Advancing Deep Learning Techniques for Accurate Identification of Battery Defects
DOI:
https://doi.org/10.54097/oastipa3Keywords:
Deep learning, Defect detection, Lithium battery manufacturing, Neural networksAbstract
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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