Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207540500536913
Title: Sensor fusion for online tool condition monitoring in milling
Authors: Wang, W.H.
Hong, G.S. 
Wong, Y.S. 
Zhu, K.P. 
Keywords: Force
Kohonen's self-organizing map (SOM)
Milling
Sensor fusion
Tool condition monitoring (TCM)
Vision
Issue Date: Nov-2007
Source: Wang, W.H., Hong, G.S., Wong, Y.S., Zhu, K.P. (2007-11). Sensor fusion for online tool condition monitoring in milling. International Journal of Production Research 45 (21) : 5095-5116. ScholarBank@NUS Repository. https://doi.org/10.1080/00207540500536913
Abstract: The objective of this paper is to combine a direct sensor (vision) and an indirect sensor (force) to create an intelligent integrated tool condition monitoring (TCM) system for online monitoring of flank wear and breakage in milling, using the complementary strengths of the two types of sensors. For flank wear, images of the tool are captured and processed in-cycle using successive moving-image analysis. Two features of the cutting force, which closely indicate flank wear, are extracted in-process and appropriately pre-processed. A self-organizing map (SOM) network is trained in a batch mode after each cutting pass, using the two features derived from the cutting force, and measured wear values obtained by interpolating the vision-based measurement. The trained SOM network is applied to the succeeding machining pass to estimate the flank wear in-process. The in-cycle and in-process procedures are employed alternatively for the online monitoring of the flank wear. To detect breakage, two features in time domain derived from cutting force are used, and the thresholds for them are determined dynamically. Again, vision is used to verify any breakage identified in-process through the cutting force monitoring. Experimental results show that this sensor fusion scheme is feasible and effective for the implementation of online tool condition monitoring in milling, and is independent of the cutting conditions used.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/61274
ISSN: 00207543
DOI: 10.1080/00207540500536913
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