Identifying Weak Adhesion in Single-Lap Joints Using Lamb Wave Data and Artificial Intelligence Algorithms

Author:

Ramalho Gabriel M. F.1ORCID,M. Lopes António12ORCID,Carbas Ricardo J. C.12ORCID,Da Silva Lucas F. M.12ORCID

Affiliation:

1. Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal

2. INEGI—Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal

Abstract

In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive testing techniques to detect weak adhesion. These are based on Lamb wave (LW) data and artificial intelligence algorithms. A dataset consisting of simulated LW time series extracted from single-lap joints (SLJs) subjected to multiple levels of weak adhesion was generated. The raw time series were pre-processed to avoid numerical saturation and to remove outliers. The processed data were then used as the input to different artificial intelligence algorithms, namely feedforward neural networks (FNNs), long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs), for their training and testing. The results showed that all algorithms were capable of detecting up to 20 different levels of weak adhesion in SLJs, with an overall accuracy between 97% and 99%. Regarding the training time, the FNN emerged as the most-appropriate. On the other hand, the GRU showed overall faster learning, being able to converge in less than 50 epochs. Therefore, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them the most-suitable choices. The proposed approach constitutes a new framework allowing the creation of standardized data and optimal algorithm selection for further work on nondestructive damage detection and localization in adhesive joints.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference42 articles.

1. Prediction of crack initiation and propagation of adhesive lap joints using an energy failure criterion;Chen;Eng. Fract. Mech.,2011

2. da Silva, L.F.M., Öchsner, A., and Adams, R.D. (2018). Handbook of Adhesion Technology, Springer. [2nd ed.].

3. Strength of double-reinforced adhesive joints;Adin;Mater. Test.,2021

4. Structural health monitoring of adhesive joints using Lamb waves: A review;Ramalho;Struct. Control. Health Monit.,2021

5. U.S. National Archives and Records Administration (2014). Federal Aviation Regulations—Sec. 23.573.

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