Evaluating the Wear Characteristics of Al 5024 Alloy with Varying SiC Nano Particle Percentages and Predictive Analysis using Artificial Neural Networks

This study analyses wear and tear characteristics of EN 24 alloy better suited for optimum proportions of nanosilicon carbide (Nano SiC) particles. The study employed an intensive Taguchi layout of experiments to take a look at the influence of three crucial elements, particularly the friction coefficient (Fc) and the specific wear rate (Ks),. The three variables are the Nano SiC composition, the load, and the rotating speed of the disc. The results of the take a look at show that an increase in the Nano SiC concentration greatly reduces the specific, underlining the crucial function that Nano SiC composition performs in improving put on resistance. The test in addition reveals that the sliding distance and implemented force are the number one component that effect friction behaviour. These insights are particularly useful, notably for engineering projects wherever the management of friction and the development of wear resistance are vital. This study applies Artificial Neural Networks (ANN) to enhance our understanding and predicting skills by simulating the complicated interconnections among input factors and answers. The artificial neural network (ANN) model properly estimates an ordinary performance of 85.229 %. This prediction achievement improves the usefulness of the observe by supplying a sturdy foundation for increasing the performance and durability of material in actual-world circumstances.