In recent years,there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields,and the demand for accu-rate machining of such composite materials has grown accordingly.In this paper,a feed-forward multi-layered artificial neural network(ANN)roughness prediction model,using the Levenberg-Marquardt backpropagation training algorithm,is proposed to investigate the mathe-matical relationship between cutting parameters and aver-age surface roughness during milling Al/SiC particulate composite materials.Milling experiments were conducted on a computer numerical control(CNC)milling machine with polycrystalline diamond(PCD)tools to acquire data for training the ANN roughness prediction model.Four cutting parameters were considered in these experiments:cutting speed,depth of cut,feed rate,and volume fraction of SiC.These parameters were also used as inputs for the ANN roughness prediction model.The output of the model was the average surface roughness of the machined workpiece.A successfully trained ANN roughness pre-diction model could predict the corresponding average surface roughness based on given cutting parameters,with a 2.08％ mean relative error.Moreover,a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91％ mean relative error was developed based on the ANN roughness prediction model.Finally,a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.