In many metallurgy and materials science labs, there is a “sleeping giant”: a 15-year-old upright or inverted microscope. Its chassis is heavy, its stage is smooth, and its optics—those high-quality objective lenses—are still as sharp as the day they were unboxed. However, it sits underused because the imaging process is stuck in the past. Researchers
Metallography has long relied on manual analysis of microstructures to evaluate material properties. However, recent advances in artificial intelligence (AI) and computer vision are transforming this field. From automated grain size detection to complex microstructure classification, AI is enabling faster, more accurate, and scalable metallographic analysis. The Traditional Challenge Conventional metallography involves: While effective, these