Low-cost method speeds up analysis of 3D printed metal parts

A fast and inexpensive imaging method analyzes the structure of 3D-printed metal parts and provides insight into the quality of the material, the developers claim.

3D printed metal parts
Analyzing the unique crystal patterns on the surface of a 3D printed metal could pave the way for the certification and quality assessment of parts made through additive manufacturing (Image: NTU Singapore)

Most 3D printed metal alloys consist of a large number of microscopic crystals, which differ in shape, size and atomic lattice orientation. By mapping 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 wide range of sectors, including aerospace, where a low-cost, rapid assessment of mission-critical parts could benefit maintenance, repair and maintenance. and overhaul industry.

Analyzing this microstructure in 3D-printed metal alloys has so far been done through cumbersome and time-consuming measurements with expensive scanning electron microscopes.


It is claimed that the method designed by Nanyang assistant professor Matteo Seita and his team provides the same quality of information in minutes by using a system consisting of an optical camera, a flashlight and a notebook computer with patented machine. learning software developed by the team.

The team’s new method requires first treating the metal surface with chemicals to reveal the microstructure, then placing the sample towards the camera and taking multiple optical images as the flashlight illuminates the metal from different directions.

The software then analyzes the patterns produced by light reflected from the surface of various metal crystals and deduces their orientation. The whole process takes about 15 minutes to complete.

The team’s findings were published in npj Computational Materials last month.

“With our cheap and fast imaging method, we can easily distinguish good 3D printed metal parts from defective parts. Currently, it is impossible to tell the difference unless we assess the microstructure of the material 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 alike, even though they may have been produced using the same technique and have the same geometry. Conceptually, this is similar to how two otherwise identical wooden artifacts can each have a different grain texture.

Asst Prof Seita believes that the imaging method could simplify the certification and quality assessment of metal alloy parts produced by 3D printing.

Rather than using a complicated computer program to measure the crystal orientation of the obtained optical signals, the software developed by Asst Prof Seita and his team uses a neural network. The team then used machine learning to program the software by giving 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 is scattered from the metal surface. It was then tested to create a complete ‘crystal orientation map’, which provides comprehensive information about crystal shape, size and atomic lattice orientation.

To commercialize their method, the team is now in talks with NTUitive, NTU’s innovation and enterprise firm, to explore the possibility of starting a spin-off company or licensing their patent.

Abhishek Maheswari
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