In the laser welding of thin Al/Cu sheets, proper penetration depth and wide interface bead width ensure stable joint strength and low electrical conductance. In this study, we proposed deep learning models to predict the penetration depth. The inputs for the prediction models were 500 Hz-sampled low-cost charge-coupled device (CCD) camera images and 100 Hz-sampled spectral signals. The output was the penetration depth estimated from the keyhole depth measured coaxially using optical coherence tomography. A unisensor model using a CCD image and a multisensor model using a CCD image and the spectrometer signal were proposed in this study. The input and output of the data points were resampled at 100 and 500 Hz, respectively. The 500 Hz models showed better performance than the 100 Hz models, and the multisensor models more accurately predicted the penetration depth than the unisensor models. The most accurate model had a coefficient of determination (R2) of 0.999985 and mean absolute error of 0.02035 mm in the model test. It was demonstrated that low-cost sensors can successfully predict the penetration depth during Al/Cu laser welding.
REFERENCES
1.
S. S.
Lee
, T. H.
Kim
, S. J.
Hu
, W. W.
Cai
, and J. A.
Abell
, “Joining technologies for automotive lithium-ion battery manufacturing: A review
,” in Proceedings of the ASME 2010 International Manufacturing Science and Engineering Conference
, Erie, Pennsylvania
, 12–15 October 2010 (ASME, New York, 2010
), pp. 541
–549
.2.
M.
Kang
, T.
Park
, C.
Kim
, and J.
Kim
, “Laser welding characteristics of aluminum and copper sheets for lithium-ion batteries
,” J. Korean Weld. Join. Soc.
31
, 58
–64
(2013
). 3.
T.
Solchenbach
, P.
Plapper
, and W.
Cai
, “Electrical performance of laser braze-welded aluminum–copper interconnects
,” J. Manuf. Process.
16
, 183
–189
(2014
). 4.
A.
Das
, D.
Li
, D.
Williams
, and D.
Greenwood
, “Joining technologies for automotive battery systems manufacturing
,” World Electr. Veh. J.
9
, 22
(2018
). 5.
M.
Kang
, W.
Choi
, and S.
Kang
, “Ultrasonic and laser welding technologies on Al/Cu dissimilar materials for the lithium-ion battery cell or module manufacturing
,” J. Weld. Join.
37
, 52
–59
(2019
). 6.
S.
Shawn Lee
, T.
Hyung Kim
, S.
Jack Hu
, W. W.
Cai
, J. A.
Abell
, and J.
Li
, “Characterization of joint quality in ultrasonic welding of battery tabs
,” J. Manuf. Sci. Eng.
135
, 021004
(2013
). 7.
S.
Shawn Lee
, T.
Hyung Kim
, S.
Jack Hu
, W. W.
Cai
, and J. A.
Abell
, “Analysis of weld formation in multilayer ultrasonic metal welding using high-speed images
,” J. Manuf. Sci. Eng.
137
, 031016
(2015
). 8.
M. J.
Brand
, P. A.
Schmidt
, M. F.
Zaeh
, and A.
Jossen
, “Welding techniques for battery cells and resulting electrical contact resistances
,” J. Energy Storage
1
, 7
–14
(2015
). 9.
P. A.
Schmidt
, P.
Schmitz
, and M. F.
Zaeh
, “Laser beam welding of electrical contacts for the application in stationary energy storage devices
,” J. Laser Appl.
28
, 022423
(2016
). 10.
Y.-S.
Duh
, K. H.
Lin
, and C.-S.
Kao
, “Experimental investigation and visualization on thermal runaway of hard prismatic lithium-ion batteries used in smart phones
,” J. Therm. Anal. Calorim.
132
, 1677
–1692
(2018
). 11.
H.
Saariluoma
, A.
Piiroinen
, A.
Unt
, J.
Hakanen
, T.
Rautava
, and A.
Salminen
, “Overview of optical digital measuring challenges and technologies in laser welded components in EV battery module design and manufacturing
,” Batteries
6
, 47
(2020
). 12.
P.
Plapper
, T.
Solchenbach
, and W.
Cai
, “Laser beam joining for Li-ion battery module assembly
,” in Proceedings in the European Automotive Laser Applications
, Bad Nauheim
, 19–20 February 2013 (Automotive Circle, Germany, 2013
).13.
K.
Lee
, S.
Kang
, M.
Kang
, S.
Yi
, S.
Hyun
, and C.
Kim
, “Modeling of laser welds using machine learning algorithm part I: Penetration depth for laser overlap Al/Cu dissimilar metal welds
,” J. Weld. Join.
39
, 27
–35
(2021
). 14.
H.
You
, M.
Kang
, S.
Yi
, S.
Hyun
, and C.
Kim
, “Modeling of laser welds using machine learning algorithm part II: Geometry and mechanical behaviors of laser overlap welded high strength steel sheets
,” J. Weld. Join.
39
, 36
–44
(2021
). 15.
E. A.
Algehyne
, T.
Saeed
, M.
Ibrahim
, A. S.
Berrouk
, and Y.-M.
Chu
, “Investigation of dissimilar laser welding of stainless steel 304 and copper using the artificial neural network model
,” J. Laser Appl.
33
, 022010
(2021
). 16.
C.-H.
Kim
and D.-C.
Ahn
, “Coaxial monitoring of keyhole during Yb:YAG laser welding
,” Opt. Laser Technol.
44
, 1874
–1880
(2012
). 17.
M.
Seibold
, K.
Schricker
, and J. P.
Bergmann
, “Characterization of optical spectrum in laser beam welding of dissimilar aluminum-copper joints and time-dependent correlation to process stages
,” in Proceedings in the International Symposium on Photonics and Education
, Jena
, Germany, 17–19 September 2019 (SPIE, Washington, 2019
), p. 111440U
.18.
K.
Mathivanan
and P.
Plapper
, “Laser welding of dissimilar copper and aluminum sheets by shaping the laser pulses
,” Proc. Manuf.
36
, 154
–162
(2019
). 19.
K.
Lee
, S.
Kang
, M.
Kang
, S.
Yi
, and C.
Kim
, “Estimation of Al/Cu laser weld penetration in photodiode signals using deep neural network classification
,” J. Laser Appl.
33
, 042009
(2021
). 20.
K.
Mathivanan
and P.
Plapper
, “Laser welding of copper to aluminum with spiral trajectory and identification of excessive aluminum melting
,” J. Laser Appl.
34
, 012023
(2022
). 21.
B.
Zhang
, K.-M.
Hong
, and Y. C.
Shin
, “Deep-learning-based porosity monitoring of laser welding process
,” Manuf. Lett.
23
, 62
–66
(2020
). 22.
C.
Stadter
, M.
Schmoeller
, M.
Zeitler
, V.
Tueretkan
, U.
Munzert
, and M. F.
Zaeh
, “Process control and quality assurance in remote laser beam welding by optical coherence tomography
,” J. Laser Appl.
31
, 022408
(2019
). 23.
M.
Sokolov
, P.
Franciosa
, T.
Sun
, D.
Ceglarek
, V.
Dimatteo
, A.
Ascari
, A.
Fortunato
, and F.
Nagel
, “Applying optical coherence tomography for weld depth monitoring in remote laser welding of automotive battery tab connectors
,” J. Laser Appl.
33
, 012028
(2021
). 24.
See https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.resample.html?highlight=resample#scipy.signal.resample for the up-sampling algorithm using the Fourier method.
25.
See https://physics.nist.gov/PhysRefData/Handbook/Tables/coppertable2.htm for the copper emission wavelengths.
26.
F.
Chollet
, Deep Learning with Python
(Manning Publications Co
, Shelter Island, New York, 2018
).27.
© 2022 Author(s). Published under an exclusive license by Laser Institute of America.
2022
Author(s)
You do not currently have access to this content.
