Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring
Abstract
:1. Introduction
- A finite element method (FEM)-based model of SLJs for properly modeling weak adhesion is developed.
- An approach to validate the numerical model of SLJs subjected to weak adhesion using a small set of experimental data and machine learning algorithms is derived.
- A machine learning algorithm for classifying weak adhesion based on simulated LWs data and neural networks is proposed, exhibiting remarkable potential in precisely predicting the extent of weak adhesion, and being robust even when the defect’s location within the joint is altered.
2. Numerical Model Development and Experimental Validation
2.1. Experimental Setup
2.1.1. Creating Weak Adhesion
2.1.2. Experimental Testing
2.2. Numerical Model
2.2.1. Lamb Waves Simulation
2.2.2. Weak Adhesion Simulation
2.3. Numerical Model Validation
2.3.1. Data Processing
2.3.2. Machine Learning
- A few experimental cases constantly have bad results. This is probably because the tests had some form of inconsistency during the experimental setup and thus can be considered faulty.
- The average values are always lower when simulated values are used, showing their reliability as a form of simple data augmentation.
- The validation cases cannot be correctly determined when testing and training are done only with simulated values, showing that there is still a need for a few experimental cases to create a correlation between the experimental and simulated data.
3. Weak Adhesion Detection and Classification
3.1. Simulation Data
3.2. Machine Learning Application
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
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U1 | U2 | U3 | U1 + U2 | U1 + U2 + U3 | |
---|---|---|---|---|---|
Case 1671 N | 464.5 | 227.1 | 406.4 | 406.2 | 461.8 |
Case 1902 N | 169.8 | 166.2 | 166.5 | 169.0 | 161.4 |
Case 2465 N | 147.7 | 161.2 | 130.7 | 159.4 | 130.1 |
Case 2587 N | 137.0 | 173.4 | 144.7 | 163.2 | 195.7 |
Case 3192 N | 225.3 | 199.7 | 163.6 | 194.4 | 156.1 |
Case 4562 N | 205.9 | 221.8 | 140.1 | 201.3 | 197.9 |
Case 5291 N | 327.6 | 286.0 | 332.8 | 280.4 | 279.6 |
Case 5670 N | 250.2 | 308.3 | 299.5 | 245.0 | 246.6 |
Case 6678 N | 415.1 | 331.9 | 349.7 | 413.7 | 415.7 |
Case 6680 N | 347.4 | 303.1 | 317.6 | 379.2 | 324.9 |
Case 7301 N | 224.3 | 267.5 | 439.4 | 325.5 | 220.8 |
Average | 265.0 | 240.6 | 262.8 | 267.0 | 253.7 |
77 Exp, 15% Noise | 22 Exp, 15% Noise | 22 Exp, 5% Noise | 49 Exp, 5% Noise | |||||
---|---|---|---|---|---|---|---|---|
Simu + Exp | Exp | Sim + Exp | Exp | Sim + Exp | Exp | Sim + Exp | Exp | |
Case 1671 N | 288.5 | 465.2 | 470.0 | 462.8 | 415.6 | 450.9 | 470.3 | 468.5 |
Case 1902 N | 205.4 | 144.3 | 235.2 | 288.7 | 272.9 | 306.8 | 228.6 | 169.9 |
Case 2465 N | 42.3 | 134.2 | 94.3 | 256.3 | 100.4 | 259.0 | 82.0 | 140.7 |
Case 2587 N | 215.2 | 154.7 | 177.6 | 288.9 | 214.5 | 276.5 | 224.7 | 200.4 |
Case 3192 N | 24.9 | 146.6 | 134.7 | 283.9 | 76.6 | 266.2 | 136.7 | 241.1 |
Case 4562 N | 197.7 | 211.8 | 176.2 | 31.3 | 209.7 | 127.5 | 185.0 | 141.1 |
Case 5291 N | 350.8 | 161.0 | 299.5 | 187.1 | 279.3 | 195.9 | 232.1 | 162.6 |
Case 5670 N | 253.8 | 253.1 | 163.3 | 99.1 | 65.0 | 292.5 | 163.1 | 247.9 |
Case 6678 N | 427.0 | 420.2 | 321.3 | 188.9 | 312.7 | 184.3 | 373.9 | 427.9 |
Case 6680 N | 195.4 | 417.3 | 203.0 | 188.5 | 181.8 | 284.6 | 185.2 | 355.8 |
Case 7301 N | 313.6 | 328.4 | 500.2 | 326.1 | 163.0 | 333.2 | 355.9 | 357.9 |
Average | 228.6 | 257.9 | 252.3 | 236.5 | 208.3 | 270.7 | 239.8 | 264.9 |
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Ramalho, G.M.F.; Lopes, A.M.; da Silva, L.F.M. Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring. Appl. Sci. 2023, 13, 10877. https://doi.org/10.3390/app131910877
Ramalho GMF, Lopes AM, da Silva LFM. Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring. Applied Sciences. 2023; 13(19):10877. https://doi.org/10.3390/app131910877
Chicago/Turabian StyleRamalho, Gabriel M. F., António M. Lopes, and Lucas F. M. da Silva. 2023. "Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring" Applied Sciences 13, no. 19: 10877. https://doi.org/10.3390/app131910877
APA StyleRamalho, G. M. F., Lopes, A. M., & da Silva, L. F. M. (2023). Assessing Weak Adhesion in Single Lap Joints Using Lamb Waves and Machine Learning Methods for Structural Health Monitoring. Applied Sciences, 13(19), 10877. https://doi.org/10.3390/app131910877