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Research Papers

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

[+] Author and Article Information
Chenhui Shao

Department of Mechanical Engineering,
University of Michigan,
Ann Arbor, MI 48109
e-mail: chshao@umich.edu

Tae Hyung Kim, S. Jack Hu

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

Jionghua (Judy) Jin

Department of Industrial and
Operations Engineering,
University of Michigan,
Ann Arbor, MI 48109

Jeffrey A. Abell, J. Patrick Spicer

Manufacturing Systems Research Laboratory,
General Motors Technical Center,
Warren, MI 48090

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received February 17, 2015; final manuscript received July 28, 2015; published online November 18, 2015. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 138(5), 051005 (Nov 18, 2015) (8 pages) Paper No: MANU-15-1087; doi: 10.1115/1.4031677 History: Received February 17, 2015; Revised July 28, 2015

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.

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References

Figures

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Fig. 1

A typical ultrasonic metal welding system [2]

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Fig. 2

Pyramid-shape knurls on the horn and anvil: (a) Horn knurl and (b) anvil knurl

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Fig. 3

Ultrasonic welding mechanism

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Fig. 4

Optical images of different wear stages [6]: (a) Stage 1, (b) stage 2, (c) stage 3, and (d) stage 4

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Fig. 5

Cross-sectional profiles in the horizontal direction [6]: (a) Stage 1, (b) stage 2, (c) stage 3, and (d) stage 4

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Fig. 6

Anvil knurl wear progression in the horizontal direction [6]

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Fig. 7

Cross-sectional profiles in the vertical direction [6]: (a) Stage 1, (b) stage 2, (c) stage 3, and (d) stage 4

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Fig. 8

Anvil knurl wear progression in the vertical direction [6]

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Fig. 9

Flowchart for impression method

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Fig. 10

Comparison between measurements of a tool and a coupon: (a) anvil image, (b) coupon image, (c) comparison of horizontal profiles, and (d) comparison of vertical profiles

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Fig. 11

Process flowchart for feature extraction

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Fig. 12

Shoulder width calculation

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Fig. 13

Frequency features for different stages of wear: (a) Profiles in the space domain and (b) frequency-domain features

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Fig. 14

Feature trend versus the number of welds

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Fig. 15

Fisher's ratio for all features

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Fig. 16

Scatter plots of selected features

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Fig. 17

Simulated profiles for worn tools

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