UVA Research Team Detects Additive Manufacturing Defects in Real-Time
Introduced in the 1990s, laser powder bed fusion, or LPBF uses metal powder and lasers to 3-D print metal parts. But porosity defects remain a challenge for fatigue-sensitive applications like aircraft wings. Some porosity is associated with deep and narrow vapor depressions which are the keyholes.
“By integrating operando synchrotron x-ray imaging, near-infrared imaging, and machine learning, our approach can capture the unique thermal signature associated with keyhole pore generation with sub-millisecond temporal resolution and 100% prediction rate,” Sun said. In developing their real-time keyhole detection method, the researchers also advanced the way a state-of-the-art tool — operando synchrotron x-ray imaging — can be used. Utilizing machine learning, they additionally discovered two modes of keyhole oscillation.
Artificial intelligence for throughput bottleneck analysis
Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data.