Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials
Abstract
:1. Introduction
2. Experiment Procedures and Methods
2.1. Material Preparation and Test Procedure
2.2. AE Data Acquisition Device
2.3. Experimental Data and AE Datasets
2.4. InceptionTime Model
2.5. Methodology for InceptionTime Model-Based Damage Classification
3. Results and Discussion
3.1. Train
3.1.1. Raw AE Time Series Data
3.1.2. Frequency-Domain Sequence Data
3.2. Evaluation Metrics
3.3. Comparative Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Average Value |
---|---|
Tensile strength/GPa | 4.9 |
Tensile modulus/GPa | 230 |
Elongation % | 2.10 |
Density/kg/m3 | 1800 |
Poisson’s ratio | 0.307 |
Diameter/μm | 7 |
Parameters | Average Value |
---|---|
Tensile strength/MPa | 75 |
Tensile modulus/GPa | 3 |
Elongation % | 0.2 |
Density/(kg/m3) | 980 |
Poisson’s ratio | 0.38 |
Parameter | Setting Value |
---|---|
Threshold/dB | 35 |
Sampling rate/MSPS | 1 |
Pre-trigger time/μs | 256 |
Peak definition time (PDT)/μs | 100 |
Hit definition time (HDT)/μs | 200 |
Hit locking time (HLT)/μs | 400 |
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Guo, F.; Li, W.; Jiang, P.; Chen, F.; Liu, Y. Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials. Materials 2022, 15, 4270. https://doi.org/10.3390/ma15124270
Guo F, Li W, Jiang P, Chen F, Liu Y. Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials. Materials. 2022; 15(12):4270. https://doi.org/10.3390/ma15124270
Chicago/Turabian StyleGuo, Fuping, Wei Li, Peng Jiang, Falin Chen, and Yinghonglin Liu. 2022. "Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials" Materials 15, no. 12: 4270. https://doi.org/10.3390/ma15124270