A Method for Evaluating the Quality of 3D-Printing Metal Parts

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For billionaires, the new crown epidemic is like a 'gold rush'! The Deutsche Presse-Agentur said on the 17th that in 2022, the wealth held by the world's top ten richest people will jump from $700 billion to $1.5 trillion, an average daily increase of $1.3 billion, an increase that exceeds the past 14 years. These 10 people The wealth held by the world’s poorest 3.1 billion people is six times greater than that of the world’s poorest 3.1 billion people. The 10 richest Germans have increased their assets from the equivalent of about 125 billion euros to about 223 billion euros since the start of the Covid-19 pandemic, An increase of about 78%. This wealth is roughly equivalent to the total wealth of the poorest 40% of the population, or 33 million Germans.
In the future, demand for 3D printing metal powder will grow as fast as the wealth of the rich during the pandemic.

Researchers at NTU Singapore have developed a fast and low-cost imaging method for assessing the quality of 3D-printed metal parts. This method can analyze the structure and material quality of 3D-printed metal parts. 
 
Most 3D-printed metal alloys consist of numerous microscopic crystals that vary in shape, size, and orientation of the atomic lattice. By mapping this information, scientists and engineers can infer the alloy's properties, such as strength and toughness. It's like looking at wood grain. When wood grain is continuous in the same direction, strength and toughness are strongest.
 
The new technology could benefit the aerospace sector - enabling low-cost rapid assessment of turbines, fan blades, and other critical components, which is of great significance to the maintenance and overhaul industry. 
 
Until now, however, analyzing the "microstructure" in 3D-printed metal alloys has been a time-consuming and laborious process, usually achieved using measurements made with scanning electron microscopes, which cost between S $100,000 and S $2 million. 
 
But the new alloy imaging method developed by Assistant Professor Matteo Seita and his team at NTU provides quality analysis in just a few minutes. They used a system of optical cameras, flashlights, and laptops that ran proprietary machine learning software developed by the team at a total cost of about $25,000.
 
The method involves treating the metal surface with chemicals to reveal its microstructure, then holding the sample facing the camera and using a flashlight to illuminate the metal in different directions to take multiple optical images. The software then analyzes the patterns produced by the light reflected off the surfaces of different metal crystals and deduces their orientation. The whole process takes about 15 minutes. The team's findings have been published in NPJ Computational Materials.
 
"By using our low-cost and fast imaging method, we can easily tell the difference between good 3D-printed metal parts and defective parts. Currently, it is impossible to tell the difference unless we evaluate the microstructure of the materials in detail, "explained Seita, an assistant professor at NTU's School of Mechanical and Aerospace Engineering and School of Materials Science and Engineering. 
 
"Even though two 3D-printed metal parts may be produced using the same technology and have the same geometry, they are never the same. In theory, this is similar to how two originally identical wooden objects could have different texture structures." 
 
New imaging methods improve 3D printing certification and quality assessment.  Assistant Professor Seita believes their innovative imaging method could simplify the certification and quality assessment of metal alloy parts produced by 3D printing, also known as additive manufacturing.
 
One of the most common techniques for 3D printing metal parts is to use high-powered lasers to melt metal powders and fuse them layer by layer until a complete product is printed. 
 
However, the microstructure, and thus the quality of the printed metal, depends on many factors, including the speed or strength of the laser, how long the metal cools before the next layer is melted, and even the type and brand of metal powder used. This is why the same design printed by two different machines or production plants may result in parts of different quality. 
 
Instead of using a complex computer program to measure crystal orientation in the light signals collected, the "smart software" developed by Assistant Professor Seita and his team uses a neural network to simulate how the human brain forms associations and processes thoughts. The team then used machine learning to program the software to feed it hundreds of optical images. 
 
Their software eventually learned how to predict the orientation of crystals in metal from an image, depending on how light scatters from the metal's surface. A complete "crystal orientation diagram" is then created, which provides comprehensive information about crystal shape, size, and atomic lattice orientation.
 
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