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.

Recently, there has been much scholarly research on the applications of CNTs in various fields which can be attributed to their outstanding properties. For that matter, machining processes as the backbone of manufacturing technologies have also benefited greatly from the introduction of CNTs. However, there is a lack of papers that provide a holistic overview on potential applications, which impedes focused and robust research in their application. In this work, after providing an outline of the methods used in increasing the productivity of machining processes, we will review the ways in which CNTs, known for their remarkable mechanical, chemical, electrical, and thermal characteristics, enhance the productivity of machining processes. We emphasize fit-for-purpose applications to determine the fate of CNTs use in machining processes. We examine the applications of CNTs in enhancing the mechanical characteristics of cutting tools, which include increased wear resistance, strength, and thermal conductivity, thereby extending tool life and performance. Additionally, this work highlights the application of nanofluids in MQL systems, where CNTs play a crucial role in reducing friction and enhancing thermal management, leading to reduced lubricant usage while maintaining cooling and lubrication effectiveness.

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%.

In this study, we manufactured Al and Al-SiC nanocomposites (SiC vol.% = 1, 2 and 4) using accumulative roll bonding (ARB) at room temperature. Mechanical and wear characteristic of the manufactured samples was studied. The uniform dispersion of SiC particles was achieved after five ARB cycles. The EDX mapping images reveal that the SiC reinforcement particles were homogeneously distributed into the Al matrix. After five ARB cycles, relatively small amount of nano-grains were observed in Al-SiC nanocomposite, indicating the effectiveness of the ARB process of achieving near nanostructure. It was observed that Vickers microhardness improve with increasing accumulative roll bonding cycles. Furthermore after 5 cycles, Vickers microhardness tests results showed that the highest hardness value of 66 HV is attributed to the sample containing 4 vol.% of SiC, much higher than the hardness value of the ARB-processed aluminum of 51 HV. Experimental results indicated that the wear resistance of this nanocomposite improved by increasing the number of ARB cycles due to SiC particles act as a solid lubricant.