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Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites

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Innovation in Research & Engineering Solutions (IRES), Boulevard Edmond Machtens 79/22, 1080 Brussels, Belgium
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RNANO Lab—Research Unit of Advanced, Composite, Nano Materials and Nanotechnology, School of Chemical Engineering, National Technical University of Athens, GR-15773 Zographos, Greece
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Author to whom correspondence should be addressed.
Fibers 2020, 8(1), 3; https://doi.org/10.3390/fib8010003
Received: 13 November 2019 / Revised: 16 December 2019 / Accepted: 18 December 2019 / Published: 21 December 2019
Carbon fiber reinforced polymers (CFRPs) are continuously gaining attention in aerospace and space applications, and especially their multi-scale reinforcement with nanoadditives. Carbon nanotubes (CNTs), graphene, carbon nanofibers (CNFs), and their functionalized forms are often incorporated into interactive systems to engage specific changes in the environment of application to a smart response. Structural integrity of these nanoscale reinforced composites is assessed with advanced characterization techniques, with the most prominent being nanoindentation testing. Nanoindentation is a well-established technique, which enables quantitative mapping of nanomechanical properties with the μm surficial and nm indentation resolution scale and high precision characterization. This feature enables the characterization of the interface in a statistical and quantitative manner and the correlation of (nano-) reinforcement to interface properties of CFRPs. Identification of reinforcement is performed with k-Nearest Neighbors and Support Vector Machine classification algorithms. Expertise is necessary to describe the physical problem and create representative training/testing datasets. Development of open source Machine Learning algorithms can have an influential impact on uniformity of nanometry data creation and management. The statistical character of nanoindentation is a key factor to supply information on heterogeneity of multiscale reinforced composites. Both the identification of (nano-) reinforcement and quality assessment of composites are provided by involving artificial intelligence. View Full-Text
Keywords: artificial intelligence; machine learning; multiclass classification; clustering; nanoindentation; carbon fiber reinforced composites; carbon nanotubes; interface artificial intelligence; machine learning; multiclass classification; clustering; nanoindentation; carbon fiber reinforced composites; carbon nanotubes; interface
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Koumoulos, E.; Konstantopoulos, G.; Charitidis, C. Applying Machine Learning to Nanoindentation Data of (Nano-) Enhanced Composites. Fibers 2020, 8, 3.

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