###### Abstract
As a panacea to this, the use of machine learning to build predictive models that can function with relatively small datasets and are capable of drawing conclusions regarding nonlinear, complex, and noisy data is essential. Different artificial intelligence (AI) models, such as artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and support vector regression (SVR) models, have been applied in different aspects of tribology. [PERSON], [PERSON], and [PERSON] [28] employed ANN to predict the wear rate of a PTFE matrix strengthened with discontinuous carbon and glass fibre composites. The prediction accuracy of the models was also influenced by the amount of training. The ANN results showed better agreement with the trial results [28]. ANNs were also used by [PERSON], [PERSON], and [PERSON] [29] to predict the volume loss and coefficient of friction of glass-reinforced polyamide matrix composites. To search for the optimal model, five different learning algorithms were tried. The learning algorithm results showed that the Levenberg-Marquardt algorithm was the optimum, having a prediction capacity of 87.59%. The predicted outputs showed reasonable agreement with the measured outputs [29]. Equally, ANNs were used to predict the tribological and mechanical properties of behaviors and the mechanical properties of short-fibre PMCs [30; 31]. The prediction performance of the ANN model was compared with the ANFIS model by [PERSON], [PERSON], and [PERSON] in the specific wear loss prediction of graphite-, carbon-, and nano-TiO\({}_{2}\)-filled PTFE matrix composites. After trying different learning algorithms for ANN, the LM algorithm was the best, while for the ANFIS model, the trapezoidal membership function was the best. The comparative results showed superior prediction performance to the ANN model [32]. Taguchi-ANN has been used to optimize and predict the abrasion of industrial waste and glass-filled polyester matrix composites. The combination of optimal parameters for minimum wear was found to be 1.8 ms\({}^{-1}\)-10 N-15 %-80 m for industrial waste, and for glass-reinforced polyester matrix composite, the combination was s 2.10 ms\({}^{-1}\)-10 N-15 %-70 m for the speed, load, filler amount, and distance, respectively. The ANN predictions showed better performance than the linear regression model [33]. The SVR model was used in the prediction of the tool flank wear performance of machine surface [34], the tool wear face milling of mild steel [35], and the diameter of PCL/gelatin [36]. The ANN model was compared with the Harris Hawk optimization (HHO) model in the optimized prediction of the abrasive tribological behavior of ultra-high-strength martensitic steel. The prediction results of the models revealed that the ANN-HHO model gave better prediction than the ANN as a single model [37]. Although AI models indicate practical and promising capabilities in the field of material sciences, they are still associated with certain limitations and challenges such as numerous and qualitative data, which serve as the prerequisite requirements. Even though various material characteristic databases are accessible, discrepancies in the data provided by different parties make machine learning algorithms difficult to apply effectively. Machine learning models are also known to break the principles of physical science, because they are frequently driven solely by the data provided and do not take into account any chemistry/physics-based knowledge. A survey of the reported literature on the database Scopus yielded the finding that there were 450 peer-reviewed papers, from 1989 to date, focusing on the abrasive tribological behaviors of PTFE matrix composites, indicating the wide range of interest in this topic. Figure 1b shows 388 keywords that occurred among those studies, indicating the deep interest and implementation in this field.