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ABSTR ACT This paper presents a novel data-driven approach to localize two types of acoustic emission sources in an aluminum plate, namely a Hsu-Nielsen source, which simulates a crack-like source, and steel ball impacts of varying diameters acting as the impact source. While deep neural networks have shown promise in previous studies, achieving high accuracy requires a large amount of training data, which may not always be feasible. To address this challenge, we investigated the applicability of transfer learning to address the issue of limited training data. Our approach involves transferring knowledge learned from numerical modeling to the experimental domain to localize nine different source locations. In the process, we evaluated six deep learning architectures using tenfold cross- validation and demonstrated the potential of transfer learning for efficient acoustic emission source localization, even with limited experimental data. This study contributes to the growing demand for running deep learning models with limited capacity and training time and highlights the promise of transfer learning methods such as fine-tuning pretrained models on large semi-related datasets. KEYWORDS: acoustic emission, deep neural network, finite element modeling, transfer learning, fiber optics, source localization Introduction Acoustic emission source localization is crucial in struc- tural health monitoring (SHM) and proactive maintenance of metallic structures. The constraints in deploying acoustic emission testing (AE) sensor arrays in real-world structures necessitate a shift toward intelligent, automated single-sensor approaches. Holford et al. (2001) pioneered the application of AE for damage location in steel bridges, establishing its impor- tance in SHM. Ebrahimkhanlou and Salamone (2017) further examined acoustic source localization and its significance in determining the origin of acoustic emission waves and assess- ing damage severity. Cheng et al. (2021) developed an acoustic emission source localization method using Lamb wave propa- gation simulation and artificial neural networks, proving effec- tive in I-shaped steel girder inspections. Ai et al. (2021) studied source localization on large-scale canisters used for nuclear fuel storage, addressing the need for optimal AE sensor deploy- ment. Ciampa and Meo (2010) proposed an approach using wavelet analysis and a Newton-based optimization technique for acoustic emission source localization and velocity determi- nation, contributing to the broader understanding of acoustic emission wave propagation and source detection. Significant progress has been achieved in acoustic emission source localization through the application of deep learning, demonstrating its promise in localizing acoustic emission signals (LeCun et al. 2015). Ebrahimkhanlou and Salamone (2018) proposed a deep learning approach for localizing acoustic emission sources using a single sensor in plate-like structures. This was further advanced by Ebrahimkhanlou et al. (2019), who introduced a deep learning–based framework for localizing and characterizing acoustic emission sources in metallic panels using only one sensor. Garrett et al. (2022) utilized artificial intelligence for estimating fatigue crack length from acoustic emission waves, a significant step forward in damage localization and quantification. Despite the challenge of false positives, the fusion of artificial intelligence and AE holds promising opportunities for enhancing SHM (Verstrynge et al. 2021 Hassan et al. 2021). A key challenge in using supervised learning algorithms for acoustic emission source localization is the difficulty in accessing labeled acoustic emission signals for existing struc- tures. Transfer learning is a strategy that assists the super- vised learning task when available training data is limited ACOUSTIC EMISSION SOURCE LOCALIZATION USING DEEP TRANSFER LEARNING AND FINITE ELEMENT MODELING– BASED KNOWLEDGE TRANSFER XUHUI HUANG*, OBAID ELSHAFIEY*, KARIM FARZIA†, LALITA UDPA*, MING HAN*, AND YIMING DENG*‡ *Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI † Nikon Inc., 9453 Innovation Dr., Manassas, VA ‡ Corresponding author: dengyimi@egr.msu.edu Materials Evaluation 81 (7): 71–84 https://doi.org/10.32548/2023.me-04348 ©2023 American Society for Nondestructive Testing NDTTECHPAPER |ME J U L Y 2 0 2 3 • M A T E R I A L S E V A L U A T I O N 71 2307 ME July dup.indd 71 6/19/23 3:41 PM