Multisensor Information Fusion Based on D-S Evidence Theory and BP Neural Network

Abstract:

This paper applies the information fusion technology to tool monitoring. As one of the most important processing factor, the cutting tool and the tool wear directly influence size precision. Signals of cutting force and vibration are measured with multi-sensor. By using multi-sensor the drawbacks can be overcome, the multi-sensor information fusion mentioned in manufacture stands for extracting kinds of information from different sensors (especially for cutting force and vibration signal in this paper), making best use of all resources，according to certain criterion to judge the spatial and time redundancy , to make the system more comprehensive. Two data fusion methods, which are BP Neural Network and Wavelet Neural Network for predicting tool wear, and are debated. By the hybrid of BP and wavelet based neural network the cutting tool status inspection system is built so that the forecast of tool wear can be achieved. The results show experimentally two of these presented methods effectively implement tool wear monitoring and predicting.

• Periodical: Key Engineering Materials (Volume 567)
• Pages: 113-117
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.567.113
• Online since: July 2013
• Authors: Can Zhao, C.R. Tang, S. Wan
• Keywords: BP Neural Network (BPNN), Cutting Force, Cutting Vibration, Data Fusion, D-S Evidence Theory

References

[1] P.B. Cheng, F.K. Yuan, and C.P. Liu, Relay, 35 (2007) 12.

[2] S. Krishnaprasad, IEEE Tram. On Neural Network, 4 (1993) 1.

[3] J. Zhang, Gilbeft ,G. Walter , Yubo Miao, and Wan Ngai Wayne Lee. IEEE Trans. on Signal Processing, 43 (1995) 6.

DOI: 10.1109/78.388860

[4] J.X. Zheng, M.J. Zhang, and Q.X. Meng, Transducer and Microsystem Technologies, 26 (2007)4.