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An Artificial Intelligence Approach Towards Sensorial Materials

Authors:
Florian Pantke
Stefan Bosse
Michael Lawo
Dirk Lehmhus
Matthias Busse

Keywords: sensorial material; finite element method; sensor network; machine learning; multi-agent system

Abstract:
Sensorization aims at equipping technical structures with an analog of a nervous system by providing a network of sensors and communication facilities that link them. The objective is that, instead of having been designed to loads and tested to conditions, a structure can experience and report design constraint violations by means of real-time self-monitoring. Specialized electronic components and computational algorithms are needed to derive meaning from the combined signals of integrated sensors. For this task, artificial intelligence approaches constantly gain importance; the more so as the trend of ever increasing sensor network size and density suggests that sensor and structure may soon become one, forming a sensorial material. Current simulation techniques capture many aspects of sensor networks and structures. For decision making and communication, the intelligent agent paradigm is an accepted approach, as is finite element analysis for structural behavior. To gain knowledge how sensorial structures can most effectively be built, an artificial intelligence based process for the design of such structures was developed that uses machine learning methods for fast load inference. It is presented in this paper, along with evaluation results obtained in experiments using a finite element model of a strain gauge equipped plate, which demonstrate the general practicability.

Pages: 62 to 68

Copyright: Copyright (c) IARIA, 2011

Publication date: September 25, 2011

Published in: conference

ISSN: 2308-3735

ISBN: 978-1-61208-154-0

Location: Rome, Italy

Dates: from September 25, 2011 to September 30, 2011