Wear estimation by testing the elastic behavior of tool surface

Abstract Impedance and Lamb wave structural health monitoring (SHM) techniques are two common approaches used to successfully monitor the integrity of a variety of structures. In this paper, the feasibility of applying both methods for monitoring tool wear is investigated. Additionally, the surface response to excitation (SuRE) was investigated using spectrum analyzers an alternative to the costlier impedance method. Three approaches were used to monitor the condition of both new and artificially worn drill bits. Artificial degradation of the drill bit cutting edges was necessary to avoid any possible data contamination from accidental damage to the sensors or wiring in a harsh machining environment. The estimated magnitude characteristics found from the impedance and spectrum analyzers correctly distinguished between new and worn tools. Lamb wave characteristics were represented by the envelopes of the propagated signal. The S-transformation was then used to obtain the envelope of the harmonic component of the signal at the excitation frequency. The envelopes were nearly identical at similar tool wear levels, and envelope characteristics changed significantly when Lamb wave reflections reached the sensor from the worn cutting edges. The study indicated that each of the three approaches used successfully detected tool wear.

[1]  MansinhaL..  Localization of the complex spectrum , 1996 .

[2]  Xu Yang,et al.  Radial Basis Function Network Based Monitoring of Tool Wear States , 2009, 2009 Second International Symposium on Computational Intelligence and Design.

[3]  Suresh Bhalla,et al.  Ultra Low-cost Adaptations of Electro-mechanical Impedance Technique for Structural Health Monitoring , 2009 .

[4]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[5]  Enrique Alegre,et al.  On-line tool wear monitoring using geometric descriptors from digital images , 2007 .

[6]  Hoon Sohn,et al.  Overview of Piezoelectric Impedance-Based Health Monitoring and Path Forward , 2003 .

[7]  Ibrahim N. Tansel,et al.  Modeling the Propagation of Lamb Waves using a Genetic Algorithm and S-transformation , 2007 .

[8]  Ibrahim N. Tansel,et al.  Modeling micro-end-milling operations. Part III: influence of tool wear , 2000 .

[9]  Ibrahim N. Tansel,et al.  Modeling micro-end-milling operations. Part II: tool run-out , 2000 .

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

[11]  Chengfeng Li,et al.  Modeling of three-dimensional cutting forces in micro-end-milling , 2007 .

[12]  Daniel J. Inman,et al.  Improving Accessibility of the Impedance-Based Structural Health Monitoring Method , 2004 .

[13]  P. Srinivasa Pai,et al.  Acoustic emission analysis for tool wear monitoring in face milling , 2002 .

[14]  Ibrahim N. Tansel,et al.  Detecting chatter and estimating wear from the torque of end milling signals by using Index Based Reasoner (IBR) , 2012 .

[15]  David Kerr,et al.  Assessment and visualisation of machine tool wear using computer vision , 2006 .

[16]  Ibrahim N. Tansel,et al.  Genetic tool monitor (GTM) for micro-end-milling operations , 2005 .

[17]  Xiaoli Li,et al.  Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring , 2000, IEEE Trans. Ind. Electron..

[18]  Daniel J. Inman,et al.  An Integrated Health Monitoring Technique Using Structural Impedance Sensors , 2000 .

[19]  G. Rutelli,et al.  Tool wear monitoring based on cutting power measurement , 1990 .

[20]  Cuneyt Oysu,et al.  Drill wear monitoring using cutting force signals , 2004 .

[21]  Ibrahim N. Tansel,et al.  Inspection of micro-tools at high rotational speeds , 1994 .

[22]  Ibrahim N. Tansel,et al.  Modeling micro-end-milling operations. Part I: analytical cutting force model , 2000 .