BEGIN:VCALENDAR BEGIN:VEVENT SUMMARY:AME Seminar (Virtual) DESCRIPTION:Speaker: Sebastian Pattinson, University of Cambridge Talk Title: Generalisable 3D Printing Error Detection and Correction via Neural Networks Abstract: Material extrusion is the most widespread additive manufacturing method but its application in end-use products is limited by vulnerability to errors. Humans can detect errors but cannot provide continuous monitoring or real-time correction. Existing automated approaches are not generalisable across different parts, materials, and printing systems. In this talk I will discuss recent work in our lab where we train a multi-head neural network using images automatically labelled by deviation from optimal printing parameters. The automation of data acquisition and labelling allows the generation of a large and varied extrusion 3D printing dataset, containing 1.2 million images from 192 different parts labelled with printing parameters. The thus trained neural network, alongside a control loop, enables real-time detection and rapid correction of diverse errors that is effective across many different 2D and 3D geometries, materials, printers, toolpaths, and even extrusion methods. Biography: Sebastian Pattinson is an Assistant Professor in the Department of Engineering at the University of Cambridge. His group develops 3D printers that learn how to make things better and uses these to make better medical devices. Before joining the Cambridge, Sebastian was a postdoctoral fellow in the Department of Mechanical Engineering at MIT focusing on 3D printed materials and devices. He received Ph.D. and Masters degrees in the Department of Materials Science & Metallurgy at the University of Cambridge, where he developed nanomaterial synthesis methods. His awards include a UK Academy of Medical Sciences Springboard award; US National Science Foundation postdoctoral fellowship; UK Engineering and Physical Sciences Research Council Doctoral Training Grant; MIT Translational Fellowship; and a (Google) X Moonshot Fellowship. Host: AME Department More Info: https://ame.usc.edu/seminars/ Webcast: https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09 DTSTART:20221130T153000 LOCATION: URL;VALUE=URI:https://ame.usc.edu/seminars/ DTEND:20221130T163000 END:VEVENT END:VCALENDAR