In recent years, aluminium (Al) matrix reinforced with graphene-based nanomaterial (GN)
composites (AGNCs) have emerged as a potential candidate for various applications due to …

This work focused on develo** an analytical fast and reliable model to predict the
morphological changes in nanocomposite powders during mechanical alloying. A …

This study examines the mechanical properties and wear mechanisms of magnesium (Mg)
metal matrix composites reinforced with titanium (Ti) and silicon carbide (SiC) particles …

In this paper, we present a newly modified machine learning model that employs a long
short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) …

Friction coefficient and wear rate are crucial performance indexes for the development of
nanomaterials, which are of great significance for realizing energy conservation and …

Due to the lack of analytical solutions for the wear rates prediction of nanocomposites, we
present a modified machine learning method named Dendritic Neural (DN) to predict the …

Although dental raw PMMA has unique features such as its ease and aesthetic fabrication,
low cost, and a favorable physical and mechanical properties. Nonetheless, these are …

Owing to the absence of scientific methods for predicting nanocomposites' wear rates, a
freshly updated machine learning method that uses an Artificial Protozoa Optimizer (APO) to …

The advancement in material science is the need of the hour to generate efficient and
lightweight materials for diverse technological fields, eg, automobile, aerospace, naval, and …

Peripheral coarse grain (PCG) structure is a common microstructural defect appearing in the
aluminum alloy extrusion process, which seriously affects the mechanical properties of the …