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March 12, 2026MethodsX1 citationsOpen Access

batteryₓctworkflows: extracting quality metrics from X-ray computed tomography of Li-ion cells

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MJMatthew P. JonesECSI Fibrotools (United States)HRHamish ReidECSI Fibrotools (United States)RYR. S. YoungRutherford Appleton Laboratory

Key Points

  • The aim is to develop a method for extracting quantitative quality metrics from X-ray computed tomography of Li-ion cells.
  • Utilized Python notebooks to derive metrics from XCT images
  • Employed preprocessing and segmentation techniques with optional U-Net models
  • Addressed specific QA questions related to the design and condition of Li-ion cells
  • Provided a framework for reproducible quality assessment of cylindrical Li-ion cells
  • Enabled adaptation for various scanners and cell formats
  • Lowered the barrier for implementing XCT-based quality assurance in both research and industrial contexts

Abstract

batteryₓctworkflows is an open, Python notebook-based method for deriving quantitative quality assurance (QA) metrics from X-ray computed tomography (XCT) of cylindrical Li-ion cells. The workflows target three recurring industrial QA questions: (i) are electrode overhang regions within design tolerance, (ii) is the canister geometry and alignment acceptable, and (iii) is the internal winding uniform and free from gross defects. The method combines preprocessing, segmentation (including optional pre-trained U-Net models), and metric calculation into a series of executable notebooks that can be run locally or via Binder using public example datasets. By packaging data, code, models, and narrative explanations together, this method lowers the barrier to adopting XCT-based QA in both research and industrial settings. Users can reproduce the provided examples, adapt individual steps to their own scanners and cell formats, and extend the notebooks to new metrics while retaining a transparent audit trail.

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Cite This Study

Jones et al. (2026) studied this question.

synapsesocial.com/papers/69b25abe96eeacc4fcec8c2ehttps://doi.org/10.1016/j.mex.2026.103856
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