EE Colloquium: Bringing Data Analytics and Machine Learning to the Forefront of Additive Manufacturing

Abstract: Additive manufacturing (AM) --the industrial version of 3D printing-- has triggered a revolution in making niche items, such as medical implants and plastic rapid-prototypes. While this seemingly science-fictional ability to “turn bits into atoms” for consumers and small entrepreneurs has received a great deal of publicity, it is in anytime-anywhere-manufacturing where the technology could have its most significant impact. However, part quality may not always be guaranteed and residual anomalies in the print process, that are often stochastic in nature, can never be eliminated completely. Clearly, an excess of anomalies or flaws within the part may render the printed component unusable. Therefore, data driven techniques that detect and identify such process anomalies during the AM build process or automatically characterize the quality of the part immediately after the build are critically needed. Both approaches, in-situ and ex-situ, may offer on-site part certification in the future. This talk will cover ongoing research and development efforts at PSU/ARL that aim to bring data science and data analytics to the forefront of metal AM. Instrumentation and data acquisition capabilities at PSU/CIMP-3D will be presented. In addition, machine perception techniques for Xray Computed Tomography (CT) inspection as well as machine learning algorithms for in-situ defect detection will be discussed.   

Bio:  Dr. Jan Petrich is a Research and Development Engineer in the Geospatial Intelligence Department at Penn State’s Applied Research Laboratory. His research interests include machine perception, data analytics and artificial intelligence as applied to remote sensing and additive manufacturing processes. Dr. Petrich received his Ph.D. in Electrical and Computer Engineering from Virginia Tech in 2009 and a M.Sc. in Electrical Engineering from the University of Technology in Dresden, Germany in 2004. Prior to joining PSU/ARL, he gained five years of industry experience at NextGen Aeronautics Inc, a small DoD contractor. At NextGen, he has been the PI for multiple DoD SBIR/STTR efforts focusing on novel and fault tolerant control and estimation frameworks for dynamical systems and processes.