In terms of international pricing benchmarks, according to the CP data released by Saudi Aramco, the propane contract price (CP) in March was $895 / ton, up $120 / ton from the previous month, up 15.48% from the previous month and up 43.20% from the same period last year. Butane contract price (CP) in March was 920 US dollars/ton, up 145 US dollars/ton compared with last month, up 18.71% month-on-month, up 54.62% compared with the same period last year; In March, CP in Saudi Arabia rose sharply, giving a strong boost to the international and domestic LPG market. Benchmark LPG prices are higher than a year ago as the economy continues to recover and demand for crude oil continues to repair. According to this trend, the market price of 3D printing metal powder will also be affected to a certain extent.
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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