Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables

Abstract

Buckypapers are thin sheets produced from Carbon NanoTubes (CNTs) that effectively transfer the exceptional mechanical properties of CNTs to bulk materials. To accomplish a sensible tradeoff between effectiveness and efficiency in predicting the mechanical properties of CNT buckypapers, a multi-fidelity analysis appears necessary, combining costly but high-fidelity physical experiment outputs with affordable but low-fidelity Finite Element Analysis (FEA)-based simulation responses. Unlike the existing multi-fidelity analysis reported in the literature, not all of the input variables in the FEA simulation code are observable in the physical experiments; the unobservable ones are the latent variables in our multi-fidelity analysis. This article presents a formulation for multi-fidelity analysis problems involving latent variables and further develops a solution procedure based on nonlinear optimization. In a broad sense, this latent variable-involved multi-fidelity analysis falls under the category of non-isometric matching problems. The performance of the proposed method is compared with both a single-fidelity analysis and the existing multi-fidelity analysis without considering latent variables, and the superiority of the new method is demonstrated, especially when we perform extrapolation.

Keywords:

• Buckypaper
• carbon nanotubes
• latent variables
• Gaussian processes
• multi-fidelity analysis

Additional information

Notes on contributors

Arash Pourhabib

Arash Pourhabib received his B.S. in Industrial Engineering from Sharif University of Technology in 2008, and his Ph.D. from the Department of Industrial and Systems Engineering at Texas A&M University in 2014. He is currently an Assistant Professor at the School of Industrial Engineering and Management at Oklahoma State University. His research interests are in the areas of system informatics and control and statistical machine learning. He is a member of INFORMS and IIE.

Jianhua Z. Huang

Jianhua Huang received his B.S. degree in 1989 and M.S. degree in 1992, both in Probability and Statistics, from Beijing University, China, and his Ph.D. in Statistics from the University of California, Berkeley, in 1997. He is currently a Professor of Statistics at Texas A&M University. His research interests include computational statistics, semi- and non-parametric statistical methods, statistical machine learning, and applied statistics. He is a fellow of ASA and IMS.

Kan Wang

Dr. Kan Wang is a Post-Doctoral Researcher in the Georgia Tech Manufacturing Institute and H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He received his Ph.D. (2013) in Industrial & Manufacturing Engineering from the Florida State University. After his graduation, he worked as a major researcher in several projects sponsored by various organizations and industrial companies including National Science Foundation, Air Force Office of Scientific Research, Veterans Affairs, ATK, and Genesis Engineering Solutions. His research fields include computational modeling and simulation of manufacturing processes, additive manufacturing, and nanomaterials and nanomanufacturing.

Chuck Zhang

Chuck Zhang is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is also an affiliated faculty member of the Georgia Tech Manufacturing Institute. He received his Ph.D. (1993) in Industrial Engineering from the University of Iowa and was on the faculty of the Industrial & Manufacturing Engineering Department at Florida A&M University–Florida State University College of Engineering prior to joining Georgia Tech. His research interests include advanced composites/nanocomposite materials manufacturing, integrated computational materials engineering, additive manufacturing/printed electronics, and geometric tolerancing and metrology. His research projects have been sponsored by a number of organizations including the Air Force Office of Scientific Research, the Army Research Laboratory, the National Institute of Standards and Technology, the National Science Foundation, the Office of Naval Research, the Society of Manufacturing Engineers, and Veterans Affairs, as well as industrial companies such as ATK, Cummins, General Dynamics, Lockheed Martin, and Siemens Power Generation. He has published over 140 refereed journal articles and 180 conference papers. He also holds 15 U.S. patents.

Ben Wang

Dr. Ben Wang is the Chief Manufacturing Officer of the Georgia Institute of Technology and Executive Director of the Georgia Tech Manufacturing Institute. He is a Professor and Gwaltney Chair in Manufacturing Systems in the School of Industrial and Systems Engineering and Professor in the School of Materials Science and Engineering. He serves on the National Materials and Manufacturing Board, National Research Council of the National Academies. He is a Fellow of the Institute of Industrial Engineers, Society of Manufacturing Engineers, and Society for the Advancement of Materials and Process Engineering. He has published more than 220 papers in refereed journals and is a co-inventor on 25 patents or patent applications. With a primary research interest in applying emerging technologies to improve manufacturing competitiveness, he specializes in process development for affordable composite materials and is widely acknowledged as a pioneer in the growing field of nano-materials. Currently, he focuses his research on high-performance and affordable composites, which is already changing product innovations worldwide. His attention to applications of the integrated product–process design approach toward substituting metal structures with low-cost, high-performance composite materials is unique among researchers in these difficult, yet most promising, investigations.

Yu Ding

Yu Ding received a B.S. in Precision Engineering from the University of Science and Technology of China in 1993; an M.S. in Precision Instruments from Tsinghua University, China, in 1996; an M.S. in Mechanical Engineering from Pennsylvania State University in 1998; and a Ph.D. in Mechanical Engineering from the University of Michigan in 2001. He is currently the Mike and Sugar Barnes Professor of Industrial and Systems Engineering and Professor of Electrical and Computer Engineering at Texas A&M University. His research interests are in the area of systems informatics and quality and reliability engineering. He currently serves as a Department Editor of IIE Transactions. He is a member of IIE, a senior member of IEEE, and a member of INFORMS, ASQ, and ASME.