Stefan Bosse
University of Bremen, Dept. Mathematics & Computer Science
University of Koblenz, Fac. Computer Science 11.4.2018
sbosse@uni-bremen.de |
Smart Materials: Fusion of Sensorial- and Adaptive Materials with Information Processing
A material or structure with integrated sensing and data processing (ICT)
A material or structure with integrated sensing, data processing and actuation that can control and change material or structure properties
McEvoy, 2015 [1]
In our understanding, a smart material provides the following major features:
Perception using various kinds of sensors, e.g., measuring of strain, displacement, temperature, pressure, forces;
Changing of local material and structure properties by actuators, e.g., stiffness or damping variation;
Integrated Information and Communication Technologies (ICT);
Distributed Approach: Local sensor processing and actuator control - Global cooperation and coordination.
Robustness and Self-Organization
Traditionally computation is separated from sensing and control
Smart Materials poses the tight coupling of computation, communication, sensing, and control with loosely coupled nano computers
Algorithmic scaling and distribution are required
Micro Mote M3
| ELM System
|
Micro Mote (M3 ) | ELM System | Atmel Tiny 20 | Freescale KL03 | ARM Cortex Smart Phone | |
Processor | Arm Cortex M0 | C8051F990 (SL) | AVR | Arm Cotex M0+ | Arm Cortex A9 |
Clock | 740kHz max. | 32kHz | - | 48MHz | 1GHz |
CPU Chip Area | 0.1mm2 | 9mm2 | 1mm2 | 4mm2 | 7mm2/ROM |
Sensors | Temperature | - | - | - | Temp, Light, Sound, Accel., Press., Magn. |
Communication | 900MHz radio, optical | optical | electrical | - | 3G/4G, WLAN, USB, Bluetooth, NFC |
Harvester, Battery | Solar cell, Thin film | Solar cell, Coin | - | - | - |
Power Consumption | 70mW / CPU | 160mW / CPU | 20mW | 3mW @ 48MHz | 100mW avg., |
Manufacturing | 180nm CMOS | - | - | - | 40nm CMOS |
Package | Wire bonded | Silicon Stack | PCB | Single Chip | Single Chip |
Computing Eff. ε | 150 | 0.02 | 0.6 | 4.0 | 0.53 |
A component composed of a Smart Material consists of:
Solid body mass nodes with springs connecting nodes:
A spring is an actuator with two optimization target variables:
Stiffness s, Damping d
Each spring is a strain sensor delivering the sensor value σ for the computation of the observation variable
Reduction of global and local stress, strain, or forces of arbitrariely shaped components under varying load situations
Control and Optimization Cycle
Perception using sensors
Comparison of local and global observation variables
Modification of material parameters by actuators
Global
| Segment
| Neighbour
|
Each node agent sends its sensor values to neighbour nodes within a range of radius 1
This completes the set of sensor each node requires
Remote signals are used for sensor distribution using &Delta-distance routing
Global observable computation in a large-scale distributed network is expensive and difficult (failures)
Segmented approach requires network segmentation
Without central instance difficult;
Instead a floating segment window is placed around each node
Each node has observable from all direct neighbours (North, South, West, East, Up, Down)
A chained distribution of data is used in each segment (N nodes ↔ N segments!)
Observable values with distance r=1 and r=2 are collected by each node to compute region observable
Observable from direct neighbours
Direct neighbours deliver also values form their neighbours (opposite to request direction)
MAS World
| Physical World |
Global and segment optimization achieves 40% decrease of total strain and maximum strain energy of mass elements using a linear correction function
Neighbour negotiation is simple but not as efficient as segment approach
Smart Materials poses the tight coupling of computation, communication, sensing, and control with loosely coupled nano computers
Algorithmic scaling and distribution are required
Distributed information processing paradigma: Multi-agent Systems
Multi-domain simulation enables the development and evaluation of different optimization strategies for smart adaptive materials and structures
The SEJAM simulator enables the simulation and analysis of coupled physical and computational systems
Global and segment optimization achieves 40% decrease of total strain and maximum strain energy of mass elements using a linear correction function
[1] M. A. McEvoy and N. Correll, “Thermoplastic variable stiffness composites with embedded, networked sensing, actuation, and control,” Journal of Composite Materials, vol. 49, no. 15, 2015.