Salwa O. Mohammed

The CoCrMo alloys are progressively utilized as biomaterials. This research is dedicated to studying the consequence of (1, 3, and 5) wt% nano-TiO₂ addition on the porosity, microstructure, microhardness, and wear behavior of pre-alloyed CoCrMo powder produced by powder metallurgy (PM). Microstructural features were examined using SEM, SEM mapping, and XRD. Wear behavior was assessed through pin-on-disk tests performed under dry sliding conditions at varying loads and durations. Porosity increased with the addition of nano-TiO₂, from 15.26 at 0 wt% reaching 25.12% at 5 wt%, while density decreased from 7.16 to 6.33 g/cm³. Microhardness exhibited a slight improvement, attaining 348 HV at 5 wt%. SEM and XRD analyses confirmed partial particle separation after sintering and identified the TiO₂ reinforcement as rutile. Wear tests revealed that adding 1 wt% nano-TiO₂ enhanced wear resistance, whereas extended sliding durations resulted in increased wear rates. Adhesive wear was the predominant mechanism, accompanied by limited abrasive wear, oxidation, and plastic deformation.

Bioinspired materials are among the most durable materials known to man. Mimicking solutions and structures observed in nature is a modern approach to modeling materials in line with sustainable development. Designers of mechanical structures are continually seeking new applications and materials that replicate natural effects. This article presents the primary natural sources of bioinspiration in the production of advanced composite materials. The focus is on discussing current advances in the production of impact-resistant composite materials. The main sources of bioinspiration for impact-resistant materials are pearl structures, insect exoskeletons, and fruit shells. Insect cuticles offer a sustainable alternative due to their exceptional stiffness, unique properties, and mechanical parameters. The use of biocomposites in the production of mechanical structures is expected to grow in the coming years due to the continuous development of new composite technologies.

It is challenging to model the coefficient of friction, surface roughness, and related tribological processes during metal contact because of flattening, ploughing, and adhesion. It is important to choose the appropriate process parameters carefully when creating analytical models to overcome the challenges posed by complexity. This will ensure the production of sheet metal formed components that meets the required quality standards and is free from faults. This research analyses the impacts of nominal pressure, kinematic viscosity of lubricant, and lubricant pressure on the coefficient of friction and average roughness of DC05 deep-drawing steel sheets. The strip drawing test was used to determine the coefficient of friction. This work utilises the Categoric Boosting (CatBoost) machine learning algorithm created by Yandex to estimate the COF and surface roughness, intending to conduct a comprehensive investigation of process parameters. A Shapley decision plot exhibits the coefficient of friction prediction models via cumulative SHapley Additive exPlanations (SHAP) data. CatBoost has outstanding prediction accuracy, as seen by R2 values ranging from 0.955 to 0.894 for both the training and testing datasets for the COF, as well as 0.992 to 0.885 for surface roughness.