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Research Papers

Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes

[+] Author and Article Information
S. Shawn Lee, Chenhui Shao, Tae Hyung Kim, S. Jack Hu, Elijah Kannatey-Asibu

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

Wayne W. Cai, J. Patrick Spicer, Jeffrey A. Abell

Manufacturing Systems Research Laboratory,
General Motors R&D Center,
Warren, MI 48090

1Now with Hyundai Motor Company.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received March 3, 2014; final manuscript received July 18, 2014; published online August 12, 2014. Assoc. Editor: Z. J. Pei.

J. Manuf. Sci. Eng 136(5), 051019 (Aug 12, 2014) (10 pages) Paper No: MANU-14-1088; doi: 10.1115/1.4028059 History: Received March 03, 2014; Revised July 18, 2014

Online process monitoring in ultrasonic welding of automotive lithium-ion batteries is essential for robust and reliable battery pack assembly. Effective quality monitoring algorithms have been developed to identify out of control parts by applying purely statistical classification methods. However, such methods do not provide the deep physical understanding of the manufacturing process that is necessary to provide diagnostic capability when the process is out of control. The purpose of this study is to determine the physical correlation between ultrasonic welding signal features and the ultrasonic welding process conditions and ultimately joint performance. A deep understanding in these relationships will enable a significant reduction in production launch time and cost, improve process design for ultrasonic welding, and reduce operational downtime through advanced diagnostic methods. In this study, the fundamental physics behind the ultrasonic welding process is investigated using two process signals, weld power and horn displacement. Several online features are identified by examining those signals and their variations under abnormal process conditions. The joint quality is predicted by correlating such online features to weld attributes such as bond density and postweld thickness that directly impact the weld performance. This study provides a guideline for feature selection and advanced diagnostics to achieve a reliable online quality monitoring system in ultrasonic metal welding.

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Figures

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Fig. 1

Ultrasonic metal welding system and sensor signal acquisition

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Fig. 2

Optical micrographs with two main bonding mechanisms for ultrasonic metal welds: (a) metallurgical bonding; and (b) mechanical interlocking [3]

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Fig. 3

Influence of weld time on (a) weld strength obtained from U-tensile test; (b) bond density; and (c) postweld thickness under different levels of contamination (level 0: cleaned with isopropyl alcohol, level 1: one drop of vanishing oil, level 2: two drops of vanishing oil)

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Fig. 4

Power signal variation over time: (a) power profile for a single welding cycle; and (b) continuous cross section images at the weld interface during welding cycle

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Fig. 5

LVDT signal: (a) horn displacement; (b) cross section images at the top of metal surface illustrating material filling behavior that corresponds to the displacements shown in (a)

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Fig. 6

Variation of (a) power signal and (b) displacement signal for different levels of surface contamination

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Fig. 7

Optical micrographs showing weld line formation with welding time of (a) 0.1 s, (b) 0.2 s, (c) 0.3 s, and (d) 0.4 s

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Fig. 8

Optical micrographs at the weld interface for three levels of surface contamination: (a) level 0 (clean); (b) level 1; and (c) level 2

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Fig. 9

Features in power and displacement signals

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Fig. 10

Relationship between weld performance and signal features: (a) total energy (Etotal); (b) midpoint energy (Emid); (c) total material compaction (Dtotal); and (d) midpoint material compaction (Dmid)

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Fig. 11

Effect of welding time on: (a) total energy (Etotal); and (b) total material compaction (Dtotal)

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Fig. 12

Relationship between weld attributes and power signal features: (a) bond density versus total energy (Etotal); (b) postweld thickness versus total energy (Etotal); (c) bond density versus midpoint energy (Emid); and (c) postweld thickness versus midpoint energy (Emid)

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Fig. 13

Relationship between weld attributes and displacement signal features: (a) bond density versus total material compaction (Dtotal); (b) postweld thickness versus total material compaction (Dtotal); (c) bond density versus midpoint material compaction (Dmid); and (c) postweld thickness versus midpoint material compaction (Dmid)

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Fig. 14

Relationship between signal features: (a) midpoint material compaction (Dmid) versus midpoint energy (Emid); (b) total material compaction (Dtotal) versus total energy (Etotal)

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