Characterization of local mechanical properties of Al/Cu Magnetic Pulse Welded joints under high strain rates using synchrotron X-ray imaging

Zielinski, Benjamin; Sadat, Tarik; Lukić, Bratislav; Haugou, Grégory; Morvan, Hervé; Rack, Alexander; Markiewicz, Éric; Dubar, Laurent

doi:10.1016/j.matlet.2023.133943

Cited by 6 publications

(1 citation statement)

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“…In addition, exploring alternative machine learning algorithms could be beneficial to determine whether they yield more accurate predictions or provide deeper insights into the behavior of biocomposites. It would also be worthwhile to explore the application of these techniques to other materials, such as Ni-W with a composite-like microstructure [49][50][51], dissimilar welding joints [52], or harmonic alloys [53,54]. An R 2 value of 0.94 was obtained for the decision tree model, while the random forest model achieved a value of 0.95.…”

Section: Resultsmentioning

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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