Enabling future generations of self-sensing materials

A team of engineers, led by researchers from the university, has created the first system capable of modelling the complex physics of 3D-printed composites that can detect strain, load, and damage by simply measuring electrical current.

This innovation allows material scientists to predict how new structures can be fine-tuned to produce specific combinations of strength, stiffness, and self-sensing properties. Such advancements could drive the development of revolutionary new technologies across various industries.

In aerospace and automotive fields, for instance, materials developed using these insights could enable real-time structural integrity monitoring in aircraft, spacecraft, and vehicle components, improving safety and maintenance efficiency. In civil engineering, these materials could help create smart infrastructure capable of continuously assessing the condition of bridges, tunnels, and high-rise buildings, detecting potential problems before they lead to failure. Similar benefits could be extended to robots in automated manufacturing and even to soldiers, who could use such materials to monitor the integrity of body armour.

The process of 3D printing, also known as additive manufacturing, creates structures by melting and layering strands of plastic. Over time, researchers have developed more complex materials with unique properties, such as lattice structures with honeycomb-like chambers that balance weight and strength. By integrating carbon nanotubes into these materials, they can also carry an electrical current and monitor their structural integrity using piezoresistivity. Changes in electrical current can signal damage, prompting maintenance before failures occur.

The research, led by Professor Shanmugam Kumar from the University of Glasgow’s James Watt School of Engineering, and published in Advanced Functional Materials, demonstrates how these 3D-printed materials could self-monitor without the need for additional hardware. Professor Kumar explained: “Imparting piezoresistive behaviour to 3D-printed cellular materials allows them to monitor their own performance without added equipment. This means we can create low-cost, easy-to-manufacture materials with the ability to detect damage and measure its extent. These autonomous sensing architected materials offer great potential for the development of advanced new components.”

Until now, researchers have known about piezoresistive properties in such materials but have relied on trial and error to determine their effectiveness. This new system removes much of the guesswork, offering a more efficient, cost-effective way to design and test self-sensing materials.

The research team combined extensive lab experiments with computer modelling to develop this predictive system. They created lightweight lattice structures from a plastic called polyetherimide (PEI), mixed with carbon nanotubes, and tested them for stiffness, strength, energy absorption, and self-sensing capabilities. Using sophisticated modelling, they predicted how these materials would behave under various loads. To validate their results, they performed real-world testing using infrared thermal imaging to visualise electrical current flow and how it relates to the material’s performance.

Their findings showed that the models could accurately predict how these 3D-printed materials would respond to different stress levels and how electrical resistance would be impacted. These results could play a crucial role in advancing additive manufacturing by enabling better prediction of material performance before physical prototypes are created.

This research builds on the team’s earlier work, which focused on predicting how manufacturing-induced flaws could affect the structural integrity of 3D-printed materials.

Professor Kumar added: “With this study, we have developed a comprehensive system capable of modelling the performance of self-sensing, 3D-printed materials. Informed by rigorous experimentation and theory, it’s the first system of its kind that models these materials across multiple scales and incorporates various physics.”

While this study focused on PEI materials embedded with carbon nanotubes, the modelling approach could be applied to other materials created via additive manufacturing. Professor Kumar expressed hope that this system will inspire further research into autonomous sensing architected materials and unlock new possibilities for material design across numerous industries.