bonding, surface pretreatment is a key factor in terms of reliability and strength. Different surface treatment processes such as grinding, grit blasting, plasma and pulsed lasers treatments are discussed. Furthermore, the possibilities and technical implementation of an automated milling process for the repair of composite structures are presented. This change from manual production to automation tremendously improved the quality and duration of the repair and allows the creation of a uniform surface for adhesive bonding. Further integration of novel technologies is discussed and will further support and enhance the repair in the near future.}, language = {en} } @inproceedings{WindbuehlerOkkesimChristetal., author = {Windb{\"u}hler, Anna and Okkesim, S{\"u}kr{\"u} and Christ, Olaf and Mottaghi, Soheil and Rastogi, Shavika and Schmuker, Michael and Baumann, Timo and Hofmann, Ulrich G.}, title = {Machine Learning Approaches to Classify Anatomical Regions in Rodent Brain from High Density Recordings}, series = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022): 11-15 July 2022, Glasgow, Scotland, United Kingdom}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2022): 11-15 July 2022, Glasgow, Scotland, United Kingdom}, publisher = {IEEE}, doi = {10.1109/EMBC48229.2022.9871702}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-35296}, pages = {3530 -- 3533}, abstract = {Identifying different functional regions during a brain surgery is a challenging task usually performed by highly specialized neurophysiologists. Progress in this field may be used to improve in situ brain navigation and will serve as an important building block to minimize the number of animals in preclinical brain research required by properly positioning implants intraoperatively. The study at hand aims to correlate recorded extracellular signals with the volume of origin by deep learning methods. Our work establishes connections between the position in the brain and recorded high-density neural signals. This was achieved by evaluating the performance of BLSTM, BGRU, QRNN and CNN neural network architectures on multisite electrophysiological data sets. All networks were able to successfully distinguish cortical and thalamic brain regions according to their respective neural signals. The BGRU provides the best results with an accuracy of 88.6 \% and demonstrates that this classification task might be solved in higher detail while minimizing complex preprocessing steps.}, language = {en} } @article{FreeseHoltmannspoetterRaschendorferetal., author = {Freese, Jens de and Holtmannsp{\"o}tter, Jens and Raschendorfer, Stefan and Hofmann, Timo}, title = {End milling of Carbon Fiber Reinforced Plastics as surface pretreatment for adhesive bonding - effect of intralaminar damages and particle residues}, series = {The Journal of Adhesion}, volume = {96}, journal = {The Journal of Adhesion}, number = {12}, publisher = {TAYLOR \& FRANCIS}, doi = {10.1080/00218464.2018.1557054}, pages = {1122 -- 1140}, abstract = {In this study, the use of dry end milling of carbon fiber reinforced plastics (CFRP) as surface pretreatment for high-strength (structural) adhesive bonding was investigated. Surfaces were pretreated using different milling parameters; subsequently, they were adhesively bonded and tested. In comparison with sanding and other industrial standard pretreatment methods, the measured adhesive strength was significantly lower. Detailed surface investigations utilizing field-emission scanning electron microscopy could identify two major effects for lower adhesion strength. Intralaminar damages and microparticle residues on the created surface reduced the strength of the CFRP adhesive joints. This eventually explains results from investigations on milling pretreated repairs. By application of power ultrasound cleaning equipment and coating with low viscosity epoxy primers, the authors showed a way to overcome the discovered drawbacks and to improve bond strength significantly. Surface roughness measurements showed that the arithmetical mean roughness R(a)can be used as an effective value for assessment of mechanical pretreated CFRP surfaces as well as for the quality of necessarily following cleaning processes.}, language = {en} }