Experimental and Simulation of Mechanical and Wear Behaviour of Metal Matrix Nano Composites (original) (raw)

The Bayer process, which converts bauxite into alumina, results in the primary waste product being a rusty-colored mud called "red mud." In addition to a few other insignificant chemicals, it is made up mostly of oxides of the elements iron, titanium, aluminum, and silica. Concerns about the economy and the environment have motivated enormous work on a worldwide scale to find solutions to problems associated with red mud management. These problems include usage, storage, and disposal. Even though there are a lot of diverse applications for red mud, none of them have been shown to be commercially or economically viable as of now. It is well knowledge that MMCs are strengthened to increase their resistance to wear, and that the wear characteristics may be significantly enhanced by including a tough intermetallic compound into the aluminum matrix. The purpose of the present research project is to investigate the low-cost alternative of making use of red mud as a material for reinforcing structures. This is because red mud is not only readily available but also has all of these different components that are good for strengthening. Experiments have been carried out in a laboratory setting with the purpose of determining the wear characteristics of an aluminum red mud composite when subjected to a variety of various operating situations using a pin-on-disc machine in pure sliding mode. Additionally, the samples were heated in order to improve their wear properties. Under an optical microscope, the worn surfaces of the samples that had already been used were analyzed in order to get a better understanding of how the particle reinforcement influences the wear behavior of the composite. The wear resistance and tensile strength of the composite are both enhanced when red mud particles are dispersed throughout the aluminum matrix. If the cooling medium and heat treatment are chosen appropriately, the composite may have higher wear resistance. In addition, an artificial neural network prediction model (ANN) is used in order to reproduce the correlation that exists between the property parameters. As a consequence of this, the values that were seen experimentally and those that were projected match up incredibly well. These results may serve as a springboard for researchers and industrial designers to make MMC components from industrial waste for use in wearable settings. These components may be made from trash from many industries.