UC3M Develops Computational System to Optimise 3D-Printed Materials

Universidad Carlos III de Madrid (UC3M) has developed a computational model that can improve the behaviour of multifunctional structures manufactured using 3D printers.

UC3M Develops Computational System to Optimise 3D-Printed Materials

The model was made in collaboration with the University of Oxford, Imperial College London, and the BC Materials research centre in the Basque Country.

This technology can be applied in sectors such as biomedicine, soft robotics, and other branches of engineering.

The Role of 3D Printing and Multifunctional Structures in Engineering

“Currently, conductive thermoplastics are very promising because of their ability to transmit electrical signals while providing structural support,” said one of the study's authors, Daniel García-González, from the UC3M Department of Mechanics of Continuous Media and Theory of Structures.

“But the main challenge in the manufacture of these materials is the control of their internal structure, since the bonding between filaments and the presence of small cavities affect both their mechanical resistance and their capacity to transmit electrical signals,” he said.

Applications of Computational Models in Soft Robotics and Biomedicine 

The researchers have integrated advanced computational tools and experimental trials, which allows them to manufacture structures that are sensitive and capable of transforming mechanical signals into electrical signals.

“A key point about this discovery is that it can be extrapolated to other types of 3D printing technology in which softer materials could be used,” said Javier Crespo, from UC3M's Department of Mechanics of Continuous Media and Theory of Structures.

Crespo is optimistic that it will be possible to design materials that lay the foundations for future advances in additive manufacturing, thanks to the combination of these new computational tools.

The Future of Additive Manufacturing and Machine Learning 

“For example, in the field of engineering, these structures could be used both for the manufacture of soft robots and for obtaining virtual data that can serve machine learning technologies,” said Crespo.