Efficient MAPoD via Least Angle Regression based Polynomial Chaos Expansion Metamodel for Eddy Current NDT

Authors

  • Yang Bao College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China
  • Jiahao Qiu College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China
  • Praveen Gurrala Micron Technology, Inc. Boise, ID 83707, USA
  • Jiming Song Department of Electrical and Computer Engineering Iowa State University, Ames, IA 50011, USA

DOI:

https://doi.org/10.13052/2024.ACES.J.390510

Keywords:

Boundary element analysis, eddy current nondestructive testing (NDT), meta learning, model-assisted probability of detection (MAPoD), polynomial chaos expansions with least angle regression (LAR-PCE)

Abstract

In this article, a metamodeling approach based on non-intrusive polynomial chaos expansion (PCE) with least angle regression (LAR) method is used in boundary element analysis for a model-assisted probability of detection (MAPoD) study of eddy current nondestructive testing (NDT) systems. The LAR-PCE metamodel represents the NDT system model responses by a set of coefficients with the polynomial basis functions in lieu of pure kernel degeneration accelerated boundary element method (KD-BEM) based physical model. Both the computational accuracy and efficiency of the LAR-PCE metamodel over the ordinary least squares (OLS) based PCE metamodel are demonstrated by testing the 3D eddy current NDT benchmarks with different system setups, flaw lengths and widths. The simulation results show two digits accuracy of the PoD metrics compared with the ones achieved by the KD-BEM based physical model as the benchmark. The LAR-PCE metamodel has remarkable improvements in computational efficiency over the OLS-PCE metamodel and accelerates the MAPoD study.

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Author Biographies

Yang Bao, College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China

Yang Bao was born in Nanjing, Jiangsu, China. He received the B.S. and M.S. degrees from Nanjing University of Posts and Telecommunications in 2011 and 2014, respectively, and the Ph.D. degree in Electrical Engineering from Iowa State University in 2019. Since 2019, he has been an Assistant Professor at Nanjing University of Posts and Telecommunications. His research interests focus on modeling and simulations of eddy current nondestructive evaluation.

Jiahao Qiu, College of Electronic and Optical Engineering Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China

Jiahao Qiu was born in Huai’an, Jiangsu, China. He received the B.S. degrees from Yancheng Institute of Technology in 2021. He is currently working toward the master’s degree in College of Electronic and Optical Engineering with Nanjing University of Posts and Telecommunications. His current research interests are computational electromagnetics and machine learning.

Praveen Gurrala, Micron Technology, Inc. Boise, ID 83707, USA

Praveen Gurrala received the B.Tech. degree from Indian Institute of Technology Madras in 2014, and the Ph.D. degree from Iowa State University in 2020, both in Electrical Engineering. He is currently a Signal Integrity Engineer at Micron Technology, Inc. His research interests include computational modeling of ultrasonic and eddy current NDE inspections, fast-multipole boundary element methods, EMI/EMC modeling and measurements, and capacitance tomography.

Jiming Song, Department of Electrical and Computer Engineering Iowa State University, Ames, IA 50011, USA

Jiming Song received the Ph.D. degree in Electrical Engineering from Michigan State University in 1993. From 1993 to 2000, he worked as a Postdoctoral Research Associate, a Research Scientist and Visiting Assistant Professor at the University of Illinois at Urbana-Champaign. From 1996 to 2000, he worked part-time as a Research Scientist at SAIC-DEMACO. Dr. Song was the principal author of the Fast Illinois Solver Code (FISC). He was a Principal Staff Engineer/Scientist at Semiconductor Products Sector of Motorola in Tempe, Arizona, before he joined Department of Electrical and Computer Engineering at Iowa State University as an Assistant Professor in 2002.

Dr. Song currently is a Professor at Iowa State University’s Department of Electrical and Computer Engineering. His research has dealt with modeling and simulations of electromagnetic, acoustic and elastic wave propagation, scattering, and non-destructive evaluation, electromagnetic wave propagation in metamaterials and periodic structures and applications, interconnects on lossy silicon and radio frequency components, antenna radiation and electromagnetic wave scattering using fast algorithms, and transient electromagnetic fields. He received the NSF Career Award in 2006 and is an IEEE Fellow and ACES Fellow.

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Published

2024-05-31

How to Cite

[1]
Y. Bao, J. Qiu, P. Gurrala, and J. Song, “Efficient MAPoD via Least Angle Regression based Polynomial Chaos Expansion Metamodel for Eddy Current NDT”, ACES Journal, vol. 39, no. 05, pp. 461–469, May 2024.

Issue

2024: Vol 39 Iss 5

Section

General Submission

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