使用实例分割卷积神经网络自动评估锂金属电池生产中的激光切割质量

IF 1.7 4区 工程技术 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY
J. Kriegler, Tianran Liu, R. Hartl, Lucas Hille, M. F. Zaeh
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引用次数: 0

摘要

将锂金属箔分离成单个阳极是全固态电池生产的关键工艺步骤。使用纳秒脉冲激光切割技术,可根据所选参数设置形成决定质量的切割边缘几何特征。这种切割边缘可通过微米级成像技术(如共焦激光扫描显微镜)进行表征。目前,通过实验确定合适的工艺参数非常耗时,而且人为测量方法存在偏差,而自动质量保证方法尚不得而知。本研究提出了一种用于锂箔激光切割边缘几何特征描述的深度学习计算机视觉方法。该方法采用了卷积神经网络架构 Mask R-CNN,并将其用于对显示缺陷和成功切割的共焦激光扫描显微镜图像进行分类,分类精度达到 95% 以上。对该算法进行了训练,以对切割边缘与质量相关的熔体超高进行像素级自动分割,分割准确率高达 88%。评估了训练数据集大小对分类和分割精确度的影响,证实该算法具有工业应用潜力,因为所需的原始图像数量较少,仅为 246 张或更少。分割掩模与切割边缘的地形数据相结合,获得了用于锂金属电极质量评估的量化指标。所介绍的计算机视觉流水线实现了锂箔激光切割质量检测的自动图像评估集成,促进了使用锂金属负极的全固态电池的工业化生产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated quality evaluation for laser cutting in lithium metal battery production using an instance segmentation convolutional neural network
Separating lithium metal foil into individual anodes is a critical process step in all-solid-state battery production. With the use of nanosecond-pulsed laser cutting, a characteristic quality-decisive cut edge geometry is formed depending on the chosen parameter set. This cut edge can be characterized by micrometer-scale imaging techniques such as confocal laser scanning microscopy. Currently, experimental determination of suitable process parameters is time-consuming and biased by the human measurement approach, while no methods for automated quality assurance are known. This study presents a deep-learning computer vision approach for geometry characterization of lithium foil laser cut edges. The convolutional neural network architecture Mask R-CNN was implemented and applied for categorizing confocal laser scanning microscopy images showing defective and successful cuts, achieving a classification precision of more than 95%. The algorithm was trained for automatic pixel-wise segmentation of the quality-relevant melt superelevation along the cut edge, reaching segmentation accuracies of up to 88%. Influence of the training data set size on the classification and segmentation accuracies was assessed confirming the algorithm’s industrial application potential due to the low number of 246 or fewer original images required. The segmentation masks were combined with topography data of cut edges to obtain quantitative metrics for the quality evaluation of lithium metal electrodes. The presented computer vision pipeline enables the integration of an automated image evaluation for quality inspection of lithium foil laser cutting, promoting industrial production of all-solid-state batteries with lithium metal anode.
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来源期刊
CiteScore
3.60
自引率
9.50%
发文量
125
审稿时长
>12 weeks
期刊介绍: The Journal of Laser Applications (JLA) is the scientific platform of the Laser Institute of America (LIA) and is published in cooperation with AIP Publishing. The high-quality articles cover a broad range from fundamental and applied research and development to industrial applications. Therefore, JLA is a reflection of the state-of-R&D in photonic production, sensing and measurement as well as Laser safety. The following international and well known first-class scientists serve as allocated Editors in 9 new categories: High Precision Materials Processing with Ultrafast Lasers Laser Additive Manufacturing High Power Materials Processing with High Brightness Lasers Emerging Applications of Laser Technologies in High-performance/Multi-function Materials and Structures Surface Modification Lasers in Nanomanufacturing / Nanophotonics & Thin Film Technology Spectroscopy / Imaging / Diagnostics / Measurements Laser Systems and Markets Medical Applications & Safety Thermal Transportation Nanomaterials and Nanoprocessing Laser applications in Microelectronics.
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