A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning |
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| Affiliation: | 1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing, 211816, China;2. Siemens Factory Automation Engineering Ltd., Nanjing, 211300, China;1. Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA;2. Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA;1. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Huihong Building, Changwu Middle Road 801, Changzhou 213164, Jiangsu, China;2. Department of Science Island, University of Science and Technology of China, Hefei 230026, Anhui, China;1. Department of Mechanical Engineering, Guangxi University, 530004 Nanning, China;2. Guangxi Key Laboratory of Manufacturing Systems and Advance Manufacturing Technology, Guangxi University, 530004 Nanning, China;1. Department of Computer Science, University of Central Florida, Orlando 32816, FL, United States;2. Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando 32816, FL, United States;1. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, Ontario K7L 3N6, Canada;2. Bernal Institute, University of Limerick, Limerick V94 T9PX, Ireland;3. École de Technologie Supérieure (ÉTS), 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada |
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| Abstract: | Intelligent monitoring and diagnosis of tool status are of great significance for improving the manufacturing efficiency and accuracy of the workpiece. It is difficult to quickly and accurately predict the wear state of worm gear hob under different working conditions. This paper proposes a novel approach to predict hob wear status based on CNC real-time monitoring data. Based on the open platform communication unified architecture (OPC UA) technology and orthogonal test, the machine data of motor power, current, etc. related to tool wear are collected online in the worm gear machining process. And then, an improved deep belief network (DBN) is used to generate a tool wear model by training data. A growing DBN with transfer learning is introduced to automatically decide its best model structure, which can accelerate its learning process, improve training efficiency and model performance. The experiment results show that the proposed method can effectively predict hob wear status under multi-cutting conditions. To show the advantages of the proposed approach, the performance of the DBN is compared with the traditional back propagation neural network (BP) method in terms of the mean-squared error (MSE). The compared results show that this tool wear prediction method has better prediction accuracy than the traditional BP method during worm gear hobbing. |
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| Keywords: | Tool wear OPC UA Deep belief network Worm gear hobbing Transfer learning |
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