Hierarchical spatial-temporal modeling and monitoring of melt pool evolution in laser-based additive manufacturing

Abstract

Melt pool dynamics reflect the formulation of microstructural defects in parts during Laser-Based Additive Manufacturing (LBAM). The thermal images of the melt pool collected during the LBAM process provide unique opportunities for modeling and monitoring its evolution. The recognized anomalies are evidence of part defects that are to be eliminated for higher product quality. A unique concern in analyzing thermal images is spatial-temporal correlations – the heat transfer within the melt pool causes spatial correlations among pixels in an image, and the evolution of the melt pool causes temporal correlations across images. The objective of this study is to develop a LBAM modeling-monitoring framework that incorporates spatial-temporal effects in characterizing and monitoring melt pool behavior. Spatial-Temporal Conditional Autoregressive (STCAR) models are explored. STCAR-AR is identified as the best candidate among the numerous STCAR variants. A novel two-level control chart is constructed on top of the STCAR-AR model to monitor the melt pool dynamics. A hierarchical structure underlies the two-level control chart in the sense that global anomalies recognized in Level II can be traced in Level I for further inspection. A comparison with other recently developed in-situ monitoring approaches shows that the proposed framework achieves the best detection power and false positive rate.

Keywords:

• Additive manufacturing
• hierarchical control charts
• spatial-temporal conditional autoregressive model

Notes on contributors

Shenghan Guo is a Ph.D. candidate in the Department of Industrial & Systems Engineering at Rutgers University. She received a B.S. degree in Financial Engineering from Jilin University, an M.S. degree in Financial Mathematics from Johns Hopkins University and an M.S. degree in Engineering Sciences & Applied Mathematics from Northwestern University. Her research interests include statistical process control, Big Data analytics and financial mathematics. Her current research focuses on developing innovative data mining approaches to exploit the value of manufacturing big data in guiding in-situ process monitoring and product quality control. She is the recipient of the 2019 Tayfur Altiok Scholarship at the Department of Industrial & Systems Engineering at Rutgers, a finalist in the University and Louis Bevier Dissertation Completion Fellowship, and the winner of IISE Quality Control and Reliability Engineering (QCRE) Division’s Data Challenge at the 2019 IISE Annual Conference.

Weihong “Grace” Guo is an Assistant Professor in the Department of Industrial & Systems Engineering at Rutgers University. She received a B.S. degree in Industrial Engineering from Tsinghua University, an M.S. degree and a Ph.D. in Industrial & Operations Engineering from the University of Michigan, Ann Arbor. Her research focuses on developing novel methodologies for extracting and analyzing massive and complex data to facilitate effective monitoring of operational quality, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. She received the Barbara M. Fossum Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers in 2019. Dr. Guo served as the President of the IISE Process Industries Division in 2016-2018.

Linkan Bian, Ph.D., is the Thomas B. & Terri L. Nusz Associate Professor in Industrial and Systems Engineering Department at Mississippi State University. He received his Ph.D. in Industrial and Systems Engineering from Georgia Institute of Technology in 2013 and a B.S. degree in Applied Mathematics from Beijing University. Dr. Bian research interests focus on the analytics of Big Data generated from complex engineering systems. Methodology of his research includes areas such as, data mining, surrogate modeling, statistics optimization, and uncertainty quantification. His research has been applied to areas including additive manufacturing, reliability/maintenance, supply chains, cybersecurity, and other engineering systems. He has received federal funding from NSF, NIH, DoD, and DoE, as well as industrial companies. Dr. Bian has published over 50 peer-reviewed papers that appear in prestigious journals such as IISE Transactions, ASME Journals, Additive Manufacturing, Rapid Prototyping, IEEE Transactions, and other journals. Dr. Bian’s work has been widely recognized in the IISE professional communities. He received the Outstanding Young Investigator Award from the IISE Manufacturing and Design division, as well as multiple Best Paper Awards from IISE.

Additional information

Funding

This research was partially sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-15-2-0025. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.