0
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.

Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
Jantunen, E. , 2002, “ A Summary of Methods Applied to Tool Condition Monitoring in Drilling,” Int. J. Mach. Tools Manuf., 42(9), pp. 997–1010. [CrossRef]
Cook, N. H. , 1973, “ Tool Wear and Tool Life,” ASME J. Manuf. Sci. Eng., 95(4), pp. 931–938.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Kurada, S. , and Bradley, C. , 1997, “ A Machine Vision System for Tool Wear Assessment,” Tribol. Int., 30(4), pp. 295–304. [CrossRef]
Kurada, S. , and Bradley, C. , 1997, “ A Review of Machine Vision Sensors for Tool Condition Monitoring,” Comput. Ind., 34(1), pp. 55–72. [CrossRef]
Lanzetta, M. , 2001, “ A New Flexible High-Resolution Vision Sensor for Tool Condition Monitoring,” J. Mater. Process. Technol., 119(1), pp. 73–82. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Wu, C. J. , and Hamada, M. S. , 2011, Experiments: Planning, Analysis, and Optimization, Wiley, New York.
Fisher, R. A. , 1936, “ The Use of Multiple Measurements in Taxonomic Problems,” Ann. Eugen., 7(2), pp. 179–188. [CrossRef]
Duda, R. O. , Hart, P. E. , and Stork, D. G. , 2012, Pattern Classification, Wiley, New York.
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. [CrossRef] [PubMed]
Suykens, J. A. , and Vandewalle, J. , 1999, “ Least Squares Support Vector Machine Classifiers,” Neural Process. Lett., 9(3), pp. 293–300. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

A typical ultrasonic metal welding system [2]

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

Ultrasonic welding mechanism

Grahic Jump Location
Fig. 4

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

Grahic Jump Location
Fig. 5

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

Grahic Jump Location
Fig. 6

Anvil knurl wear progression in the horizontal direction [6]

Grahic Jump Location
Fig. 7

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

Grahic Jump Location
Fig. 8

Anvil knurl wear progression in the vertical direction [6]

Grahic Jump Location
Fig. 9

Flowchart for impression method

Grahic Jump Location
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

Grahic Jump Location
Fig. 11

Process flowchart for feature extraction

Grahic Jump Location
Fig. 12

Shoulder width calculation

Grahic Jump Location
Fig. 13

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

Grahic Jump Location
Fig. 14

Feature trend versus the number of welds

Grahic Jump Location
Fig. 15

Fisher's ratio for all features

Grahic Jump Location
Fig. 16

Scatter plots of selected features

Grahic Jump Location
Fig. 17

Simulated profiles for worn tools

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In