Advanced signal processing in acoustic emission monitoring systems for machining technology

Publisher Summary This chapter focuses on the application of acoustic emission (AE) based monitoring systems to machining processes. It describes the most common advanced signal processing methods used in this type of systems such as continuous and discrete transforms (Fourier, Gabor, and Wavelet) and statistical analysis methods (amplitude distribution method and the entropic distance method). In addition, some of the most relevant papers illustrating the mentioned signal processing methods are discussed. The principal machining technology aspects considered for AE based sensor are catastrophic tool failure and chip formation.

[1]  Roberto Teti.  In-Process Monitoring of Cutting Conditions and Tool Wear Using Acoustic Emission , 1989 .

[2]  Roberto Teti,et al.  Tool Wear Monitoring Through Acoustic Emission , 1989 .

[3]  Andrew Y. C. Nee,et al.  A Comprehensive Identification of Tool Failure and Chatter Using a Parallel Multi-ART2 Neural Network , 1998 .

[4]  Xiaoli Li,et al.  Tool wear detection with fuzzy classification and wavelet fuzzy neural network , 1999 .

[5]  S. Sengupta.  Introduction to Applied Statistical Signal Analysis , 1991 .

[6]  Philip Denbigh,et al.  System analysis and signal processing , 1998 .

[7]  Elijah Kannatey-Asibu,et al.  Acoustic emission and force sensor fusion for monitoring the cutting process , 1989 .

[8]  S. K. Malhotra,et al.  Online monitoring of acoustic emission for quality control in drilling of polymeric composites , 2007 .

[9]  João Fernando Gomes de Oliveira,et al.  Precision manufacturing process monitoring with acoustic emission , 2006 .

[10]  Igor Grabec,et al.  Application of a neural network to the estimation of surface roughness from ae signals generated by friction process , 1995 .

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

[12]  Xiaoli Li,et al.  A brief review: acoustic emission method for tool wear monitoring during turning , 2002 .

[13]  David Dornfeld,et al.  A study of tool wear using statistical analysis of metal-cutting acoustic emission , 1982 .

[14]  Chen Ming,et al.  On-line tool breakage monitoring in turning , 2003 .

[15]  Igor Grabec,et al.  Analysis of Grinding Process Acoustic Emission by a Neural Network , 1993 .

[16]  Igor Grabec,et al.  Characterization of the grinding process by acoustic emission , 2000 .

[17]  Krzysztof Jemielniak,et al.  Catastrophic Tool Failure Detection Based on Acoustic Emission Signal Analysis , 1998 .

[18]  Soundar Rajan Tirupatikumara,et al.  Wavelet representation of Acoustic Emission in turning process , 1995 .

[19]  Description of ground surfaces based upon AE analysis by a neural network , 1997 .

[20]  Fritz Klocke,et al.  Automatic chatter detection in grinding , 2003 .

[21]  Yuan Zhejun,et al.  Tool wear monitoring with wavelet packet transform—fuzzy clustering method , 1998 .

[22]  Jemielniak.  Some aspects of AE application in tool condition monitoring , 2000, Ultrasonics.

[23]  Elijah Kannatey-Asibu,et al.  Tool Failure Monitoring in Turning by Pattern Recognition Analysis of AE Signals , 1988 .

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