Abstract
ABSTRACT CNC Magnetic Abrasive Finishing (CNC MAF) is famous for aluminium sheet processing. This study used an artificial neural network (ANN) analytical model with a Bayesian regularisation algorithm (BR) to examine how machine parameters (rotation speed, feed rate, depth of cut, and several passes) affect surface roughness during CNC MAF of aluminium sheet. Experimental and quantitative data proved its prediction ability. ANN model to select the best machine parameters for low surface roughness. The perfect numerical solution was experimentally tested, and surface morphology and roughness were used to evaluate processing quality under optimal machine parameters. The ANN model is also compared to Taguchi L16 for prediction and optimisation. Compared to ANN, prediction and optimal surface finishing mean absolute error are 96% lower. The results show that the ANN model is better at studying machine configuration during CNC MAF of Al sheet. SEM and 3D Optical profilometer were also used to analyze the Al sheet. The UV-VIS Spectrophotometer measures Al polished sample reflectance at 200–700 nm.
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