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 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.

This manuscript aims to study the effects of drilling factors on the thermal-mechanical properties and delamination experimentally during the drilling of glass fiber reinforced polymer (GFRP). Drilling studies were carried out using a CNC machine under dry cutting conditions by 6 mm diameter with different point angles of ∅ = 100°, 118°, and 140°. The drill spindle speed (400, 800, 1600 rpm), feed (0.025, 0.05, 0.1, 0.2 mm/r), and sample thickness (2.6, 5.3, and 7.7 mm) are considered in the analysis. Heat affected zone (HAZ) generated by drilling was measured using a thermal infrared camera and two K-thermocouples installed in the internal coolant holes of the drill. Therefore, two setups were used; the first is with a rotating drill and fixed specimen holder, and the second is with a rotating holder and fixed drill bit. To measure thrust force/torque through drilling, the Kistler dynamometer model 9272 was utilized. Pull-in and push-out delamination were evaluated based on the image analyzed by an AutoCAD technique. The regression models and multivariable regression analysis were developed to find relations between the drilling factors and responses. The results illustrate the significant relations between drilling factors and drilling responses such as thrust force, delamination, and heat affect zone. It was observed that the thrust force is more inspired by feed; however, the speed effect is more trivial and marginal on the thrust force. All machining parameters have a significant effect on the measured temperature, and the largest contribution is of the laminate thickness (33.14%), followed by speed and feed (29.00% and 15.10%, respectively), ended by the lowest contribution of the drill point angle (11.85%).

This research article interprets the computational fluid dynamics analysis on blood flow inside a symmetric stenosed artery. The current problem models the blood flow inside the left coronary artery as having a symmetric stenosis in the central region. A comprehensive physiological examination of coronary artery disease is numerically evaluated by using the computational fluid dynamics toolbox Open-Field Operation And Manipulation. There are no assumptions of mild stenosis taken into account since the considered stenosis has an exactly measured length, height and position, etc. The blood flow problem is modeled for the non-Newtonian Casson fluid with unsteady, laminar, and incompressible flow assumptions. The underlying problem is solved numerically in its dimensional form. A thorough graphical analysis is provided on the blood flow simulations, pressure profile, velocity line graphs, pressure line graphs, and streamlines for the left coronary artery having a symmetric stenosis formation. The considered artery is divided into three sections, i.e. pre-stenosis, post-stenosis, and stenosis region, and the velocity and pressure line graphs are plotted for these considered regions. The graphical illustrations provide a detailed analysis of how the blood flow is affected inside the left coronary artery due to coronary artery disease. These pre- and post-stenosis velocity line graphs reveal two intriguing results: In the pre-stenosis zone, the velocity increases with increasing axial coordinate length, whereas in the post-stenosis region, the velocity decreases with rising axial coordinate length. It is evident that as the flow moves toward the stenosis region, the flow profile rises; yet, after passing through the stenosis zone, the flow profile begins to fall as the flow moves away from the stenosis region.

This paper presents a machine learning model to predict the effect of Al₂O₃ nanoparticle content on the coefficient of thermal expansion in Cu-Al₂O₃ nanocomposites prepared using an in situ chemical technique. The model developed is a modification of Long Short-Term Memory (LSTM) using dwarf mongoose optimization (DMO), which mimics the behavior of DMO to find its food for predicting the behavior of the composite. The swarm of DMO consists of three groups, namely the alpha group, scouts, and babysitters. Each group has its own behavior to capture the food. The preparation of the nanocomposite was performed 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 thermal treatment at 850 °C for 1 h. The powders were consolidated using compaction and sintering processes. The impact of Al₂O₃ contents on the thermal properties of the Cu-Al₂O₃ nanocomposite was investigated. The results showed that the Thermal Expansion Coefficient (TEC) decreases with increasing Al₂O₃ content due to the increased precipitation of Al₂O₃ nanoparticles at the grain boundaries of the Cu matrix. Moreover, the good interfacial bonding between Al₂O₃ and the Cu may participate in this decrease in TEC. The proposed machine learning model was able to predict the TEC of all the produced composites with different Al₂O₃ content and was tested at different temperatures with very good accuracy, reaching 99%.