journal article·2025-07Open access

Numerical and Experimental Study of High‐Speed Laser Cutting of Copper Current Collectors: Process Optimization for Quality Assessment

Caterina Angeloni, Samuele Piandoro, Erica Liverani, Alessandro Fortunato

Advanced Materials Technologies· Volume 10· Issue 14

Abstract

Abstract Ensuring high‐edge quality in battery current collectors is crucial for improving battery performance and preventing potential safety issues. Defects such as uneven cuts, spatter, and excessive remelted zones can negatively impact the current collectors' electrical conductivity and mechanical integrity. Laser cutting offers advantages over mechanical methods by enabling faster processing, higher precision, and greater energy concentration. This study models and predicts defect occurrence under varying process parameters, focusing on the interaction between a single‐mode continuous‐wave (CW) laser and a copper current collector foil. Key factors influencing edge profile defects and cutting quality are investigated through experimental analysis and numerical simulation. A Computational Fluid Dynamics (CFD) model based on the volume of fluid method identifies parameters affecting the physical phenomena and optimal cutting conditions. Model validation is achieved by comparing experimental results across a range of process parameters associated with distinct defect formation modes. This model enables the prediction of defect types across a wide spectrum of laser speeds (2–25 m s−1), power levels (200–1000 W), and foil thicknesses (8–16 ). Findings serve as a guideline for selecting process parameters when using current collectors of varying materials and thicknesses.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Surface Polishing Techniques · Welding Techniques and Residual Stresses · Advanced Machining and Optimization Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Industrial Engineering (DIN), viale Risorgimento 2 Alma Mater Studiorum ‐ University of Bologna Bologna 40136 Italy

References (33)

Show source-provided references
1. 10.1016/j.jpowsour.2020.22932110.1016/j.jpowsour.2020.229321 ↗
2. 10.1515/nanoph-2017-004410.1515/nanoph-2017-0044 ↗
3. 10.1016/j.phpro.2011.03.13510.1016/j.phpro.2011.03.135 ↗
4. 10.3390/batteries901000910.3390/batteries9010009 ↗
5. 10.1016/j.apenergy.2024.12296810.1016/j.apenergy.2024.122968 ↗
6. J.Garche C. K.Dyer Encyclopedia of electrochemical power sources Academic Press: Imprint of Elsevier 2009.
7. 10.1088/0022-3727/32/11/30710.1088/0022-3727/32/11/307 ↗
8. 10.2351/1.359783010.2351/1.3597830 ↗
9. 10.1146/annurev.fluid.30.1.8510.1146/annurev.fluid.30.1.85 ↗
10. 10.1007/978-1-4613-8413-7_210.1007/978-1-4613-8413-7_2 ↗
11. A.Yule J.Dunkley Atomization of Melts: for Powder Production and Spray Deposition Oxford University Press 1994.
12. 10.2961/jlmn.2013.03.001310.2961/jlmn.2013.03.0013 ↗
13. 10.1016/S0169-4332(99)00331-110.1016/S0169-4332(99)00331-1 ↗
14. 10.1016/j.jmatprotec.2023.11826610.1016/j.jmatprotec.2023.118266 ↗
15. 10.1070/QE1992v022n09ABEH00361210.1070/QE1992v022n09ABEH003612 ↗
16. 10.2351/7.000118210.2351/7.0001182 ↗
17. 10.1088/0022-3727/39/7/01710.1088/0022-3727/39/7/017 ↗
18. 10.1016/S0924-0136(96)02698-210.1016/S0924-0136(96)02698-2 ↗
19. 10.1016/j.jpowsour.2012.03.03010.1016/j.jpowsour.2012.03.030 ↗
20. 10.2351/1.495090810.2351/1.4950908 ↗
21. 10.1088/0022-3727/34/3/32010.1088/0022-3727/34/3/320 ↗
22. 10.1038/nmeth.201910.1038/nmeth.2019 ↗
23. 10.1038/nmeth.208910.1038/nmeth.2089 ↗
24. 10.1016/j.dib.2017.12.02110.1016/j.dib.2017.12.021 ↗
25. 10.1016/j.matdes.2020.10905610.1016/j.matdes.2020.109056 ↗
26. 10.1088/0022-3727/30/18/00810.1088/0022-3727/30/18/008 ↗
27. 10.1088/0022-3727/39/24/03910.1088/0022-3727/39/24/039 ↗
28. 10.1016/j.ijheatmasstransfer.2016.12.01910.1016/j.ijheatmasstransfer.2016.12.019 ↗
29. 10.1016/j.jmatprotec.2023.11820210.1016/j.jmatprotec.2023.118202 ↗
30. 10.3390/en1518654910.3390/en15186549 ↗
31. Padhma A comprehensive introduction to evaluating regression models 2024 https://www.analyticsvidhya.com/blog/2021/10/evaluation‐metric‐for‐regression‐models/.
32. 10.1023/B:HITE.0000008330.90760.f110.1023/B:HITE.0000008330.90760.f1 ↗
33. 10.1063/1.346749610.1063/1.3467496 ↗