This paper presents an experimental and statistical study of the fatigue behavior of unidirectional glass fiber-reinforced epoxy composite rods manufactured using pultrusion technique and modified with nanoparticles of alumina (Al₂O₃) and silica (SiO₂) at four different weight fractions (0.5, 1.0, 2.0 and 3.0 wt.%). Tensile test was performed to investigate the influence of nanoparticles. Addition of alumina nanoparticles up to 3 wt.% increases the tensile strength by 54.76% over the pure glass fiber-reinforced epoxy specimen. For silica nanoparticles, there is an increase in the tensile strength of 31.29% for the content of 0.5 wt.% over the pure glass fiber-reinforced epoxy specimen. As the silica nanoparticles’ content increases over 0.5 wt.%, there is a decrease in the tensile strength. Rotating bending fatigue tests have been conducted at five different stress levels. Fatigue life of glass fiber-reinforced epoxy composite rods modified with alumina nanoparticles increases as the content of the nanoparticles increases. The effect of adding silica nanoparticles on the fatigue life of glass fiber-reinforced epoxy composite rods is relatively insignificant with a small improvement in the content of 0.5 wt.% silica above the pure glass fiber-reinforced epoxy specimens. Two-parameter Weibull distribution function was used to statistically analyze the fatigue life data.

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) algorithm to predict the tribological performance of Cu–Al₂O₃ nanocomposites. The modified model was applied to predict the wear rates and coefficient of friction of Cu–Al₂O₃ nanocomposites that were developed in this study. Electroless coating of Al₂O₃ nanoparticles with Ag was performed to improve the wettability followed by ball milling and compaction to consolidate the composites. The microstructural, mechanical, and wear properties of the produced composites with different Al₂O₃ content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear tests at different loads and speeds. From a materials point of view, the manufactured composites with 10% Al₂O₃ content showed huge enhancement in hardness and wear rates compared to pure copper, reaching 170% and 65%, respectively. The improvement of the properties was due to the excellent mechanical properties of Al₂O₃, grain refinement, and dislocation movement impedance. The developed model using the LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all the considered composites.

This study aims to detect the positive effects of alumina coating by silver on the mechanical and tribological characteristics such as hardness and wear resistance of Al₂O₃ reinforced copper matrix nanocomposites. 2.5, 5, 7.5, and 10% wt. of alumina particles reinforced coated silver copper matrix nanocomposites were fabricated using the PM method. A scanning electron microscopy (SEM) was used to investigate the morphology of powders and microstructure of the nanocomposite samples. The density and hardness of samples were measured by the Archimede’s method and Vickers microhardness method, respectively. Abrasive wear test of samples was measured using pin on disk. The results showed that coated alumina were homogenous distributed in the copper matrix. The densification of samples decreased but the hardness of samples increased with increasing Al₂O₃ content due to hard structure of Al₂O₃ ceramic particles. The highest microhardness was measured as 175 HV for copper matrix nanocomposites fabricated by using 10%wt of Al₂O₃ content. 70 HV was obtained for the copper matrix. Abrasive wear rate reduces by increasing the coated alumina percent and increase via increasing the normal load. In addition, the friction coefficient of the developed surface was found to decreasing with respect to increase coated alumina percent and increasing normal load.

This paper presents a machine learning model to predict the effect of Al₂O₃ nanoparticles content on the wear rates in Cu-Al₂O₃ nanocomposite prepared using in situ chemical technique. The model developed is a modification of the random vector functional link (RVFL) algorithm using artificial hummingbird algorithm (AHA). The objective of using AHA is used to find the optimal configuration of RVFL to enhance the prediction of Al₂O₃ nanoparticles. The preparation of the composite was done using aluminum nitrate that was added to a solution containing scattered copper nitrate. After that, the powders of CuO and Al₂O₃ were obtained, and the leftover liquid was removed using a thermal treatment at 850 °C for 1 h. The powders were consolidated using compaction and sintering processes. The microhardness of the nanocomposite with 12.5% Al₂O₃ content is 2.03-fold times larger than the pure copper, while the wear rate of the same composite is reduced, reaching 55% lower than pure copper. These improved properties are attributed to the presence of Al₂O₃ nanoparticles and their homogenized distributions inside the matrix. The developed RVFl-AHA model was able to predict the wear rates of all the prepared composites at different wear load and speed, with very good accuracy, reaching nearly 100% and 99.5% using training and testing, respectively, in terms of coefficient of determination R₂.