Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves

Abstract Discrete wavelet transforms of ultrasound waves is used to measure the gradual wear of carbide inserts during turning operations. Ultrasound waves, propagating at a nominal frequency of 10 MHz, were pulsed into the cutting tools towards the cutting edge at a burst frequency of 10 KHz. The reflected waves off the mark, nose and flank surfaces were digitized at a sampling rate of 100 MHz. Daubechies Quadrature Mirror Filter pair was used to decompose ultrasound signals into frequency packets using a tree structure. Normalized signals in each level of decomposition were used to search for a neural network architecture that correlates the ultrasound measurements to the wear level on the tool. A three-layer Multi-Layer Perceptron architecture yielded the best correlation (95.9%) using the wave packets from the fourth level of decomposition with frequencies 3.75–4.375 and 5.625–6.875 MHz.

[1]  Hiroshi Ota,et al.  Application of Wavelet Analysis to Monitoring of Cutting Conditions in Milling , 1994 .

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  Osama K. Eyada,et al.  An integrated ultrasonic sensor for monitoring gradual wear on-line during turning operations , 1995 .

[4]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[5]  Roderick K. Stanley,et al.  Nondestructive Evaluation: A Tool in Design, Manufacturing and Service , 2018 .

[6]  M. F. DeVries,et al.  Neural Network Sensor Fusion for Tool Condition Monitoring , 1990 .

[7]  Ichiro Inasaki,et al.  Tool Condition Monitoring (TCM) — The Status of Research and Industrial Application , 1995 .

[8]  Li Dan,et al.  Tool wear and failure monitoring techniques for turning—A review , 1990 .

[9]  K. F. Martin,et al.  A review by discussion of condition monitoring and fault diagnosis in machine tools , 1994 .

[10]  Nagayoshi Kasashima,et al.  Online Failure Detection in Face Milling Using Discrete Wavelet Transform , 1995 .

[11]  Ibrahim N. Tansel,et al.  Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN) , 1995 .

[12]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[13]  Weiguo Gong,et al.  Monitoring of Tool Wear States in Turning Based on Wavelet Analysis , 1997 .

[14]  Adrian J. Shepherd,et al.  Second-Order Methods for Neural Networks , 1997 .

[15]  Geok Soon Hong,et al.  Using neural network for tool condition monitoring based on wavelet decomposition , 1996 .

[16]  Taysir H. Nayfeh,et al.  Calibrated method for ultrasonic on-line monitoring of gradual wear during turning operations , 1997 .

[17]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[18]  Laura I. Burke,et al.  An unsupervised neural network approach to tool wear identification , 1993 .

[19]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[20]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[21]  Gang Yu,et al.  Analytical model for tool wear monitoring in turning operations using ultrasound waves , 2000 .

[22]  Li Xiaoli,et al.  On-line detection of the breakage of small diameter drills using current signature wavelet transform , 1999 .

[23]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[24]  LI XIAOLI,et al.  On-line tool condition monitoring system with wavelet fuzzy neural network , 1997 .