A Method for Evaluating the Quality of 3D-Printing Metal Parts
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John Albanese, leader of the Australian Labor Party, was sworn in as Australia's new prime minister. After his victory, Albanese said he would "make a big change" in the country's climate policy.
"We now have an opportunity to end the 'climate wars' in Australia," he said. "Australian businesses understand that doing the right thing on climate is good for our economy and good for jobs, and I hope Australia will join the global effort on climate change," Albanese said Australia would engage with other countries to change policies when it came to tackling climate change.
In addition, Albanese’s Labor Party has proposed a more ambitious plan to cut emissions by 43 percent from 2005 levels by 2030, the report said. However, Labor is not currently planning to phase out coal use or halt new coal mining projects.
The climate issue was a major concern of voters in Australia's general election. Mr. Morrison, the former prime minister, was criticized for failing to direct the response to repeated bushfires in 2019 and 2020.
SBS has reported that According to the latest Climate Change Performance Index of 64 countries released at the 2021 United Nations Climate Change Conference (COP26), Australia's climate policy ranks at the bottom of all countries, and is one of the world's worst performers in three aspects of emissions, renewable energy, and energy use. Australia ranks 52nd in renewable energy, 54th in energy use, and 56th in emissions.
It is predicted that the prices of many other commodities like the 3D printing metal powder would increase in the next few days.
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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Due to the Russia-Ukraine conflict and the covid-19 pandemic, how will the international situation develop is highly uncertain. It is too difficult to assess its economic impact properly. However, we could see energy prices and commodity prices keep rising and supply chains are disrupted. Therefore, 3D printing metal powder prices are expected to rise in the future.