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Composites are replacing more conventional materials due to their advantageous properties, such as high strength, hardness, low weight, and wear resistance. In this study, the stir casting method is used to create an Al7075/SiC aluminium matrix composite, and its dry sliding wear behaviour is examined. The EDX and SEM results both show that the silicon carbide is evenly distributed throughout the matrix. The dry sliding wear behaviour of the composites is investigated using the Taguchi L16 orthogonal array to reduce the number of experimental runs. Four key process parameters—reinforcement quantity (0%, 3%, 6%, and 9%), load (15N, 30N, 45N, and 60N), sliding velocity (0.75m/s), sliding distance (1.5m/s), and sliding distance (3m/s)—are evaluated across four levels to determine the best parameter combination for reducing wear rate. S/N ratios are best when the following conditions are met: 3 wt.% SiC reinforcement, 15 N load, 3 m/s sliding velocity, and 800 m sliding distance (as shown in the main effect graphic). Wear rate, frictional force, and coefficient of friction are all affected by the four process parameters, and their effects are often studied using analysis of variance (ANOVA). The Analysis of Variance (ANOVA) outcome indicated that the probability value associated with the applied load was below 0.05, signifying statistical significance.

This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.

AbstractButt welds were produced on AA5154/AA7150 using the FSW method, and measurements were taken for the hardness and elongation percentage of each joint. Regression analysis was utilized to investigate the significance of each parameter and formulate a mathematical relationship. Welding was performed at 1200 rpm, with a welding speed of 90 mm/min and a tool diameter of 28 mm, resulting in defect-free welds. Failures were commonly observed at the pin and the side of AA7150. To decrease the ultimate tensile strength (UTS), the welding speed (WS) or tool rotational speed (TRS) was increased. As both TRS and WS constitute the nugget zone, variations in hardness distribution were noted across the weld zones. ANOVA was applied in this study to establish the relative importance and influence of each factor on UTS. The ANOVA results indicated that all parameters significantly impacted UTS. Specifically, WS contributed 41.42%, diameter contributed 17.57%, and TRS contributed 13.27%. A quadratic model was also developed to describe the relationship between the factors and UTS values. Based on the data, a range of 0.41% to 10.36% differences existed between projected and actual values. The strength of the model was evaluated using R-Squared (R-Sq.), resulting in an obtained R-Sq. value of 0.892. This value indicates a robust relationship between the predicted and actual values. [ABSTRACT FROM AUTHOR]

The present study highlighted the nano-Si3N4 addition effect on microstructural and mechanical characteristics, such as hardness and tensile properties of the dissimilar AA2014 and AA2024 based composite material. The fabricated composites were characterized through Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD) analysis. The experimental findings revealed that the FSP technique produced a uniform distribution of nano- Si3N4 particles in the aluminum matrix (AA2014 and AA2024), which provided a significant improvement in the mechanical performance of the composite developed after triple tool passes. Fair interfacial reaction layer was identified between nano-Si3N4 and alumnium matrix. The addition of nano- Si3N4 particles in the aluminum matrix composite enhanced the hardness by 21.91% compared to the un-reinforced aluminum matrix composite. Moreover, the tensile strength of the composite was improved by approximately 14.42% in comparison to the base aluminum matrix. However, composite developed in triple tool passes produced a nanocrystalline layer near the surface of the material, which improved the wear resistance of the composite. [ABSTRACT FROM AUTHOR]