General Survey of How Drill Wear is Measured
(Reference:Papers about Online Drill Wear Monitoring)
1. “Applications of time-frequency analysis to signals from manufacturing
and machine monitoring sensors” by Les. E. Atlas, et al:
-They cite an experiment where drill wear appears to be determined by measuring
the diameter of the hole drilled. As the drill became worn (specifically, after
it chipped) the whole diameter increased significantly. Exact values in their
holes were from .4127 - .4165; close to the size of the holes we are drilling.
(see graph)
2. “A decision fusion algorithm for tool wear condition monitoring
in drilling” by H.M. Ertunc and K.A. Loparo:
-They focus their monitoring efforts on power and force signals, and develop
a method for combining the output of several different detectors. They use outer
corner wear as their criteria. “Different types of drill wear…can
be observed on the drill because the geometry of the drill and the cutting conditions
vary along the cutting lips from the margin to the chisel edge. However, maximum
wear occurs at the corners of the cutting lips because these are the locations
of the highest cutting speed. Therefore in this work, outer corner wear was
selected as the criterion..”
3. “Tool wear condition monitoring in drilling operations using hidden
Markov models (HMMs)” by by H.M. Ertunc and K.A. Loparo:
-Ertunc feels our pain. As he puts it, “the determination of a reliable
criterion that is proportional to tool wear under all cutting conditions is
the main problem for both indirect and direct methods” of tool condition
monitoring. Our removing the drill and visually measuring it is one of the direct
methods he mentions: “In the drilling operation, a direct measurement
of tool wear such as the worn area on the tool cutting lip (edge) can be obtained…”
That’s our method. Here’s another suggestion: “By analyzing
the image of the tool surface, one can characterize and asses the condition
of the tool—because tool wear is a deformation of the tool surface and
the intensity of the light reflected from the worn sections of the drill surface
changes as the drill tool wears.”
4. “Drill wear monitoring based on current signals” by Xiaoli
Li and S.K. Tso:
-Li and Tso tried to develop a model based on current speed. Instead of measuring
the drill as wear progressed, they artificially applied specific amounts of
flank wear (0.2, 0.5, and 0.8mm) to each drill and recorded the data only at
these points. They did three trials each with three diameters of drills with
the three wear amounts. So I think they might have used 27 separate drills,
unless they drilled once at 0.2, then shaved it to 0.5, etc.
5. “Real-time tool condition monitoring using wavelet transforms and
fuzzy techniques” by Xiaoli Li, Shiu Kit Tso:
-same author, same method; artificially apply specific amounts of wear
6. “Tool condition monitoring in drilling using vibration signature
analysis” by Elbestawi, et al.
-They make reference to experiments where different types of wear were artificially
induced. However, the somewhat random wear values in their figures (like 0.608mm
or 0.824mm) suggest to me that they measured the actual wear. But they never
explicitly say how they did this.
7. “Frequency and Time Doamin Analyses of Sensor Signals in Drilling-1.
Corrrelation with Drill Wear” by A. Noori-Khajavi and R. Komanduri.
-It seems that they use the same technique as we do, i.e. measuring drill wear
at the corner of the drill after cutting a fixed number of holes (Off-line Monitoring).
Parameter for Worn Tool - .3mm
8. “Monitoring Drilling Wear States by a Fuzzy Pattern Recognition
Technique” by P.G. Li and S.M. Wu.
-They state indirect tool wear measurement techniques to sense drill wear. These
techniques include the monitoring of the thrust force and torque which increases
as drill wear increases.
-Limitations: Unsuitable to use these methods in automated manufacturing environment
because of various cutting conditions.
9. “Online Detection of Drill Wear” by T.I. Liu and S.M. Wu
-They used the concept of thrust wear and changes in acceleration to measure
drill wear for normal drills.
- (Multifaceted Drill) MFD: To measure the wear of this particular drill a vision
system was developed which involved illumination of the entire drill point.
The flank wear was distinguishable because of the higher reflectivity of the
worn area compared to that of the unworn area.
10. “Online Hole Quality Evaluation For Drilling Composite Material
Using Dynamic Data” by T. Radhakrishnan:
- Analyses of the Drilling Thrust and the corresponding quality of the hole
(Drill Wear). The thrust and the torque signals were measured. The surface of
the holes was measured using a “Talysurf” instrument. The signals
were digitalized and the static characteristics such as mean and peak thrust/torque
were determined. The variation in the peaks was used to determine the hole quality.
11. “A Summary of Methods Applied to Tool Conditioning Monitoring
in Drilling” by Erkki Jantunen:
- Same method involving thrust, drift and feed force.
12 “In-Process Prediction of Corner Wear in Drilling Operation”
by H.S. Liu, B.Y. Lee and Y.S. Tang:
-To measure corner they have a developed a polynomial network which uses thrust
force sensing signals. Average Absolute between the measured corner wear and
the predicted corner wear using the thrust force is less than 10%.
13. “Prediction of Tool Fracture in Drilling” by E.Brinksmeier:
-Involved analyzing the process forces, the power consumption of the driving
motor, the dynamic torque and vibration phenomena. Not recommended since to
measure torque and feeding force, special clutches need to be installed to the
main spindle which will interfere with the machine tool design.
14. “Sensing of Drill Wear and Prediction of Drill Life” by K.
Subramanian:
-Involves the same method as we use except with a 5x magnification.
The wear was measured by the taking 4 averages i.e. 2 at the bottom of the flank,
and
2 at the top of the flank.
******** This could help us give accurate measurements*******
***************Suggested wear cut-off .15mm**************
15. “Intelligent Classification and Measurement of Drill Wear”
by K.S. Anantharaman:
- Same as above except used 6 averages.
16 “Intelligent Real-Time Predictive Diagnostics for Cutting Tools
and Supervisory Control of Machining Operations”. By K Ramamurthi
and C.L. Hough. Jr:
-The wear on the cutting edges was measured using Nixon Microscope. The drill
was defined to be as worn if the flank wear on both lips exceeded .6 mm for
.25 in. tools and 0.3mm for 0.125 in. tools.
Note: Such measurements were based on definitions that are approximate, subjective
and general. Does this mean that the researchers were not sure how much they
should base their results on the actual measurements?
17 “Signature Analysis Applied to Drilling” by S.Braun, E. Lenz
and C.L. Wung:
-Use of a Kistler 4 component dynamometer. The relations between condition of
the
wear and the thrust force Z, torque M, and two drifting forces ‘x’
and ‘y’. Results indicate no relation between wear propagation and
the Z and M forces. The drifting forces indicate the amount of force, which
indicates the level of wear on the drill.
A diagram used to plot different forces and the level of wear.
18 “Self-Organizing Neural Network Application to Drill Wear classification”
by E. Govekar and I. Grabec:
-Similar method of ours.
19 “A fuzzy logic Approach For Multi-Sensor Process Monitoring in
Machining’’ By S.Li, M.A. Elbestawi and R.X. Du:
-Uninterrupted cutting was produced by machining a work-piece with 2 slots
along the feed direction. Tool breakage was identified by a chipping area on
the tool than 0.04mm^2.
Slight Wear – Average Flank between 0.1mm and 0.16 mm.
Medium Wear – Drill Wear between 0.16mm and 0.3mm.
Severe Wear, - Drill wear above 0.3mm
20 “Tool Wear Monitoring in Machining Processes Through Wavelet Analysis”
by Litao Wang, Mostafa G. Mehrabi, and Elijah Kasnnatey Asibu Jr:
-They use0.3mm as the cut-off for tool wear and 0.6mm height cut-off.
This limit according to the paper is in accordance with the criteria recommended
by ISO 3685.