Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks
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Prediction and control of surface roughness for the milling of Al/ SiC metal matrix composites based on neural networks Guo Zhou1 • Chao Xu2,3 • Yuan Ma2,3 • Xiao-Hao Wang1,2 • Ping-Fa Feng2,3 Min Zhang2
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Received: 17 February 2020 / Revised: 8 May 2020 / Accepted: 13 October 2020 / Published online: 23 November 2020 Ó Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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 accurate 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 mathematical relationship between cutting parameters and average 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 prediction 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
& Min Zhang [email protected] 1
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, Guangdong, People’s Republic of China
2
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, People’s Republic of China
3
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China
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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. Keywords Al/SiC metal matrix composite (MMC) Surface roughness Prediction Control Neural network List of symbols ANN Artificial neural network CNC Computer numerical control PCD Polycrystalline diamond MMC Metal matrix composite Vc Cutting speed Fr Feed rate Dc Depth of cut uSiC Volume fraction of SiC Output of the mth neuron in the layer under okm consideration Output of the nth neuron in the preceding ok1 n layer wmn Weight value of the connection between the mth neuron in the layer under consideration and the nth neuron in the preceding layer Bias value for the mth neuron in
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