Developed at Nanyang Technological University, Singapore (NTU Singapore), the method involves treating the metal surface with chemicals to first reveal the microstructure, then placing the sample facing a camera, which takes multiple optical images as the flash illuminates the metal from different directions. Software then analyses the patterns produced by light reflected off the surface of different metal crystals, deducing their orientation.
The entire process, described in npj Compuitational Materials, takes about 15 minutes to complete. According to the researchers, the technology could benefit a range of sectors, including aerospace where low-cost, rapid assessment of mission-critical parts could prove beneficial for the maintenance, repair and overhaul industry.
“Using our inexpensive and fast-imaging method, we can easily tell good 3D-printed metal parts from the faulty ones,” said Asst Prof Matteo Seita, from NTU’s School of Mechanical and Aerospace Engineering and School of Materials Science and Engineering. “Currently, it is impossible to tell the difference unless we assess the material’s microstructure in detail.
“No two 3D-printed metal parts are created equal, even though they may have been produced using the same technique and have the same geometry. Conceptually, this is akin to how two otherwise identical wooden artefacts may each possess a different grain structure.”
Most 3D-printed metal alloys consist of a myriad of microscopic crystals, which differ in shape, size, and atomic lattice orientation. By mapping out this information, scientists and engineers can infer the alloy’s properties, such as strength and toughness. Until now, analysing this microstructure in 3D printed metal alloys has been achieved through laborious and time-consuming measurements using expensive scanning electron microscopes.
Instead of using a complicated computer programme to measure the crystal orientation from the optical signals acquired, the software developed by Asst Prof Seita and his team uses a neural network. The team then used machine learning to programme the software by feeding it hundreds of optical images.
Eventually, their software learned how to predict the orientation of crystals in the metal from the images, based on differences in how light scatters off the metal surface. It was then tested to be able to create a complete ‘crystal orientation map’, which provides comprehensive information about the crystal shape, size, and atomic lattice orientation.
To commercialise their method, the team is now in discussion with NTUitive, NTU’s innovation and enterprise company, to explore the possibility of starting a spin-off company or to license their patent.
