The effects of nano-additives on the mechanical, impact, vibration, and buckling/post-buckling properties of composites: A review
This study presents a review of the effect of nano-additives in improving the mechanical
properties of composites. Nano-additives added to composites, also termed …
properties of composites. Nano-additives added to composites, also termed …
AI for tribology: Present and future
With remarkable learning capabilities and swift operational speeds, artificial intelligence (AI)
can assist researchers in swiftly extracting valuable patterns, trends, and associations from …
can assist researchers in swiftly extracting valuable patterns, trends, and associations from …
[HTML][HTML] On the prediction of the mechanical properties of ultrafine grain Al-TiO2 nanocomposites using a modified long-short term memory model with beluga whale …
Mechanical properties of fine grain nanocomposites differ from those of conventional
composites due to the in situ effect caused by the addition of nanoparticle reinforcement and …
composites due to the in situ effect caused by the addition of nanoparticle reinforcement and …
Prediction of tribological properties of alumina-coated, silver-reinforced copper nanocomposites using long short-term model combined with golden jackal optimization
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) …
short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) …
[HTML][HTML] The effect of Cu coated Al2O3 particle content and densification methods on the microstructure and mechanical properties of Al matrix composites
In the study, aluminum-based nanocomposites were reinforced with different concentrations
of Al 2 O 3 nanoparticles ranging from 0 to 15 wt.%. The Al 2 O 3 nanoparticles were coated …
of Al 2 O 3 nanoparticles ranging from 0 to 15 wt.%. The Al 2 O 3 nanoparticles were coated …
Prediction of wear rates of Al-TiO2 nanocomposites using artificial neural network modified with particle swarm optimization algorithm
The prediction of the wear rates and coefficient of friction of composite materials is relatively
complex using mathematical models due to the effect of the manufacturing process on the …
complex using mathematical models due to the effect of the manufacturing process on the …
[HTML][HTML] Prediction of the tensile properties of ultrafine grained Al–SiC nanocomposites using machine learning
We discovered and analyzed the new prediction model by using machine learning (ML) for
the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated …
the tensile strength of aluminum nanocomposites reinforced with μ-SiC particles fabricated …
[HTML][HTML] Enhanced random vector functional link based on artificial protozoa optimizer to predict wear characteristics of Cu-ZrO2 nanocomposites
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 …
freshly updated machine learning method that uses an Artificial Protozoa Optimizer (APO) to …
[HTML][HTML] Fabrication of efficient aluminium/graphene nanosheets (Al-GNP) composite by powder metallurgy for strength applications
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 …
lightweight materials for diverse technological fields, eg, automobile, aerospace, naval, and …
[HTML][HTML] Optimization of the accumulative roll bonding process parameters and SiC content for optimum enhancement in mechanical properties of Al-Ni-SiC …
Abstract Accumulative Roll Bonding (ARB) is one of the main techniques to manufacture
nanocomposites, however, due to the large number of parameters that control this process …
nanocomposites, however, due to the large number of parameters that control this process …