This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a monitoring algorithm using a quadratic classifier and features that are extracted from space and frequency domains of cross-sectional profiles on tool surfaces. The developed algorithm is validated using tool measurement data from a battery plant.

References

References
1.
Kim
,
T.
,
Yum
,
J.
,
Hu
,
S.
,
Spicer
,
J.
, and
Abell
,
J.
,
2011
, “
Process Robustness of Single Lap Ultrasonic Welding of Thin, Dissimilar Materials
,”
CIRP Ann. Manuf. Technol.
,
60
(
1
), pp.
17
20
.
2.
Shao
,
C.
,
Paynabar
,
K.
,
Kim
,
T. H.
,
Jin
,
J. J.
,
Hu
,
S. J.
,
Spicer
,
J. P.
,
Wang
,
H.
, and
Abell
,
J. A.
,
2013
, “
Feature Selection for Manufacturing Process Monitoring Using Cross-Validation
,”
J. Manuf. Syst.
,
32
(
4
), pp.
550
555
.
3.
Lee
,
S. S.
,
Shao
,
C.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Kannatey-Asibu
,
E.
,
Cai
,
W. W.
,
Spicer
,
J. P.
, and
Abell
,
J. A.
,
2014
, “
Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes
,”
ASME J. Manuf. Sci. Eng.
,
136
(
5
), p.
051019
.
4.
Lee
,
S. S.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Cai
,
W. W.
,
Abell
,
J. A.
, and
Li
,
J.
,
2013
, “
Characterization of Joint Quality in Ultrasonic Welding of Battery Tabs
,”
ASME J. Manuf. Sci. Eng.
,
135
(
2
), p.
021004
.
5.
Lee
,
S. S.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Cai
,
W. W.
, and
Abell
,
J. A.
,
2015
, “
Analysis of Weld Formation in Multilayer Ultrasonic Metal Welding Using High-Speed Images
,”
ASME J. Manuf. Sci. Eng.
,
137
(
3
), p.
031016
.
6.
Shao
,
C.
,
Guo
,
W.
,
Kim
,
T. H.
,
Jin
,
J. J.
,
Hu
,
S. J.
,
Spicer
,
J. P.
, and
Abell
,
J. A.
,
2014
, “
Characterization and Monitoring of Tool Wear in Ultrasonic Metal Welding
,”
9th International Workshop on Microfactories
, pp.
161
169
.
7.
Jantunen
,
E.
,
2002
, “
A Summary of Methods Applied to Tool Condition Monitoring in Drilling
,”
Int. J. Mach. Tools Manuf.
,
42
(
9
), pp.
997
1010
.
8.
Cook
,
N. H.
,
1973
, “
Tool Wear and Tool Life
,”
ASME J. Manuf. Sci. Eng.
,
95
(
4
), pp.
931
938
.
9.
Koren
,
Y.
,
Ko
,
T.-R.
,
Ulsoy
,
A. G.
, and
Danai
,
K.
,
1991
, “
Flank Wear Estimation Under Varying Cutting Conditions
,”
ASME J. Dyn. Syst. Meas. Control
,
113
(
2
), pp.
300
307
.
10.
Abellan-Nebot
,
J. V.
, and
Subirón
,
F. R.
,
2010
, “
A Review of Machining Monitoring Systems Based on Artificial Intelligence Process Models
,”
Int. J. Adv. Manuf. Technol.
,
47
(
1–4
), pp.
237
257
.
11.
Rehorn
,
A. G.
,
Jiang
,
J.
, and
Orban
,
P. E.
,
2005
, “
State-of-the-Art Methods and Results in Tool Condition Monitoring: A Review
,”
Int. J. Adv. Manuf. Technol.
,
26
(
7–8
), pp.
693
710
.
12.
Zhou
,
J.-H.
,
Pang
,
C. K.
,
Zhong
,
Z.-W.
, and
Lewis
,
F. L.
,
2011
, “
Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification
,”
IEEE Trans. Instrum. Meas.
,
60
(
2
), pp.
547
559
.
13.
Ertunc
,
H. M.
,
Loparo
,
K. A.
, and
Ocak
,
H.
,
2001
, “
Tool Wear Condition Monitoring in Drilling Operations Using Hidden Markov Models (HMMs)
,”
Int. J. Mach. Tools Manuf.
,
41
(
9
), pp.
1363
1384
.
14.
Dimla
,
D. E.
,
2000
, “
Sensor Signals for Tool-Wear Monitoring in Metal Cutting Operations—A Review of Methods
,”
Int. J. Mach. Tools Manuf.
,
40
(
8
), pp.
1073
1098
.
15.
Kurada
,
S.
, and
Bradley
,
C.
,
1997
, “
A Machine Vision System for Tool Wear Assessment
,”
Tribol. Int.
,
30
(
4
), pp.
295
304
.
16.
Kurada
,
S.
, and
Bradley
,
C.
,
1997
, “
A Review of Machine Vision Sensors for Tool Condition Monitoring
,”
Comput. Ind.
,
34
(
1
), pp.
55
72
.
17.
Lanzetta
,
M.
,
2001
, “
A New Flexible High-Resolution Vision Sensor for Tool Condition Monitoring
,”
J. Mater. Process. Technol.
,
119
(
1
), pp.
73
82
.
18.
Byrne
,
G.
,
Dornfeld
,
D.
,
Inasaki
,
I.
,
Ketteler
,
G.
,
König
,
W.
, and
Teti
,
R.
,
1995
, “
Tool Condition Monitoring (TCM)—The Status of Research and Industrial Application
,”
CIRP Ann. Manuf. Technol.
,
44
(
2
), pp.
541
567
.
19.
Kuttolamadom
,
M.
,
Mehta
,
P.
,
Mears
,
L.
, and
Kurfess
,
T.
,
2015
, “
Correlation of the Volumetric Tool Wear Rate of Carbide Milling Inserts With the Material Removal Rate of ti–6al–4v
,”
ASME J. Manuf. Sci. Eng.
,
137
(
2
), p.
021021
.
20.
Kuttolamadom
,
M. A.
,
Mears
,
M. L.
, and
Kurfess
,
T. R.
,
2015
, “
The Correlation of the Volumetric Wear Rate of Turning Tool Inserts With Carbide Grain Sizes
,”
ASME J. Manuf. Sci. Eng.
,
137
(
1
), p.
011015
.
21.
Kang
,
J.
,
Park
,
I.
,
Jae
,
J.
, and
Kang
,
S.
,
1999
, “
A Study on a Die Wear Model Considering Thermal Softening: (i) Construction of the Wear Model
,”
J. Mater. Process. Technol.
,
96
(
1
), pp.
53
58
.
22.
Kang
,
J.
,
Park
,
I.
,
Jae
,
J.
, and
Kang
,
S.
,
1999
, “
A Study on Die Wear Model Considering Thermal Softening (ii): Application of the Suggested Wear Model
,”
J. Mater. Process. Technol.
,
94
(
2
), pp.
183
188
.
23.
Lepadatu
,
D.
,
Hambli
,
R.
,
Kobi
,
A.
, and
Barreau
,
A.
,
2006
, “
Statistical Investigation of Die Wear in Metal Extrusion Processes
,”
Int. J. Adv. Manuf. Technol.
,
28
(
3–4
), pp.
272
278
.
24.
Kong
,
L. X.
, and
Nahavandi
,
S.
,
2002
, “
On-Line Tool Condition Monitoring and Control System in Forging Processes
,”
J. Mater. Process. Technol.
,
125–126
, pp.
464
470
.
25.
Wu
,
C. J.
, and
Hamada
,
M. S.
,
2011
,
Experiments: Planning, Analysis, and Optimization
,
Wiley
,
New York
.
26.
Fisher
,
R. A.
,
1936
, “
The Use of Multiple Measurements in Taxonomic Problems
,”
Ann. Eugen.
,
7
(
2
), pp.
179
188
.
27.
Duda
,
R. O.
,
Hart
,
P. E.
, and
Stork
,
D. G.
,
2012
,
Pattern Classification
,
Wiley
,
New York
.
28.
Zhang
,
M.
,
1997
, “
Identification of Protein Coding Regions in the Human Genome by Quadratic Discriminant Analysis
,”
Proc. Natl. Acad. Sci. U. S. A.
,
94
(
2
), pp.
565
568
.
29.
Suykens
,
J. A.
, and
Vandewalle
,
J.
,
1999
, “
Least Squares Support Vector Machine Classifiers
,”
Neural Process. Lett.
,
9
(
3
), pp.
293
300
.
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