Lithium Metal Battery Quality Control via Transformer-CNN Segmentation

TL;DR

This paper introduces TransforCNN, a transformer-based neural network for segmenting dendrites in XCT images of lithium metal batteries, improving accuracy over existing models for non-destructive battery analysis.

Contribution

The work presents a novel transformer-CNN hybrid model for 3D XCT image segmentation, outperforming traditional models like U-Net, Y-Net, and E-Net in lithium battery dendrite detection.

Findings

TransforCNN achieves higher over-segmentation metrics (mIoU, mDSC)

TransforCNN outperforms U-Net, Y-Net, and E-Net in qualitative visualizations

Proposes a new method for non-destructive lithium dendrite analysis

Abstract

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, such as U-Net, Y-Net, and E-Net, consisting of an Ensemble Network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean Intersection over Union (mIoU) and mean Dice Similarity Coefficient (mDSC) as well as through several qualitatively comparative visualizations.