TY - JOUR
T1 - Development of a cutting direction and sensor orientation independent monitoring technique for end-milling
AU - Roth, John Timothy
AU - Pandit, Sudhakar M.
PY - 1999
Y1 - 1999
N2 - In the authors' previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to triaxial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation.
AB - In the authors' previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to triaxial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation.
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M3 - Article
AN - SCOPUS:0033310962
VL - 10
SP - 597
EP - 603
JO - American Society of Mechanical Engineers, Textile Engineering Division (Publication) TED
JF - American Society of Mechanical Engineers, Textile Engineering Division (Publication) TED
SN - 1071-6947
ER -