Abstract
An artificial neural network (ANN) model was developed to express the relationship between friction surfacing process parameters and experimentally developed coating geometries. In ANN modelling, process parameters such as downward axial force, rotational speed, and transverse speed are used as model input parameters, while coating width (CW) and coating thickness (CT) are used as output parameters. The ANN model was delivered with accurate results.. According to the process input parameters, the ANN model was able to forecast the coating geometries. The combined impact of input parameters and coating geometry of aluminium deposition are simulated. A detailed comparison has been made between simulated and experimental values. The results confirmed a good understanding among simulated and experimental values which confirms the RMS error values of 0.058501333% and 0.000055% for coating width and coating thickness respectively.
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