Manufacturing and Critical Analysis of Al/SiC/Coconut and Bagasse Ash MMC using Dimensional Analysis and Feed Forward Back–Propagation Neural Network

Depending on the intended usage, a composite material composed of Silicon Carbide (SIC), Aluminium (Al), and natural waste may offer a number of benefits. Sixteen combinations containing different concentrations of Silicon Carbide (SIC), aluminium, coconut, and bagasse ash were developed and tested in order to determine the ideal composition. With a strength of 161.03 MPa and a BHN of 54.3, the composite of Al:SiC: coconut ash: bagasse ash (78:6:8:8) was determined to be the best one based on testing. The Vertical Machining Centre (VMC) machine is also used to test the machining performance. A mathematical model that correlates MRR with all input parameters was developed using novel Dimensional Analysis (DA) and a feed-forward back-propagation neural network. Performance was compared to the regression, and the DA-based MRR's efficacy was examined. With a correlation coefficient (R2) of 0.9968, the experimental results indicate that DA-based MRR prediction models perform well. DA was found to be reliable and effective. Based on the current research, it is recommended that the best composite be used for the different underwater applications, like motor casing. Major Findings: Experimental findings show that the Al/SiC/ Ash MMC has gives good strength and quality after the machining. The MRR prediction model using DA also shows a good commitment to represent the process effectively and efficiently. The correlation of closeness is 0.9968, which shows the accuracy of the prediction model.