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.
According to scientists from Nanyang Technological University, Singapore (NTU Singapore), 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.
Until now, analysing this microstructure in 3D printed metal alloys has been achieved through laborious and time-consuming measurements using expensive scanning electron microscopes.
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The method designed by Nanyang Assistant Professor Matteo Seita and his team is claimed to provide the same quality of information in minutes by using a system consisting of an optical camera, a flashlight, and a notebook computer that runs proprietary machine-learning software developed by the team.
The team’s new method first requires treating the metal surface with chemicals to reveal the microstructure, then placing the sample facing the camera, and taking multiple optical images as the flashlight illuminates the metal from different directions.
The software then analyses the patterns produced by light that is reflected off the surface of different metal crystals and deduces their orientation. The entire process takes about 15 minutes to complete.
The team’s findings were published in npj Computational Materials last month.
“Using our inexpensive and fast-imaging method, we can easily tell good 3D-printed metal parts from the faulty ones. Currently, it is impossible to tell the difference unless we assess the material’s microstructure in detail,” said Asst Prof Seita, from NTU’s School of Mechanical and Aerospace Engineering and School of Materials Science and Engineering.
“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.”
Asst Prof Seita believes the imaging method could simplify the certification and quality assessment of metal alloy parts produced by 3D printing.
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.
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