A new strategy for Tool Condition Monitoring of small diameter twist drills in deep-hole drilling

Abstract Tool condition monitoring (TCM) systems employed in industry are mostly used to detect tool fracture, although the prevention of it should be the principal aim. This would not only allow for the avoidance of any fracture-related damage to both the workpiece and machine tool, but also for recondition the tool for further use. This paper presents a strategy, which utilises several features extracted from the spindle power and acoustic emission (AE-RMS) signals recorded when drilling small deep holes using twist drills in order to predict an imminent tool failure. A key to achieving this is the subdivision of the drilling cycle into sections and only monitoring those sections in which the most significant change occurs over the tool life. By doing this it is possible to identify the final (i.e. tertiary) tool life stage and replace the worn out tool shortly before fracture occurs, thus improving the overall tool utilisation to. Of 24 drills tested, the TCM system was able to utilise an average of 84% of the tool life; in only one case it failed to detect tool breakage.

[1]  T. P. Wilks,et al.  Performance evaluation of TiN-coated twist drills using force measurement and microscopy , 1993 .

[2]  J. Tlusty,et al.  A Critical Review of Sensors for Unmanned Machining , 1983 .

[3]  Yinghong Peng,et al.  Computational modeling and control system of continuous casting process , 2007 .

[4]  Robert Heinemann,et al.  Use of process signals for tool wear progression sensing in drilling small deep holes , 2007 .

[5]  Mamoru Mitsuishi,et al.  In-Process Prediction and Prevention of the Breakage of Small Diameter Drills Based on Theoretical Analysis , 1994 .

[6]  Robert Heinemann,et al.  The Performance of Small Diameter Twist Drills in Deep-Hole Drilling , 2006 .

[7]  Y. G. Srinivasa,et al.  Acoustic emission for tool condition monitoring in metal cutting , 1997 .

[8]  Ruxu Du,et al.  Signal understanding and tool condition monitoring , 1999 .

[9]  A. Mardapittas,et al.  Characteristics of acoustic emission in drilling , 1994 .

[10]  Kenneth A. Loparo,et al.  Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs) , 2001 .

[11]  S. Söderberg,et al.  Performance and failure of high speed steel drills related to wear , 1982 .

[12]  E.J.A. Armarego.  Some fundamental and practical aspects of twist drills and drilling , 1994 .

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

[14]  Hwa-Young,et al.  Chip Disposal State Monitoring in Drilling Using Neural Network , 2002 .

[15]  Yiannis Kavaratzis.  Deep hole drilling with twist drills: aspects of the CNC process and its real time monitoring and adaptive control , 1990 .

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

[17]  W. König,et al.  Tool monitoring of small drills with acoustic emission , 1992 .