Advancing Deep Learning Techniques for Accurate Identification of Battery Defects

Authors

  • Pete Kutsch
  • Bruce Wattenbach
  • Marc Lanigan

DOI:

https://doi.org/10.54097/

Keywords:

Deep learning, Defect detection, Lithium battery manufacturing, Neural networks

Abstract

The increasing reliance on lithium-ion batteries in various industries, including electric vehicles and portable electronics, necessitates efficient and reliable defect detection methods. Traditional inspection techniques often fall short in accuracy and speed, paving the way for advanced methods such as deep learning. This paper explores the application of deep learning techniques in detecting defects in lithium-ion batteries, providing a comprehensive overview of the methodologies, challenges, and future directions. Our study demonstrates that deep learning models can achieve up to 98% accuracy in detecting surface defects, significantly outperforming traditional methods.

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Published

28-06-2024

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Articles

How to Cite

Kutsch, P., Wattenbach, B., & Lanigan, M. (2024). Advancing Deep Learning Techniques for Accurate Identification of Battery Defects. Journal of Computing and Electronic Information Management, 13(2), 42-46. https://doi.org/10.54097/

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