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.

Microstructure characteristics and compressive property relationships of so-called harmonic (composed by fine and coarse grains) and conventional pure titanium (Ti) and Ti-6Al-4V alloy processed by powder metallurgy route are presented in the present work. Electron backscatter diffraction (EBSD) analysis was performed to characterize the as-processed microstructures. The harmonicity structure of selected samples is described, and relevant EBSD maps are presented. The bulk samples’ hardness is reported, along with compressive responses at quasi-static and intermediate strain rates, ranging from 0.005 s⁻¹ to 16 s⁻¹. The strain rate sensitivity of these metallic samples is discussed, and the benefits in terms of mechanical properties of the harmonic microstructures compared with the non-harmonic conventional ones are highlighted. Finally, a modified Johnson–Cook model was shown to predict fairly the experimental results.

A multi-principal element alloy (MPEA) is a type of metallic alloy that is composed of multiple metallic elements, with each element making up a significant portion of the alloy. In this study, the initial atomic percentage of elements in an (AlFeNiTiVZr)1-xCrx MPEA alloy as a function of the position on the surface was investigated using machine learning algorithms. Given the absence of a linear relationship between the atomic percentage of elements and their location on the surface, it is not possible to discern any clear association from the dataset. To overcome this non-linear relationship, the prediction of the atomic percentage of elements was accomplished using both decision tree (DT) and random forest (RF) regression models. The models were compared, and the results were found to be consistent with the experimental findings (a coefficient of determination R² of 0.98 is obtained with the DT algorithm and 0.99 with the RF one). This research demonstrates the potential of machine learning algorithms in the analysis of wavelength-dispersive X-ray spectroscopy (WDS) datasets.