Machine learning for impact detection on composite structures
| Autor: | Mario Emanuele De Simone, Michele Meo, Christos Andreades, Francesco Ciampa, Stefano Cuomo |
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| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Dependency (UML) Cross-correlation Computer science business.industry Composite number Process (computing) 02 engineering and technology Impact test 021001 nanoscience & nanotechnology Machine learning computer.software_genre 01 natural sciences 0103 physical sciences Radial basis function Artificial intelligence 0210 nano-technology Baseline (configuration management) business computer Interpolation |
| Zdroj: | Materials Today: Proceedings. 34:93-98 |
| ISSN: | 2214-7853 |
| DOI: | 10.1016/j.matpr.2020.01.295 |
| Popis: | In order to overcome the current limitations of the impact localisation process in composite materials, such as the a-priori knowledge of the mechanical properties and the direction dependency of the wave speed, a novel method is here proposed based on the machine learning approach. The algorithm is formed by two steps: the first is the training process, in which a baseline consisting of the structural responses due to impact tests is acquired; the second one evaluates the impact location exploiting the highest cross-correlation coefficient, obtained after the interpolation of the impact response baseline using the Radial Basis Function (RBF) method. Numerous experimental tests are performed on a simple carbon fibre reinforced polymer (CFRP) plate fitted with three piezo-sensors at three different drop heights to validate the training process. The results showed high accuracy in both the reconstruction and the impact localisation, with an error less than 10 mm. |
| Databáze: | OpenAIRE |
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