This study presented the ballistic properties and impact strength of the aluminum alloy AA 7075-reinforced nano-SiC composites, which were produced by stir-squeeze casting and forging processes. Ballistic tests revealed that the samples met the National Institute of Justice (NIJ) Type II standard, except for samples that were not forged. The other characteristics examined were microstructural observations analyzed using optical and scanning electron microscopes. As a result of the pinning effect, the microstructure of the samples showed a decrease in grain size as the SiC content increased, except for samples with over 0.25 vf% of nano-SiC. This effect was observed in samples with and without forging. The forging process decreased grain size further, affecting ballistic and mechanical properties. The addition of 0.3 vf% nano-SiC improved the ballistic properties and impact strength, resulting in the highest impact value of 7.7 J, despite higher values of the average ballistic diameter of perforation (about 4.7 mm) and average indentation depth (about 17.22 mm) measured on this sample.
THE EFFECT OF ANNEALING AND COLD FORGING ON MICROSTRUCTURE AND HARDNESS PROPERTIES OF AL-SIC COMPOSITE: A PRELIMINARY STUDY. Aluminium Metal Matrix Composites (AMMCs) are one of the exciting materials that have an extensive function in various applications. By utilizing reinforcement in the fabrication process, Al composites can produce superior properties such as high strength, good fracture resistance, and of course, lightweight. Therefore, many studies are interested in revealing the characteristics of Al composite materials through various methods and variations of reinforcement. This research is a preliminary study with a scope of work, including observing the effects of annealing and cold forging processes on the microstructure morphology and hardness properties of SiC nano-ceramic reinforced Al composites. The aluminium used in this study is a 7xxx series aluminium alloy. The fabrication process was carried out by stir-squeeze casting method. Microstructure analysis was conducted by optical microscopy and Scanning Electron Microscopy (SEM) equipped with Emission Dispersion Spectroscopy (EDS). The hardness properties of the Al-SiC composite were examined by micro Vickers hardness testing. This research reported that the annealing process influences the grain refinement and hardness properties of the Al-SiC composite. The sample experienced to cold forging has to improve the hardness value. Increasing hardness by forging after anneal may introduce due to the grain compression effect of the dislocation mechanism. Comprehensive research is required to find out other potentials of Al-SiC composite materials. Keywords: Al-SiC composite, annealing temperature, cold forging, hardness, microstructure.
Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.
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