Abstract

Tool condition monitoring is difficult in micro-milling due to irregular wear and chipping of the cutting edges, which lead to unexpected tool breakage. This study demonstrates the use of force data to reliably predict different tool life stages until tool breakage, while micro-milling hard materials like stainless steel (SS304) using tungsten carbide tools of 500 μm diameter. Extensive experiments involving machining of 465 slots over 62 min of machining time were performed in this study. The resulting voluminous force data were analyzed to divide the tool life into three stages based on the variation in the forces and other related features. The first stage is the initial 12.5% of the tool life, second stage consists of 12.5–70% of tool life, and the third stage is from 70% to 100% tool life. The analysis of the tool wear and cutting forces shows that the average tool diameter reduces by 32 μm, 67 μm and 108 μm, and the average resultant cutting force were 2.45 N, 4.17 N, and 4.93 N in stage 1, 2, and 3, respectively. To avoid catastrophic breakage of the tool, the tool life stages are predicted from the force data using machine learning models. Among the machine learning models, random forest method gave a better prediction accuracy of 88.5%. The model was further improved by incorporating the initial cutting edge radius as an additional feature, and the variance in the prediction was seen to drop by 48.76%.

References

References
1.
Bukkapatnam
,
S. T.
,
Kumara
,
S. R.
, and
Lakhtakia
,
A.
,
2000
, “
Fractal Estimation of Flank Wear in Turning
,”
J. Dyn. Sys., Meas. Control
,
122
(
1
), pp.
89
94
. 10.1115/1.482446
2.
Wang
,
Z.
,
Chegdani
,
F.
,
Yalamarti
,
N.
,
Takabi
,
B.
,
Tai
,
B.
,
Mansori
,
M. E.
, and
Bukkapatnam
,
S. T.
,
2020
, “
Acoustic Emission (AE) Characterization of Natural Fiber Reinforced Plastic (NFRP) Composite Machining Using a Random Forest Machine Learning Model
,”
ASME J. Manuf. Sci. Eng.
,
142
(
3
), p.
031003
. 10.1115/1.4045945
3.
Tansel
,
I.
,
Rodriguez
,
O.
,
Trujillo
,
M.
,
Paz
,
E.
, and
Li
,
W.
,
1998
, “
Micro-End-Milling–i. Wear and Breakage
,”
Int. J. Mach. Tools. Manuf.
,
38
(
12
), pp.
1419
1436
. 10.1016/S0890-6955(98)00015-7
4.
Malekian
,
M.
,
Park
,
S. S.
, and
Jun
,
M. B.
,
2009
, “
Tool Wear Monitoring of Micro-Milling Operations
,”
J. Mater. Process. Technol.
,
209
(
10
), pp.
4903
4914
. 10.1016/j.jmatprotec.2009.01.013
5.
Varghese
,
A.
,
Maurya
,
P. K.
,
Kulkarni
,
V.
, and
Joshi
,
S. S.
,
2019
, “
Experimental Investigation of the Correlation Between Surface Roughness and Tool-Life in Micromilling
,”
Adv. Mater. Proces. Technol.
,
5
(
1
), pp.
67
77
.
6.
Jemielniak
,
K.
, and
Arrazola
,
P.
,
2008
, “
Application of AE and Cutting Force Signals in Tool Condition Monitoring in Micro-Milling
,”
CIRP. J. Manuf. Sci. Technol.
,
1
(
2
), pp.
97
102
. 10.1016/j.cirpj.2008.09.007
7.
Wu
,
D.
,
Jennings
,
C.
,
Terpenny
,
J.
,
Gao
,
R. X.
, and
Kumara
,
S.
,
2017
, “
A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests
,”
ASME J. Manuf. Sci. Eng.
,
139
(
7
), p.
071018
. 10.1115/1.4036350
8.
Chen
,
B.
,
Chen
,
X.
,
Li
,
B.
,
He
,
Z.
,
Cao
,
H.
, and
Cai
,
G.
,
2011
, “
Reliability Estimation for Cutting Tools Based on Logistic Regression Model Using Vibration Signals
,”
Mech. Syst. Signal Proces.
,
25
(
7
), pp.
2526
2537
. 10.1016/j.ymssp.2011.03.001
9.
Varghese
,
A.
,
Kulkarni
,
V.
, and
Joshi
,
S. S.
, “
Effect of Tool Condition on Cutting Mechanism in Micromilling
,”
ASME 2019 14th International Manufacturing Science and Engineering Conference
,
Erie, PA
,
June 10–14
.
10.
Griffiths
,
C.
,
Dimov
,
S.
,
Rees
,
A.
,
Dellea
,
O.
,
Gavillet
,
J.
,
Lacan
,
F.
, and
Hirshy
,
H.
,
2013
, “
A Novel Texturing of Micro Injection Moulding Tools by Applying an Amorphous Hydrogenated Carbon Coating
,”
Surf. Coat. Technol.
,
235
, pp.
1
9
. 10.1016/j.surfcoat.2013.07.006
11.
MathWorks
, “
findchangepts
,” https://in.mathworks.com/help/signal/ref/findchangepts.html, Accessed May 3, 2020.
12.
Aslan
,
D.
, and
Altintas
,
Y.
,
2018
, “
On-Line Chatter Detection in Milling Using Drive Motor Current Commands Extracted From CNC
,”
Int. J. Mach. Tools. Manuf.
,
132
, pp.
64
80
. 10.1016/j.ijmachtools.2018.04.007
13.
Zhu
,
K.
,
San Wong
,
Y.
, and
Hong
,
G. S.
,
2009
, “
Multi-Category Micro-Milling Tool Wear Monitoring With Continuous Hidden Markov Models
,”
Mech. Syst. Signal Process.
,
23
(
2
), pp.
547
560
. 10.1016/j.ymssp.2008.04.010
14.
James
,
G.
,
Witten
,
D.
,
Hastie
,
T.
, and
Tibshirani
,
R.
,
2013
,
An Introduction to Statistical Learning
, Vol.
112
,
Springer
,
New York
.
You do not currently have access to this content.