ArticleMaterials (Basel, Switzerland)2022
Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials.
Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Prediction of Damage Distribution in Gas Cylinder Stages Based on Semi-Supervised and Transfer Learning Algorithms.Sensors (Basel, Switzerland) · 2026Article
- Acoustic deep learning for defect detection in aluminium wheel rims.Scientific reports · 2026Article
- Acoustic Emission Mechanisms During Polymer Processing and Chain Orientation: From Amorphous to Crystalline.Polymers · 2025Review
- Recent Trends in Non-Destructive Testing Approaches for Composite Materials: A Review of Successful Implementations.Materials (Basel, Switzerland) · 2025Review
- Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review.Materials (Basel, Switzerland) · 2025Review
- Application of Object Detection Algorithms in Non-Destructive Testing of Pressure Equipment: A Review.Sensors (Basel, Switzerland) · 2024Review
- Effects of Seawater on Mechanical Performance of Composite Sandwich Structures: A Machine Learning Framework.Materials (Basel, Switzerland) · 2024Article
- Study of the Effect of NaOH Treatment on the Properties of GF/VER Composites Using AE Technique.Materials (Basel, Switzerland) · 2024Article
- Acoustic-Signal-Based Damage Detection of Wind Turbine Blades-A Review.Sensors (Basel, Switzerland) · 2023Review
- Article
- Effects of High Temperatures on the Performance of Carbon Fiber Reinforced Polymer (CFRP) Composite Cables Protected with Fire-Retardant Materials.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials. Raw AE time series and frequency-domain sequence data are used as the input for the InceptionTime network, and both obtain very high classification performances, achieving high accuracy scores of about 99%. The InceptionTime network produces better training, validation, and test accuracy with the raw AE time series data than it does with the frequency-domain sequence data. Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.
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