Dynamic Fracture Toughness Prediction of Fiber/Epoxy Composites Using K-Nearest Neighbor (KNN) Method

Sharma, Aanchna; Madhushri, Priyanka; Kushvaha, Vinod

doi:10.1007/978-981-19-3603-6_6

Aanchna Sharma⁵,
Priyanka Madhushri⁶ &
Vinod Kushvaha⁵

1031 Accesses

Abstract

Geometrical features like size and shape of the particles which are used to reinforce the composites affect the mechanical behavior of the resulting particulate polymer composites to a great extent. The aspect ratio of the reinforcing filler is of great importance specially when such composites are subjected to impact loading. Usually, an increase in the aspect ratio results in a significant increase in the energy-absorbing ability which ultimately improves the fracture toughness of the resulting composite. However, the experimental procedure followed for determining the fracture toughness of polymer composites reinforced with particles of varying aspect ratio is very complex and time-consuming. In this view, this chapter investigates the applicability of a machine learning algorithm known as K-nearest neighbor (KNN) for determining the dynamic fracture toughness of glass-filled polymer composites. The proposed methodology aims to predict the fracture toughness in terms of stress intensity factor with limited experimentation and maximum accuracy. The current framework of machine learning utilizes time, dynamic elastic modulus, aspect ratio, and volume fraction of the glass particles as the independent model parameters. The proposed KNN model predicts the fracture behavior of these composites with an accuracy of ~96%.

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Department of Civil Engineering, Indian Institute of Technology Jammu, Jammu, India
Aanchna Sharma & Vinod Kushvaha
Stanley Black and Decker, Atlanta, GA, USA
Priyanka Madhushri

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Aanchna Sharma
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Priyanka Madhushri
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Department of Materials and Production Engineering, The Sirindhorn International Thai German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangsue, Bangkok, Thailand
Sanjay Mavinkere Rangappa
Division of Chemistry Department of Science Faculty of Science & Technology, Alliance University, Bengaluru,, Karnataka, India
Jyotishkumar Parameswaranpillai
Department of Materials and Production Engineering, The Sirindhorn International Thai German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangsue, Bangkok, Thailand
Suchart Siengchin
International and Inter University Centre for Nanoscience and Nanotechnology (IIUCNN), School of Energy Materials Mahatma Gandhi University, Kottayam, Kerala, India
Sabu Thomas

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Sharma, A., Madhushri, P., Kushvaha, V. (2022). Dynamic Fracture Toughness Prediction of Fiber/Epoxy Composites Using K-Nearest Neighbor (KNN) Method. In: Mavinkere Rangappa, S., Parameswaranpillai, J., Siengchin, S., Thomas, S. (eds) Handbook of Epoxy/Fiber Composites . Springer, Singapore. https://doi.org/10.1007/978-981-19-3603-6_6

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DOI: https://doi.org/10.1007/978-981-19-3603-6_6
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