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 Zhou , Chao Xu , Yuan Ma , Xiao-Hao Wang , Ping-Fa Feng , Min Zhang

Advances in Manufacturing ›› 2020, Vol. 8 ›› Issue (4) : 486 -507.

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Advances in Manufacturing ›› 2020, Vol. 8 ›› Issue (4) : 486 -507. DOI: 10.1007/s40436-020-00326-x

Article

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

Guo Zhou ¹
, Chao Xu ²^,³
, Yuan Ma ²^,³
, Xiao-Hao Wang ¹^,²
, Ping-Fa Feng ²^,³
, Min Zhang ²^,^f

Author information +

¹ Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, 518055, Shenzhen, Guangdong, People’s Republic of China

² Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, Guangdong, People’s Republic of China

³ Department of Mechanical Engineering, Tsinghua University, 100084, Beijing, People’s Republic of China

^f zhang.min@sz.tsinghua.edu.cn

Guo Zhou received his BS and MS degree in Huazhong University of science & technology and Melbourne University, respectively. He currently is a PhD candidate in Tsinghua-Berkeley Shenzhen Institute, Tsinghua University. His research topic is applying the machine learning technologies on the precision manufacturing.

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Chao Xu received his PhD from Department of Mechanical Engineering, Tsinghua University, Beijing, China in 2015. He is a research assistant in the Division of Advanced Manufacturing, Shenzhen International School, Tsinghua University. Dr. Xu has shown great interest in vibration monitoring and control of manufacturing systems, ultrasonic-assisted machining processing, et al. He has published more than 30 journal or conference papers.

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Yuan Ma received his PhD degree in engineering science from the Department of Mechanical Engineering of Tsinghua University where he was engaged in research in the field of high precision machining technology. At present, he is working in the Laboratory of Intelligent Manufacturing and Precision Machining, and studying advanced machining equipment and technology.

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Xiao-Hao Wang received his PhD degree in engineering science from the Department of Precision Instruments and Mechanology of Tsinghua University where he was engaged in research in the field of microfluidics and microfluidic MEMS devices. At present, he is working in the Micro and Nano Technology Research Center, and studying microactuators and their application.

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Ping-Fa Feng received his B.S. and M.S. degree from Tsinghua University, China, and received his Ph.D. degree from Technical University Berlin, Germany. He is now a professor in Department of Mechanical Engineering of Tsinghua University, and the director of Division of Advanced Manufacturing, Graduate School at Shenzhen, Tsinghua University. His research interests include high Speed and high performance machining technology, rotary ultrasonic precision machining technology, performance analysis and optimization of manufacturing equipment, on-machine verification technology for NC machining accuracy.

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Min Zhang received the Ph.D. degree in mechanical engineering from Dalian University of Technology, China in 2006. He then worked as a postdoctoral researcher in Tsinghua University, China and The University of Minnesota, USA from 2006 to 2011. In 2012, he joined the Faculty at Tsinghua University, Shenzhen, China, where he is currently an Associate Professor with the Graduate School at Shenzhen. He is directing the Microsensors and Actuators Lab. He currently serves as an Editorial Board Member for Advances in Mechanical Engineering.

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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 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

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Guo Zhou, Chao Xu, Yuan Ma, Xiao-Hao Wang, Ping-Fa Feng, Min Zhang. Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks. Advances in Manufacturing, 2020, 8(4): 486-507 DOI:10.1007/s40436-020-00326-x

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Funding

National High Technology Research and Development Plan of China(2015AA043505)

Equipment Advanced Research Funds(61402100401)

Equipment Advanced Research Key Laboratary Funds(6142804180106)

Shenzhen Fundamental Research Funds(JCYJ20180508151910775)

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