The biennial international congress "Materials Science Engineering (MSE 2016)" will take place in Darmstadt from September 27th - 29th, 2016.

We will welcome national and international experts and the congress language is English.

Topic: A - Functional Materials and Devices
Section: A08 - Material-integrated intelligent systems for real time condition awareness

Summary

Development trends in structural health monitoring and control of mechanically loaded structures profit from continuing miniaturization of sensors and sensor network components such as signal- and data processing or communication hardware, energy harvesting and storage systems etc. This technological background supports realization of material-integrated intelligent systems which locate reliable and fault-tolerant distributed data processing and information evaluation within the monitored material itself.

Best use can be made of such systems if the latter is achieved in real time. The final aim is to go beyond load monitoring, by quantifying and localizing a load acting on the structure, by evaluating its effect on structural state, and by incorporating the outcome of this evaluation in future analyses. This implies either recognition or prediction of internal damage within the structure in terms of size, position, geometry and effect on structural performance under all service conditions – information which must then be integrated in the internal models the respective materials and structures use in sensor data interpretation. In order to achieve the reliable integration of such additional functionality, methods for the design and technologies for integration of transducer elements in lightweight structures have to be developed. The main objective is to enable the components to exhibit their intrinsic actuatory and sensory effect.

Addressing these challenges, and providing the required real-time capability, necessitates an interdisciplinary approach which combines in-depth knowledge of integration technology, materials response, failure mechanisms etc. with advanced data evaluation and system identification techniques. Conventional inverse FEM methods, though fast for a fixed structural state, typically lack the ability of internal material model adaptation in response to an identified state of damage. At the same time, the chain that links an external load, the change it may have caused within the material under the given boundary conditions (environment-, service or service history-related) and the recorded sensor signal is still subject to uncertainty. For example, structural performance will be influenced by material-integrated sensors and electronic components via their own mechanical properties, interaction and compliance with host material characteristics, their interfaces with the host material etc. The ensuing uncertainty is a major obstacle towards implementation e.g. of SHM systems in aerospace, as it precludes pinpointing the exact level of safety of an envisaged intelligent material or structure at any moment in time and thus prevents exploitation of the structure up to its true performance limits.

With this background, the symposium means to bring together experts in sensor integration, materials performance evaluation (structural health monitoring, NDT etc.) and structural and failure mechanics (initiation of failure, “effects of defects” etc.) with researchers working on sensor network data evaluation using novel methods from artificial intelligence and applied mathematics to support an interdisciplinary discussion of material-integrated intelligence for structural health monitoring, management and control applications.

As a medium for publication of contributions to the symposium, a special issue of a suitable, well-regarded scientific journal from fields like smart structures or computational methods in materials science and engineering will be organized.


Chair(s):

Axel von Hehl
(IWT Bremen, Germany)

Stefan Bosse
(University of Bremen, Germany)

Lothar Kroll
(Chemnitz University of Technology, Germany, Germany)


Armin Lechleiter
(University of Bremen, Germany)

Dirk Lehmhus
(University of Bremen, Germany)

Richard Fu
(Northumbria University, UK)