Real-time detection of clustered events in video-imaging data with applications to additive manufacturing

Abstract

The use of video-imaging data for in-line process monitoring applications has become popular in industry. In this framework, spatio-temporal statistical process monitoring methods are needed to capture the relevant information content and signal possible out-of-control states. Video-imaging data are characterized by a spatio-temporal variability structure that depends on the underlying phenomenon, and typical out-of-control patterns are related to events that are localized both in time and space. In this article, we propose an integrated spatio-temporal decomposition and regression approach for anomaly detection in video-imaging data. Out-of-control events are typically sparse, spatially clustered and temporally consistent. The goal is not only to detect the anomaly as quickly as possible (“when”) but also to locate it in space (“where”). The proposed approach works by decomposing the original spatio-temporal data into random natural events, sparse spatially clustered and temporally consistent anomalous events, and random noise. Recursive estimation procedures for spatio-temporal regression are presented to enable the real-time implementation of the proposed methodology. Finally, a likelihood ratio test procedure is proposed to detect when and where the anomaly happens. The proposed approach was applied to the analysis of high-sped video-imaging data to detect and locate local hot-spots during a metal additive manufacturing process.

Keywords:

• Spatio-temporal regression
• video-imaging data
• statistical process monitoring
• in-situ defect detection
• metal additive manufacturing
• laser powder bed fusion

Acknowledgments

The authors would like to thank the Editor, AE and referees for their valuable comments.

Additional information

Funding

The research of Yan is supported by the NSF grants DMS-1830363 and CMMI-1922739. The research of Paynabar is supported by the NSF grants CMMI-1839591. The research of Prof. B.M. Colosimo and Dr. M. Grasso’s research was partially supported by the SIADD Project (Soluzioni Innovative per la qualitá e la sostenibilitá dei processi di ADDitive manufacturing), funded in the framework of the National Operating Programme (PON) “Ricerca e Innovazione 2014-2020”.

Notes on contributors

Hao Yan

Hao Yan received his BS degree in Physics from the Peking University, Beijing, China, in 2011. He also received a MS degree in Statistics, a MS degree in Computational Science and Engineering, and a PhD degree in Industrial Engineering from Georgia Institute of Technology, Atlanta, in 2015, 2016, 2017, respectively. Currently, he is an Assistant Professor in the School of Computing, Informatics, and Decision Systems Engineering at ASU. His research interests focus on developing scalable statistical learning algorithms for large-scale high-dimensional data with complex heterogeneous structures to extract useful information for the purpose of system performance assessment, anomaly detection, intelligent sampling and decision making. Dr. Yan was also recipients of multiple awards including best paper award in IEEE TASE, IISE Transaction and ASQ Brumbaugh Award. Dr. Yan is a member of INFORMS and IIE.

Marco Grasso is Assistant Professor in the Department of Mechanical Engineering of Politecnico di Milano. He got both his MSc in Aerospace Engineering and his PhD in Mechanical Engineering at Politecnico di Milano. The framework of his research consists of statistical process monitoring of manufacturing processes via signal data analysis, statistical learning and data mining techniques. The core of his research is carried out at the AddMe Lab, the laboratory of the Department of Mechanical Engineering focused on Additive Manufacturing technologies, with a focus on in-situ sensing and monitoring of laser and electron beam powder bed fusion processes.

Kamran Paynabar is the Fouts Family Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. He received his BSc and MSc in Industrial Engineering from Iran, and his PhD in IOE and MA in Statistics from The University of Michigan. His research interests comprise both applied and methodological aspects of machine-learning and statistical modeling integrated with engineering principles. He served as the chair of QSR of INFORMS, and the president of QCRE of IISE. He is an Associate Editor for Technometrics and IEEE-TASE, a Department Editor for IISE-Transactions and a member of the editorial board for Journal of Quality Technology.

Bianca Maria Colosimo is Professor in the Department of Mechanical Engineering of Politecnico di Milano, where she is Deputy-Head of the Department. She received her MSc and PhD (cum Laude) in Industrial Engineering from Politecnico di Milano. Her research interest is mainly in the area of complex data modeling monitoring and control, with special attention to surface point clouds, signal, images and video data in advanced manufacturing applications, additive manufacturing among the others. She is Editor-in-Chief of the Journal of Quality Technology, member of the QSR Advisory Board at INFORMS, Council member of ENBIS, member of the Implementation Support Group of the Manufuture-EU, member of the CLC South of the European Institute of Innovation & Technology (EIT) on Manufacturing. She is included among the top 100 Italian woman scientists in STEM – (https://100esperte.it/search?id=170).