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Research Papers

Characterization of Joint Quality in Ultrasonic Welding of Battery Tabs

[+] Author and Article Information
S. Shawn Lee

e-mail: shawnlee@umich.edu

S. Jack Hu

Department of Mechanical Engineering,
The University of Michigan,
Ann Arbor, MI 48109

Jeffrey A. Abell

General Motors R&D Center,
Warren, MI 48090

Jingjing Li

Department of Mechanical Engineering,
University of Hawaii,
Honolulu, HI 96822

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the Journal of Manufacturing Science and Engineering. Manuscript received December 15, 2011; final manuscript received September 20, 2012; published online March 22, 2013. Assoc. Editor: Wei Li.

J. Manuf. Sci. Eng 135(2), 021004 (Mar 22, 2013) (13 pages) Paper No: MANU-11-1396; doi: 10.1115/1.4023364 History: Received December 15, 2011; Revised September 20, 2012

Manufacturing of lithium-ion battery packs for electric or hybrid electric vehicles requires a significant amount of joining, such as welding, to meet the desired power and capacity needs. However, conventional fusion welding processes, such as resistance spot welding and laser welding, face difficulties in joining multiple sheets of highly conductive, dissimilar materials to create large weld areas. Ultrasonic metal welding overcomes these difficulties by using its inherent advantages derived from its solid-state process characteristics. Although ultrasonic metal welding is well-qualified for battery manufacturing, there is a lack of scientific quality guidelines for implementing ultrasonic welding in volume production. In order to establish such quality guidelines, this paper first identifies a number of critical weld attributes that determine the quality of welds by experimentally characterizing the weld formation over time using copper-to-copper welding as an example. Samples of different weld quality were cross-sectioned and characterized with optical microscopy, scanning electronic microscopy (SEM), and hardness measurements in order to identify the relationship between physical weld attributes and weld performance. A novel microstructural classification method for the weld region of an ultrasonic metal weld is introduced to complete the weld quality characterization. The methodology provided in this paper links process parameters to weld performance through physical weld attributes.

Copyright © 2013 by ASME
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Figures

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Fig. 1

Ultrasonic welding configuration: (a) an example of battery tab joining; (b) dimension and configuration of weld coupons; and (c) cross-section image (AA′)

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Fig. 2

Weld performance testing: (a) U-tensile test configuration and (b) maximum U-tensile load plotted against welding time for three different clamping pressures

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Fig. 3

Optical images of Cu and Ni-plated Cu weld cross-sections produced with a pressure of 50 psi, with increasing welding times (0.2 s, 0.6 s, and 1.0 s)

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Fig. 4

Knurl pattern of sonotrode (left) and its dimension (right)

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Fig. 5

Optical images of the formation process of microbonds and interfacial waves along the bonding line

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Fig. 6

Convoluted bonding line in the weld samples produced in 1.0 s weld time

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Fig. 7

Optical images of Cu and Ni-plated Cu joints produced in 0.4 s weld time: the layer of deformed material (a) overflowing into the space that does not contact with sonotrode tips and (b) starting to flow along the inclined plane

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Fig. 8

Material flow during the welding process: (a) a LVDT signal for 1.0 s process time and (b) optical images of valley areas of the sonotrode knurl pattern for different welding times ((a) 0.2 s; (b) 0.4 s; and (c) 0.6 s)

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Fig. 9

Optical images of Cu and Ni-plated Cu joints with increasing welding time: (a) as-received condition; (b) 0.2 s; (c) 0.4 s; (d) 0.6 s; (e) 0.8 s; and (f) 1.0 s

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Fig. 10

Hardness profile of weld samples for different welding times. The hardness is averaged over the peaks of the sonotrode (dots in the cross-section image).

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Fig. 11

Hardness variation in horizontal locations: (a) horizontal hardness profile of the weld cross-sections and (b) optical images for 0.4 s and 1.0 s

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Fig. 12

Hardness variation in vertical locations: (a) vertical hardness profile of the weld cross-sections and (b) optical images at the valley area of the sonotrode for 0.4 s and 1.0 s

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Fig. 13

Optical images of ultrasonically welded joints made in different weld qualities (i.e., “under,” “good,” and “over” weld)

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Fig. 14

Vertical position of sonotrode obtained from LVDT sensor (upper); percentile ratio of indentation measured from optical cross-section images (lower)

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Fig. 15

Hardness distribution of the weld samples for different weld times: (a) a schematic diagram of ultrasonically welded joint; (b) hardness profile of the weld interface; and (c) hardness profile outside of weld zone

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Fig. 16

Weld region classification: (a) a schematic diagram of weld region classification and (b) optical micrograph of an ultrasonic weld produced in 0.6 s welding time, giving an overview of classified weld regions

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Fig. 17

Optical micrograph of an ultrasonic weld produced in 1.0 s welding time with classified weld regions

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Fig. 18

Classified weld regions associated with failure types: (a) a schematic diagram indicating dimension of each weld region (TMAZ and WN) and (b) half TMAZ size and half WN size over weld time, with failed weldment images after U-tensile test

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Fig. 19

SEM images of deformed surfaces around the weld zone from the “over” weld: (a) a front view image (vibration direction: out-and-in-plane); (b) “island” features; (c) fatigue striation marks; (d) another front view image of the right hand side of image-(a); (e) crack propagations; and (f) microcracks

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Fig. 20

Correlation of weld performance with bond density and postweld thickness

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