Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach
| Autor: | Atwya, M., Panoutsos, G. |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Materials science
Artificial neural network business.industry 020208 electrical & electronic engineering Feature extraction 02 engineering and technology Welding Machine learning computer.software_genre Signal Computer Science Applications law.invention Control and Systems Engineering law Multilayer perceptron Thermography 0202 electrical engineering electronic engineering information engineering Friction stir welding Artificial intelligence Transient (oscillation) Electrical and Electronic Engineering business computer Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 16:4423-4435 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2019.2948023 |
| Popis: | A systematic computational method to simulate and detect sub-surface flaws, through non-destructive transient thermography, in aluminium sheets and friction stir welded sheets is proposed. The proposed method relies on feature extraction methods and a data driven machine learning modelling structure. In this work, we propose the use of a multi-layer perceptron feed-forward neural-network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose Thermographic Signal Linear Modelling (TSLM), a hyper-parameterfree feature extraction technique for transient thermography. The new feature extraction and modelling framework was tested with out-of-sample experimental transient thermography data and results show effectiveness in sub-surface flaw detection of up to 2.3 mm deep in aluminium sheets (99.8 % true positive rate, 92.1 % true negative rate) and up to 2.2 mm deep in friction stir welds (97.2 % true positive rate, 87.8 % true negative rate). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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