Research Team Detects Additive Manufacturing Defects in Real Time

 Another interesting area for machine learning had not considered .... 

Research Team Detects Additive Manufacturing Defects in Real Time

University of Virginia Engineering, January 6, 2023

A research team led by the University of Virginia's Tao Sun employed machine learning to detect defects in additive manufacturing (also known as three-dimensional printing) in real time. The research focused on the formation of keyhole pores, one of the major defects in laser powder bed fusion, which uses metal powder and lasers to three-dimensionally print metal parts. Said Sun, "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 the approach “provides a viable solution for high-fidelity, high-resolution detection of keyhole pore generation that can be readily applied in many additive manufacturing scenarios." .... ' 

Amazon Lookout spots Defects and Anomalies in Visuals

Had not heard of this particular AWS service, could have been useful on our industrial manufacturing and supply chain areas.  Just as we use our vision to scan for anomalies in representations of systems or data. 

Amazon’s Lookout for Vision spots defects and anomalies in visual representations  By Duncan Riley in SiliconAngle

Amazon Web Services Inc. today announced the general availability of Amazon Lookout for Vision, a cloud service that uses machine learning to spot defects and anomalies in visual representation using computer vision.

Designed with manufacturing companies in mind, the service can identify differences in images of objects at large scale, delivering the ability to identify manufacturing and production defects such as cracks, dents, incorrect colors and irregular shapes. The technology uses a technique called “few-shot learning” so it can train a model for a customer using as few as 30 baseline images.

Amazon Lookout for Vision can process thousands of images an hour to spot defects and anomalies with no machine learning experience required. Customers send camera images to Amazon Lookout for Vision in real-time to identify anomalies, such as damage to a product’s surface, missing components and other irregularities in production lines. In addition to enabling the service to detect anomalies without large amounts of training data, this capability allows the service to adapt to a wide range of inspection tasks within industrial settings.

Upon analyzing data, Amazon Lookout for Vision reports images that differ from the baseline via the service dashboard or the “DetectAnomalies” real-time application programing interface so that appropriate action can be taken.  ...  '