When 3D printing was first introduced in 1985, it marked a major turning point for the manufacturing industry. In addition to being cheaper than traditional manufacturing technologies, it also promised the ability to customize designs and make prototypes on demand. While its technology is still considered relatively new, there has been an accelerating demand for 3D printing methods across sectors in the past decade, ranging from aerospace and defense to medicine.
Yet, Associate Professor Pablo Valdivia y Alvarado from the Singapore University of Technology and Design (SUTD) believes that there are still ways to go before 3D printing can achieve its full potential. In traditional 3D printing, a nozzle is used to print the material layer by layer, and the path that the nozzle takes is known as the toolpath.
However, layer-by-layer printing is incompatible for use with materials like silicone, epoxies, and urethanes that are slow-curing and take more time to harden. These types of materials are often used to create soft mechanical metamaterials which, in turn, are used for lightweight, nature-inspired structures, such as lattices and web structures. Deposition-based processes in 3D printing, such as direct ink writing, would be able to work with these materials to create such structures, but these suffer from non-optimized toolpaths.
“These issues lead to extended print times and are further exacerbated by the dynamic material behaviors in their uncured state,” explained Assoc Prof Valdivia y Alvarado. It therefore remains challenging to 3D-print complex bioinspired structures.
To tackle this challenge, Assoc Prof Valdivia y Alvarado and his team at SUTD proposed an architected design approach, which they have published in the paper “Architected design and fabrication of soft mechanical metamaterials” published in Advanced Intelligent Systems.
Looking specifically at how direct ink writing can be used to fabricate lightweight structures, the team first designed a method to optimize the toolpaths. By breaking down the object’s 3D design into points and simple shapes, the team could then make use of both segmented and continuous toolpath designs to improve the overall toolpath. Through this, the team was able to generate toolpaths that contained fewer unnecessary starts and stops.
To test the approach, the team printed several types of bioinspired structures using this method for generating optimized toolpaths. The team first sought to tune the properties of the printing materials to further enhance their suitability for direct ink writing. By selecting three commercially available silicone materials, and then adding a modifier known as Thivex, the team created and characterized nine distinct material combinations that were more suitable for direct ink writing.
The researchers then proceeded to 3D-print cilia, webs, leaf-like structures, and lattices using their proposed approach, and tested the functionalities of the structures in different settings. For the 3D-printed cilia, the team found that adding it to suction cups improved the suction cups’ pull-off force; meanwhile, the 3D-printed lattice proved to be an effective energy-absorbing structure, demonstrating a reduction of maximum impact peak forces by up to 85 percent.
With these findings, the team is optimistic about the future development of deposition-based 3D printing methods for printing bioinspired structures.
Said Assoc Prof Valdivia y Alvarado, “Although the approach is still in the research phase, its potential for customized, high-performance designs makes it highly relevant for industries focused on robotics, wearable technologies, and advanced metamaterials.”
He also explained that deposition-based additive manufacturing processes could find a niche in advanced applications, complementing traditional methods that would remain essential for producing high-volume and standardized structures.
For now, the team is focused on improving the scaling efficiency of their method, reducing its costs, and expanding the versatility of the materials to industrial settings. To do this, the team plans to explore multi-material printing which would allow different materials to be printed, in turn creating what the team has coined as “engineered metamaterials.” In addition, the team will also be investigating how machine learning techniques can enable 3D printing users to specify performance metrics for the metamaterial designs that they wish to generate.
“These advancements will further unlock the potential of 3D-printed metamaterials for a wide range of applications, including soft robotics and wearable protective gear,” mused Assoc Prof Valdivia y Alvarado.
