###### Abstract
Thus, the design of experiment (DOE), a statistical tool that minimizes and simplifies experiments, thereby saving cost, time, and energy [16], was introduced. A commonly used DOE method is the Taguchi orthogonal array (OA). It is an efficient, simple, and cheap method of optimization. It has been widely applied in the optimization of the tribological behaviors of PMCs, as will be illustrated in the following sections. [PERSON], [PERSON], and [PERSON] optimized the tribological parameters of PMCs, and the results showed that applied load was the most influential factor affecting the wear rate [17]. [PERSON] and [PERSON] arranged the significance of factors influencing the abrasion behaviors of reinforced carbon epoxy composites as filler content > grit size > filler type [18]. Similar findings were obtained in the study of graphite and silicon carbide reinforced composites under dry conditions [19]. [PERSON] and [PERSON] reported that particle content was the most influential factor influencing the tribological rate of polyphenylene sulfide matrix composites [20]. [PERSON] and [PERSON] (2013) optimized the friction and sliding wear of submicron size cenosphere particle-reinforced vinyl ester composites [21]. Cenosphere filler was the most influential parameter on coefficient of friction and wear rate, as seen in the analysis of variance (ANOVA) results [21]. In his study of PTFE-filled composites, [PERSON] established that grit size was the most significant parameter, followed by load, distance, and flexural strain [22]. [PERSON] looked into the influence of filler content, load, and distance on the abrasive wear property of epoxy-reinforced matrix composite. According to the findings, it was demonstrated that sliding distance was the most influential factor affecting the wear rate [23]. [PERSON], [PERSON], and [PERSON] [24] studied the effect of parametric wear rate of Hallosyte nanotube reinforced basalt/silk epoxy composite. Four parameters viz abrading distance, amount of filler, applied load, as well as silica sand size at three levels were considered. The results showed that abrading distance was the most influential control factor that affected the wear rate [24]. The Taguchi method establishes single parameter significance. Consequently, to establish multiple significance, the Taguchi method has been coupled with grey relational analysis (GRA). [PERSON], [PERSON], and [PERSON] [25] combined the Taguchi approach with GRA to optimize the abrasive tribological property of aluminum hybrid metal composites. It was found that grit size and applied load were the most significant parameters affecting the wear rate and coefficient of friction, respectively, of the composites [25]. Based on the mean response Taguchi-GRA results, [PERSON], [PERSON], and [PERSON] [26] found that load at 60 N, a speed of \(\mathrm{I\,ms^{-1}}\), and a distance of 1000 m were the optimal control factors. ANOVA revealed that distance gave the highest contribution of 61.05% to the wear rate of the C4 composites. An improvement of 35% in the grey relational grade was achieved on validation [26]. [PERSON] and [PERSON] [27] used the same approach hybridized with principal component analysis (PCA) for the optimization of the laser parameters of the dimple geometry of stainless steel. It was found that that optimal GRG peak value of 0.2642 was obtained at an average power of 12 W, pulse duration of 1500 ns, and a frequency of 15 Hz. ANOVA showed that average power was the most significant parameter, while depth was of little or no importance [27]. The prediction of the abrasive tribological behaviors of PMCs is not an inconsequential role, and the departure from generality behaviors are common. Generally, it is difficult to predict the tribological behaviors of PMCs due to the interdependencies among the process variables. Tribological modelling usually entails a significant number of experiments together with analytical modeling and/or numerical simulation, leading to high costs, time consumption, and energy losses.