Abstract
This paper addresses the development of an online tool condition monitoring and diagnosis system for a milling process. To establish a tool condition monitoring and diagnosis system, three modeling algorithms–an Adaptive neuro fuzzy inference system (ANFIS), a Back-propagation neural network (BPNN) and a Response surface methodology (RSM)–are considered. In the course of modeling, the measured milling force signals are processed, and critical features such as Root mean square (RMS) values and node energies are extracted. The RMS values are input variables for the models based on ANFIS and RSM, and the node energies are those for the BPNN-based model. The output variable is the confidence value, which indicates the tool condition states–initial, workable and dull. The tool condition states are defined based on the measured flank wear values of the endmills. During training of the models, numerical confidence values are assigned to each tool condition state: 0 for the initial, 0.5 for the workable and 1 for the dull. An experimental validation was conducted for all three models, and it was found that the RSM-based model is best in terms of lowest root mean square error and highest diagnosis accuracy. Finally, the RSM-based model was used to build an online system to monitor and diagnose the tool condition in the milling process in a real-time manner, and its applicability was successfully demonstrated.
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Jiwoong Lee received his B.S. in Mechanical Engineering from Sungkyunkwan University, Korea, in 2015, where he is currently a Ph.D. student.
Sang Won Lee received his B.S. and M.S. degrees in the Department of Mechanical Design and Production Engineering from Seoul National University, Korea, in 1995 and 1997. He received the Ph.D. in Mechanical Engineering from the University of Michigan in 2004. Dr. Lee joined the School of Mechanical Engineering at Sungkyunkwan University in 2006, and is currently an Associate Professor.
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Lee, J., Choi, H.J., Nam, J. et al. Development and analysis of an online tool condition monitoring and diagnosis system for a milling process and its real-time implementation. J Mech Sci Technol 31, 5695–5703 (2017). https://doi.org/10.1007/s12206-017-1110-4
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DOI: https://doi.org/10.1007/s12206-017-1110-4