MATEC Web Conf.
Volume 207, 2018International Conference on Metal Material Processes and Manufacturing (ICMMPM 2018)
|Number of page(s)||4|
|Section||Material Science Engineering|
|Published online||18 September 2018|
- Haber, R. E., Jiménez, J. E., Peres, C. R., & Alique, J. R, An investigation of tool-wear monitoring in a high-speed machining process. ,Sensors and Actuators A: Physical, 116 (3) 539–545 (2004). [CrossRef] [Google Scholar]
- N. Ghosh, Y. B. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A. R. Mohanty, A. B. Chattopadhyay, Estimation of tool wear during CNC milling using neural network-based sensor fusion. MSSP, 21(1), 466–479, (2007). [Google Scholar]
- T. Pfeifer, L. Wiegers, Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement, 28(3), 209–218, (2000). [CrossRef] [Google Scholar]
- M. Cabibbo, A. Forcellese, R. Raffaeli, M. Simoncini, Reverse engineering and scanning electron microscopy applied to the characterization of tool wear in dry milling processes. Procedia CIRP, 62, 233–238, (2017). [CrossRef] [Google Scholar]
- S. Orhan, A.O. Er, N. Camuşcu, E. Aslan, Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness. NDT & E International, 40(2), 121–126, (2007). [CrossRef] [Google Scholar]
- C. S. Ai, Y. J. Sun, G. W. He, X. B. Ze, W. Li, K. Mao, The milling tool wear monitoring using the acoustic spectrum. Int J Adv Manuf Technol, 61(5-8), 457–463, (2012). [CrossRef] [Google Scholar]
- J.H. Zhou, C.K. Pang, Z.W. Zhong, F.L. Lewis, Tool wear monitoring using acoustic emissions by dominant-feature identification. IEEE Transactions on instrumentation and measurement, 60(2), 547–559, (2011). [CrossRef] [Google Scholar]
- K. Patra, S. K. Pal, K. Bhattacharyya, Artificial neural network based prediction of drill flank wear from motor current signals. Applied Soft Computing, 7(3), 929–935, (2007). [CrossRef] [Google Scholar]
- S. N. Huang, K. K. Tan, Y. S. Wong, C. W. de Silva, H. L. Goh, W. W. Tan, Tool wear detection and fault diagnosis based on cutting force monitoring. International Journal of Machine Tools and Manufacture, 47(3-4), 444–451, (2007). [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.