The artificial neural network (ANN) approach motivated by the biological nervous system is
an inspiring mathematical tool that simulates many complicated engineering applications …

In tribology, a considerable number of computational and experimental approaches to
understand the interfacial characteristics of material surfaces in motion and tribological …

In this article we provide an overview on the current and emerging applications of machine
learning (ML) in the design, synthesis, and characterization of metal matrix composites …

In this study, few-layer graphene oxide (FLGO) reinforced Mg matrix composite was
successfully fabricated by the hetero-agglomeration method and spark plasma sintering …

The work aims to prepare and characterize polyester nanoclay nanocomposite (PNNCs)
with various nanoclay weight percentages (0, 2, and 4). Nanoclay and polyester resin are …

Today, Discontinuously Reinforced Particulate Titanium Matrix Composites (DRPTMCs)
have been the most popular and challenging in consideration with development and heat …

Abstract The Artificial Neural Network (ANN) techniques were utilized to predict wear rate
and CoF of the Ti-5Al-2.5 Sn matrix reinforced with B4C particle manufactured by the …

This research work highlights the prediction of hardness behaviour of age-hardened LM4
and its composites fabricated using a two-stage stir casting method with TiB 2 and Si 3 N 4 …

The Gleeble3500 thermal simulator was used to (2vol% Ti 5 Si 3+ 5vol% TiBw)/TC11
composites with network reinforcement structure at a deformation temperature of 1183–1363 …

The Gleeble3500 thermal simulator was used to (2vol.% Ti5Si3+ 5vol.% TiBw)/TC11
composites with network reinforcement structure at a deformation temperature of 1183 …