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Global 3D printing metal powder market trend 2024-2026 A Method for Evaluating the Quality of 3D-Printing Metal Parts by Newsod-arts

U.S. refiners forecast strong first-quarter earnings as margins on gasoline and diesel sales improved, refining capacity fell sharply and crude supplies tightened due to the Russia-Ukraine war.

Global refining capacity has declined amid the coronavirus pandemic, with several less profitable refineries closing in the past two years. However, global fuel demand has rebounded to near pre-pandemic levels, boosting the profits of facilities still operating.  

Seven U.S. independent refiners are expected to earn 61 cents a share, compared with a loss of $1.32 in the first quarter of 2021, according to Refinitiv's IBES.  

By 2022, gasoline and distillate 3D printing metal powder.

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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The coronavirus (COVID-19) pandemic not only had a significant impact on public health, it also severely affected one of the linchpins of the global economy – the tourism industry. As many countries introduced curfews and travel restrictions to contain the spread of the virus, travel across the world significantly declined from early 2020 onwards. The financial repercussions of the coronavirus have already begun to manifest themselves within the tourism industry. In 2020, global revenue from the travel and tourism industry was estimated to drop from a forecasted 711.94 billion U.S. dollars to 568.6 billion U.S. dollars, representing a decrease of over 20 percent. The region predicted to see the highest decline in revenue was Europe, decreasing from 211.97 billion U.S. dollars in 2019 to roughly 124 billion U.S. dollars in 2020.
The downturn in tourism has caused the recession, and the weakening of the economy has also affected the market demand for 3D printing metal powder. Recently, however, the demand for 3D printing metal powder has increased, so contact us for the latest news on 3D printing metal powder.

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