EVALUATION OF SENSOR-BASED CONDITION MONITORING METHODS AS IN-PROCESS TOOL WEAR AND BREAKAGE INDICES - CASE STUDY: DRILLING

John A. TSANAKAS, Pantelis N. BOTSARIS, Iraklis G. AMIRIDS, Georgios G. GALERIDIS Democritus University of Thrace, School of Engineering Department of Production Engineering and Management Faculty of Materials, Processes and Engineering Xanthi, 67100, Thrace, Greece Telephone (and Telefax): +302541079878 itsanaka@ee.duth.gr , panmpots@pme.duth.gr , irakamoi@pme.duth.gr , ggalerid@xan.duth.gr Summary Today, effective unmanned machining operations and automated manufacturing are unthinkable without tool condition monitoring (TCM). Undoubtedly, the implementation of an adaptable, reliable TCM and its successful employment in industry, emerge as major instigations over the recent years. In this work, a sensor-based approach was deployed for the in-process monitoring and detection of tool wear and breakage in drilling. In particular, four widely reported indirect methods for tool wear monitoring, i.e. vibration signals together with thermal signatures, spindle motor and feed motor current measurements were obtained during numerous drillings, under fixed conditions. The acquired raw data was, then, processed both statistically and in the frequency domain, in order to distinguish the meaningful information. The study of the latter is influential in identifying the trend of specific signals toward tool wear mechanism. The efficiency of this information as a tool wear and/or breakage index is the feature that determines the effectiveness and reliability of a potential indirect TCM approach based on a multisensor integration. The paper concludes with a discussion of both advantages and limitations of this effort, stressing the necessity to develop simple, fast condition monitoring methods which are, generally, less likely to fail. Key words: tool condition monitoring, vibration signals, thermal signatures, spindle motor current, tool wear, statistical analysis, frequency domain analysis.

[1]  Zhijun Wang,et al.  Feature-filtered fuzzy clustering for condition monitoring of tool wear , 1996, J. Intell. Manuf..

[2]  Paul W. Prickett,et al.  Machine tool condition monitoring system using tooth rotation energy estimation (TREE) technique , 2005, 2005 IEEE Conference on Emerging Technologies and Factory Automation.

[3]  Jun Wang,et al.  Real-time tool condition monitoring using wavelet transforms and fuzzy techniques , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[4]  Erkki Jantunen,et al.  Indirect multisignal monitoring and diagnosis of drill wear , 2006 .

[5]  Snr. D. E. Dimla.  The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation , 2002 .

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

[7]  T. I. Liu,et al.  Intelligent Classification and Measurement of Drill Wear , 1994 .

[8]  Pan Fu,et al.  A neurofuzzy pattern recognition algorithm and its application in tool condition monitoring process , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).

[9]  Erkki Jantunen,et al.  A summary of methods applied to tool condition monitoring in drilling , 2002 .

[10]  Ruxu Du,et al.  Tool condition monitoring in turning using fuzzy set theory , 1992 .

[11]  W. S. Lau,et al.  In-process drill wear and breakage monitoring for a machining centre based on cutting force parameters , 1992 .

[12]  Rubén Morales-Menéndez,et al.  Tool-Wear Monitoring Based on Continuous Hidden Markov Models , 2005, CIARP.

[13]  P. Mathew,et al.  Use of predicted cutting temperatures in determining tool performance , 1989 .

[14]  Paul K. Wright,et al.  Drill wear sensing and failure prediction for untended machining , 1988 .

[15]  Rene de Jesus Romero-Troncoso,et al.  Sensorless tool failure monitoring system for drilling machines , 2006 .

[16]  J. A. Tsanakas,et al.  STATE-OF-THE-ART IN METHODS APPLIED TO TOOL CONDITION MONITORING (TCM) IN UNMANNED MACHINING OPERATIONS: A REVIEW , 2008 .

[17]  T. I. El-Wardany,et al.  Tool condition monitoring in drilling using vibration signature analysis , 1996 .

[18]  S. Balakrishnan,et al.  Predicting tool flank wear using spindle speed change , 1995 .

[19]  Issam Abu-Mahfouz,et al.  Drilling wear detection and classification using vibration signals and artificial neural network , 2003 .

[20]  Pan Fu,et al.  Intelligent Classification of Cutting Tool Wear States , 2006, ISNN.

[21]  M. A. Elbestawi,et al.  Tool Condition Monitoring in Machining , 2006 .

[22]  S. K. Choudhury,et al.  Role of temperature and surface finish in predicting tool wear using neural network and design of experiments , 2003 .

[23]  Shane Y. Hong,et al.  Knowledge-based diagnosis of drill conditions , 1993, J. Intell. Manuf..

[24]  N. Constantinides,et al.  An investigation of methods for the on-line estimation of tool wear , 1987 .

[25]  Jin Jiang,et al.  Erratum to: State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[26]  F. Ferraz Jr,et al.  Data acquisition and monitoring in machine tools with CNC of open architecture using internet , 2005 .