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%).