Supervised classification model for estimation of wear in sisal fibre-epoxy composites

Gupta, Preeti; Dwivedi, Umesh Kumar; Yadav, Vaishali; Yadav, Ashwani Kumar

doi:10.1016/j.matpr.2022.07.176

Cited by 1 publication

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“…The large dataset of mechanical properties of flax fiber/shape memory epoxy hygromorph composites used in this work was obtained from the study conducted by Li et al [24]. The dataset can be accessed at the Mendeley Data repository located at [40]. In their study, flax fiber/shape memory epoxy hygromorph composites were mechanically deformed with consideration of two fiber orientations (0 • and 90 • ) at five temperatures (20 • C, 40 • C, 60 • C, 80 • C, and 100 • C) and two humidity conditions (50% and fully immersed).…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites

Sadat

2023

Applied Mechanics

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Flax fiber/shape memory epoxy hygromorph composites are a promising area of research in the field of biocomposites. This paper focuses on the tensile modulus of these composites and investigates how it is affected by factors such as fiber orientation (0° and 90°), temperature (20 °C, 40 °C, 60 °C, 80 °C, and 100 °C), and humidity (50% and fully immersed) conditions. Machine learning algorithms were utilized to predict the tensile modulus based on non-linearly dependent initial variables. Both decision tree (DT) and random forest (RF) algorithms were employed to analyze the data, and the results showed high coefficient of determination R2 values of 0.94 and 0.95, respectively. These findings demonstrate the effectiveness of machine learning in analyzing large datasets of mechanical properties in biocomposites. Moreover, the study revealed that the orientation of the flax fibers had the greatest impact on the tensile modulus value (with feature importance of 0.598 and 0.605 for the DT and RF models, respectively), indicating that it is a crucial factor to consider when designing these materials.

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Section: Methodsmentioning

confidence: 99%

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites

Sadat

2023

Applied Mechanics

View full text Add to dashboard Cite

show abstract

Supervised classification model for estimation of wear in sisal fibre-epoxy composites

Cited by 1 publication

References 13 publications

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites

Machine Learning-Assisted Tensile Modulus Prediction for Flax Fiber/Shape Memory Epoxy Hygromorph Composites

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