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- W4381199747 abstract "The objective of the present experimental work is to analyze and identify the damage mechanisms on various composite materials. The present paper deals with different types of composites that were subjected to static tensile tests in order to investigate their damage mechanisms. A progressive approach consisting of working on different, unidirectional composites with different fiber orientations. The specimens were tested under static tensile tests. All experimental tests carried out were monitored by acoustic emission (AE): a non-destructive technique to detect the appearance and identify the evolution of major damage mechanisms. An unsupervised clustering technique, based on pattern recognition and widely used in machine learning (ML), the k-means clustering technique, was used to classify the AE recorded events based on a temporal parameter such as amplitude range, the cumulative number of hits, duration, and the acoustic energy. Four classes of AE events were identified for most materials depending on the specimen configuration. A comparison was carried out between AE data sets processed using machine learning based clustering (MLC) method and commercial software." @default.
- W4381199747 created "2023-06-20" @default.
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- W4381199747 date "2023-01-01" @default.
- W4381199747 modified "2023-09-27" @default.
- W4381199747 title "Non-destructive Identification of Damage Mechanisms in Unidirectional Composites by Acoustic Emission and Machine Learning-Based Clustering" @default.
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- W4381199747 doi "https://doi.org/10.1007/978-3-031-34190-8_8" @default.
- W4381199747 hasPublicationYear "2023" @default.
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