Machine learning unlocks secrets to advanced alloys
The concept of short-range order (SRO)—the arrangement of atoms over small distances—in metallic alloys has been underexplored in materials science and engineering. But the past decade has seen renewed interest in quantifying it, since decoding SRO is a crucial step toward developing tailored high-performing alloys, such as stronger or heat-resistant materials.
Artificial intelligence (AI) is increasingly used to analyze medical images, materials data and scientific measurements, but many systems struggle when real-world data do not match ideal conditions. Measurements collected from different instruments, experiments or simulations often vary widely in resolution, noise and reliability. Traditional machine-learning models typically assume those differences…
In order to successfully 3D-print a metal part to the exacting specifications that many in industry demand, process parameters—including printing speed, laser power, and layer thickness of the deposited material—must all be optimized.
Producing high-performance titanium alloy parts—whether for spacecraft, submarines or medical devices—has long been a slow, resource-intensive process. Even with advanced metal 3D-printing techniques, finding the right manufacturing conditions has required extensive testing and fine-tuning.