###### Abstract  
This study comprehensively investigates the impact of various parameters on aluminum matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu matrix composite (2 xxo series) was employed in the current study, and Al\({}_{2}\)O\({}_{3}\) was used as a reinforcement. The performance evaluation of the Al-4 Cu/Al\({}_{2}\)O\({}_{3}\) composite involved analyzing the influence of the Al\({}_{2}\)O\({}_{3}\) weight percent (wt. %), the height-to-diameter ratio (H/D) of the compacted samples, and the compaction pressure. Different concentrations of the Al\({}_{2}\)O\({}_{3}\) reinforcement, namely 0%, 2.5%, 50%, 7.5%, and 10% by weight, were utilized, while the compaction process was conducted for one hour under varying pressures of 500, 600, 700, 800, and 900 MPa. The compacted Al-4 Cu/Al\({}_{2}\)O\({}_{3}\) composites were in the form of cylindrical discs with a fixed diameter of 20 mm and varying H/D ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. Moreover, the machine learning (ML), design of experiment (DOE), response surface methodology (RSM), genetic algorithm (GA), and hybrid DOE-GA methodologies were utilized to thoroughly investigate the physical properties, such as the relative density (RD), as well as the mechanical properties, including the hardness distribution, fracture strain, yield strength, and compression strength. Subsequently, different statistical analysis approaches, including analysis of variance (ANOVA), 3D response surface plots, and ML approaches, were employed to predict the output responses and optimize the input variables. The optimal combination of variables that demonstrated significant improvements in the RD, fracture strain, hardness distribution, yield strength, and compression strength of the Al-4 Cu/Al\({}_{2}\)O\({}_{3}\) composite was determined using the RSM, GA, and hybrid DOE-GA approaches. Furthermore, the ML and RSM models were validated, and their accuracy was evaluated and compared, revealing close agreement with the experimental results. aluminum matrix composites (AMCs); machine learning (ML); genetic algorithm (GA); response surface method (RSM); powder metallurgy (PM); Al-Cu alloy +
Footnote †: journal: Applied Sciences
Therefore, understanding these relationships in a new approach results in it being embraced by the engineering community and materials science. In the last few decades, there has been an urgent need to reduce the energy consumption of aircraft and vehicles by reducing the weight of each component. Consequently, metal matrix composites (MMCs) have recently replaced conventional engineering materials due to their promising properties [3; 4]. Aluminum matrix composites (AMCs) are MMCs that have superior advantages, such as a high strength-to-weight ratio, excellent weldability, good corrosion, fatigue resistance, and wear resistance [5; 6; 7; 8; 9; 10; 11; 12]. AMCs, especially Al-Cu matrix composites (2 xxx series), fabricated by powder metallurgy (PM), are widely employed in the automotive and aerospace industries because of their low density and the material savings achieved by using near-net-shape processing characteristics [13; 14; 15]. Moreover, Al-4 Cu AMCs produced by PM provide extraordinary sintering control. Therefore, Al-4 Cu is the root of the majority of commercial AMCs produced by PM [16]. Numerous reinforcements, such as Al\({}_{2}\)O\({}_{3}\)[17], TiC [18], SiC, and B\({}_{4}\)C graphene [19], have been alloyed in AMCs [20]. Al\({}_{2}\)O\({}_{3}\) has significantly improved the strength of Al-4 Cu alloy by realizing the approximate theoretical sintering density [21]. However, this required trial-and-error experiments, so time was consumed. Utilizing machine learning (ML) techniques, such as predictive analytics, it is possible to anticipate the presence of a particular attribute based on the components, processing, and structure [22; 23].