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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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]