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.