@article{(Open Science Index):https://publications.waset.org/pdf/16022,
	  title     = {New Regression Model and I-Kaz Method for Online Cutting Tool Wear Monitoring},
	  author    = {Jaharah A. Ghani and  Muhammad Rizal and  Ahmad Sayuti and  Mohd Zaki Nuawi and  Mohd Nizam Ab. Rahman and  Che Hassan Che Haron},
	  country	= {},
	  institution	= {},
	  abstract     = {This study presents a new method for detecting the
cutting tool wear based on the measured cutting force signals using
the regression model and I-kaz method. The detection of tool wear
was done automatically using the in-house developed regression
model and 3D graphic presentation of I-kaz 3D coefficient during
machining process. The machining tests were carried out on a CNC
turning machine Colchester Master Tornado T4 in dry cutting
condition, and Kistler 9255B dynamometer was used to measure the
cutting force signals, which then stored and displayed in the DasyLab
software. The progression of the cutting tool flank wear land (VB)
was indicated by the amount of the cutting force generated. Later, the
I-kaz was used to analyze all the cutting force signals from beginning
of the cut until the rejection stage of the cutting tool. Results of the IKaz
analysis were represented by various characteristic of I-kaz 3D
coefficient and 3D graphic presentation. The I-kaz 3D coefficient
number decreases when the tool wear increases. This method can be
used for real time tool wear monitoring.},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {3},
	  number    = {12},
	  year      = {2009},
	  pages     = {1507 - 1512},
	  ee        = {https://publications.waset.org/pdf/16022},
	  url   	= {https://publications.waset.org/vol/36},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 36, 2009},
	}