New research models complex physics of self-sensing additive composites.
A team of engineers has developed the first system capable of modelling the complex physics of 3D printed composites which can detect strain, load or damage using nothing more than a measure of electrical current.
Allowing materials scientists to predict how new structures can be fine-tuned to produce specific combinations of strength, stiffness and self-sensing properties could help catalyze the development of smart materials.
According to University of Glasgow researchers, new materials produced using the team’s insights could enable real-time monitoring of structural integrity in aircraft, spacecraft and vehicle components for enhanced safety and maintenance efficiency.
In civil engineering, these materials could enable developments in smart infrastructure, providing continuous assessment of the structures of bridges, tunnels and high-rise buildings.
As additive manufacturing (AM) technology has developed, researchers have been able to create increasingly complex materials with unique properties. Weaving fine strands of carbon nanotubes throughout materials can allow them to carry an electrical current, imbuing them with the ability to monitor their own structural integrity via piezoresistivity. As a result, a change in the current readout can indicate that the material has been crushed or stretched.
Professor Shanmugam Kumar led the research. In a press release, he said:
“While researchers have known about these properties for some time now, what we’ve not been able to do is provide a way to know in advance how effective new attempts at creating novel self-sensing materials will be. Instead, we have often relied on trial and error to determine the optimal approach for developing these materials, which can be both time-consuming and costly.”
Kumar and his team developed their system through a rigorous set of lab experiments combined with modelling.
They used polyetherimide (PEI) mixed with carbon nanotubes to create a series of four different lightweight lattice structure designs and then tested them for stiffness, strength, energy absorption and self-sensing capabilities.
Using computer modeling, they developed a system aimed at predicting how the materials would respond to a varied set of loads, then validated their multiscale finite element model’s predictions by subjecting the materials to intense analysis under real-world conditions. They used infrared thermal imaging to visualize electrical current flowing through the materials in real-time, leveraging the analogy between heat and current flow.
What they found was that their models could accurately predict how the materials would respond to various combinations of stress and strain, as well as how their electrical resistance would be affected. These results could help underpin future developments in additive manufacturing by providing insights into how proposed new materials will perform before the first real-world prototype is printed.
The research builds on previous developments from the team, including a recently published paper showcasing another approach to modelling which enabled them to predict how additive manufacturing-induced flaws can affect the structural integrity of new designs.
Professor Kumar added: “While we focused on PEI materials with embedded carbon nanotubes in this paper, the multiscale finite element modelling our results are based on could be easily applied to other materials which can be created through additive manufacturing too.
The team’s paper, entitled “Autonomous Sensing Architected Materials” is published in Advanced Functional Materials.