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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Classification of mechanism of reinforcement in the fiber-matrix interface: Application of Machine Learning on nanoindentation data
ترجمه فارسی عنوان مقاله:
طبقه بندی مکانیسم تقویتی در رابط فیبر ماتریس: کاربرد یادگیری ماشین بر روی داده های nanoindentation
منبع:
Sciencedirect - Elsevier - Materials and Design 192 (2020) 108705
نویسنده:
Georgios Konstantopoulos a, Elias P. Koumoulos a,b,⁎, Costas A. Charitidis a
چکیده انگلیسی:
Carbon fiber reinforced polymer manufacturing is emerging, with multiple studies to focus on the design of interfacial
reinforcement to ensure the maximum of composite properties, but also respectively to be able to
align with zero defect manufacturing. The controversy on the engineering approach is a data-driven task that
can be efficiently tackled by involving Artificial Intelligence in order to establish unbiased structure-property relations.
In the present study, nanoindentation mapping datawere processedwithMachine Learning classification
models to identify the interfacial reinforcement. The data preparation included normalization and sorting out of
highly similar datawith k-means clustering, since nanoindentation on epoxy matrix does not enhance insight on
the mechanism of reinforcement. The trained models included neural networks, classification trees, and support
vector machines. Realization of models performance was evaluated on the test dataset as screening to obtain
best fitted models for each algorithm. Transfer learning potential was demonstrated by extrapolating the prediction
of best trained models to a validation dataset at different indentation depth with support vector machines
outperforming the othermodels. Overall accuracywas 67% on the test dataset, F1 Score was 65% in the prediction
of reinforcement mechanism classes and 72% in case of pristine specimen, while accuracy on validation dataset
was 72.7%. Prediction metrics were comparable to other case studies of real-world classification problems. Computational
time-cost for tuning and training was sustainable and equal to 2.3 min.
Keywords: Artificial intelligence | Machine Learning | Nanoindentation | Interface | Carbon fiber reinforced composites | Multiclass classification
قیمت: رایگان
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