DigitalMobilEuropan Campus Rottal-InnBeitrag (Sammelband oder Tagungsband)
L. Patane, T. Hoinville, H. Cruse, M. Schilling, A. Drimus, P. Arena, Ch.J. Dallmann, T. Krause, R. Strauss, A. Schneider, V. Dürr, J. Paskarbeit, A. Vitanza, Stefan Mátéfi-Tempfli, J. Schmitz
Integrative Biomimetics of Autonomous Hexapedal Locomotion
[Review paper under publication]
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) [November 3-8, 2019; Macau, China]
2019
NachhaltigAngewandte Naturwissenschaften und WirtschaftsingenieurwesenIPH TeisnachZeitschriftenartikel
G. Berger, I. Widdershoven, M. Schulz, R. Bergmann, D. Ramm, A. Beutler, J. Asfour, G. Schneider, T. Blümel, C. Elster, R. Meeß, H. Klawitter, R. Schachtschneider, M. Sandner, I. Fortmeier, K. Kubo, F. Löffler, Johannes Liebl, M. Stavridis, M. Wendel, C. Pruss
Interlaboratory comparison measurements of aspheres
Measurement Science and Technology, vol. 29, no. 5
2018
DOI: 10.1088/1361-6501/aaae96
Abstract anzeigen
The need for high-quality aspheres is rapidly growing, necessitating increased accuracy in their measurement. A reliable uncertainty assessment of asphere form measurement techniques is difficult due to their complexity. In order to explore the accuracy of current asphere form measurement techniques, an interlaboratory comparison was carried out in which four aspheres were measured by eight laboratories using tactile measurements, optical point measurements, and optical areal measurements. Altogether, 12 different devices were employed. The measurement results were analysed after subtracting the design topography and subsequently a best-fit sphere from the measurements. The surface reduced in this way was compared to a reference topography that was obtained by taking the pointwise median across the ensemble of reduced topographies on a $1000 \times 1000$ Cartesian grid. The deviations of the reduced topographies from the reference topography were analysed in terms of several characteristics including peak-to-valley and root-mean-square deviations. Root-mean-square deviations of the reduced topographies from the reference topographies were found to be on the order of some tens of nanometres up to 89 nm, with most of the deviations being smaller than 20 nm. Our results give an indication of the accuracy that can currently be expected in form measurements of aspheres.
NachhaltigAngewandte Naturwissenschaften und WirtschaftsingenieurwesenTC WeißenburgBeitrag (Sammelband oder Tagungsband)
Damigos, Y., V. Kalidromitis, S. Camarinopoulos, Frankenstein, B., Loupos, K., A. Tsertou, Gerhard, R., Amditis, A., Anastasopoulos, S. Lenas, K. Lenz, S. Schneider, M. Hill, Wirges, W., Dmitry Rychkov, A. Adesiyun, V. Tsaoussidisa
Skin-like Sensor Enabled Bridge Structural Health Monitoring System
Proceedings of the 8th European Workshop On Structural Health Monitoring (EWSHM 2016), Bilbao, Spain
2016
Abstract anzeigen
Structural Health Monitoring (SHM) has an important role in the management of transport infrastructure. However, most SHM techniques are based on data obtained from dense networks of point-based sensors (rather than sparse networks of spatial sensors) and so, inrelative terms, they are costly to implement. Most commercially available strain sensors have a limited maximum range - typically 1% to 2% - and are not well-suited to providing information of a severe loss of structural integrity. The SENSKIN project develops a dielectric-elastomer and micro-electronics-based skin-like sensor, based on the use of a largehighly extensible capacitance sensing membrane and advanced micro-electronic circuitry, for monitoring transport infrastructure - such as bridges. The sensor will provide spatial measurements of strain of more than 10% and is being designed to (a) require low power to operate, (b) be easy to install (c) have a comparable or lower cost than conventional strain sensors, (d) allow simple signal processing, and (e) have the ability to self-monitor and self-report. The system will support the new and emerging technology of Delay/Disruption-Tolerant Networking to secure that strain measurements acquired will reach the base station even under extreme conditions where communications may be disrupted. SENSKIN also develops a Decision-Support-System (DSS) for proactive condition-based structural interventions under normal operating conditions and reactive emergency intervention following an extreme event. In assessing potential rehabilitation options, the DSS will use the data supplied by the SENSKIN sensors together with advanced structural analysis models, whilst taking account of the lifecycle economic, social and environmental implications. The overall monitoring system will be evaluated and benchmarked on actual bridges of Egnatia Highway (Greece) and Bosporus Bridge (Turkey). This paper describes the concept and principles of the SENSKIN sensing system, and its various components with attention to end-user requirements, specifications and system architecture.
Version: 2.1.0