Transforming Battery Manufacturing with 3D Image Processing
August 28, 2026
CT provides detailed 3D data of internal battery structures, but extracting meaningful insights requires the right image processing tools. See how Dragonfly 3D World can support battery analysis with AI-assisted segmentation, precise measurements, and automated workflows for investigating electrode geometry, defects, and production variability.
As battery production grows, manufacturers need reliable ways to evaluate the complex structures hidden inside assembled cells. Electrode positioning, anode-cathode overhang, cracks, voids, delaminations, inclusions, and missing regions are all important areas of investigation.
X-ray imaging provides a vital, non-destructive method to inspect these internal structures. While 2D X-ray can offer a rapid view of a battery and reveal certain defects, computed tomography (CT) captures volumetric information that allows internal features to be examined in three dimensions and measured quantitatively.
But the scan itself is only a starting point. Image processing software allows battery manufacturing and quality professionals to identify structures within CT data, perform measurements, investigate defects, and compare results across samples. Artificial intelligence (AI) and automation can further reduce repetitive analysis when the same inspections need to be performed again and again.
Dragonfly 3D World provides one example of how these capabilities can be applied to battery CT data. Its newest software release, 3D World 2027, expands the tools available for visualization, AI-assisted segmentation, measurement, analysis, and reporting.
Using CT Data to Evaluate Internal Battery Structures
2D X-ray imaging can provide a fast way to visualize electrode structures and identify certain indications, making it useful for large-scale inspection. However, representing a three-dimensional battery in a two-dimensional projection means spatial information can be limited or distorted, restricting the quantitative analysis that can be performed.
CT reconstructs the battery as volumetric data. Engineers can inspect cross-sectional images throughout the cell and use 3D visualization to understand how internal features relate to one another. Image processing software can then be used to measure and annotate those structures and investigate potential manufacturing defects.
In a cylindrical lithium-ion cell, for example, engineers can examine electrode layers and look for inclusions, porosity, cracks, missing anodes or cathodes, and other internal features. Instead of simply identifying an indication, CT image processing provides the tools to investigate its location and characteristics within the battery.
Measuring Anode-Cathode Overhang More Efficiently
Electrode overhang demonstrates how CT data can move from visualization to quantitative analysis.
The relative positioning of anode and cathode components can be evaluated in CT cross-sections, allowing engineers to measure individual overhang distances. In 3D World, measurements can be made directly within 2D cross-sectional images, while annotations can identify potential defect locations or other indications.
Manual measurement can work well for individual locations, but the challenge is scaling the approach. A cylindrical battery contains many electrode layers, so performing the same measurements across those layers and numerous samples easily becomes time-consuming.
For recurring inspections, image processing can therefore help turn individual measurements into a more repeatable analysis workflow.
Automating Battery Analysis with Deep Learning
AI and deep learning can reduce the manual work required to identify structures within battery CT data.
In one 3D World battery workflow, a pretrained U-Net is adapted to new data through transfer learning, with just a few labeled frames in the Segmentation Wizard needed to fine-tune the model for segmenting individual cathode-anode overhang regions. Once segmented, each region can be measured independently and its overhang distance visualized using color coding. The trained model can then be applied to additional samples requiring the same analysis.
3D World 2027 expands these AI capabilities with Grid-based SAM, an instance-segmentation workflow built on Meta AI's Segment Anything Model. Grid-based SAM can automatically identify objects within a 2D view and publish them as individual labels for subsequent measurement and analysis.
As Zixiong Cao, Dragonfly Product Manager, explains: “Instead of spending valuable time preparing data before analysis, users can begin identifying and measuring objects almost immediately. It lowers the barrier to AI-assisted segmentation while still giving researchers the flexibility to refine their approach as needed.”
Application-specific deep-learning models and newer AI-assisted segmentation approaches, such as Cellpose-SAM, provide different options depending on the analysis, but both can reduce repetitive image-processing steps and help professionals move more quickly toward measurement and interpretation.
Improving Inspection of Cylindrical Battery Cells
Battery geometry can also affect how CT data is inspected. In cylindrical cells, electrode structures extend around the circumference of the battery, and a conventional cross-sectional view shows only one orientation at a time.
Dragonfly 3D World 2027 introduces Rotational Views to make these structures easier to examine. Users can rotate a 2D slice plane around a selected axis, creating a continuous series of cross-sections around the cell without repeatedly repositioning the complete dataset.
For battery inspection, this provides another way to follow electrode structures and other internal features around cylindrical geometry. New measurement and annotation capabilities can add quantitative context directly to these views, including Caliper and Scalar Arrow annotations and referential coordinate systems for consistent positioning.
Using Repeatable Analysis to Understand Production Variability
The value of quantitative battery inspection extends beyond evaluating an individual cell.
When the same measurements are performed across multiple batteries, the resulting data can start to reveal how internal structures vary across production. Electrode positioning, overhang distances, defect locations, and other measured characteristics can be compared between samples and, over time, contribute to a database describing manufacturing variability.
Repeatability is important to making those comparisons useful. Deep-learning models can be reused when similar structures need to be segmented across multiple samples, while Dragonfly also supports further automation of analysis tasks through Macros and associated tools.
The resulting information can help manufacturers investigate recurring characteristics, better understand production variability, and identify areas of the manufacturing process that may warrant closer examination.
In this way, CT image processing not only helps with identifying features within individual batteries, but also in developing a more quantitative understanding of the processes used to manufacture them.
Sharing Battery Inspection Results
Analysis results also need to reach the manufacturing, engineering, quality, and research teams that can use them.
The Report Builder in Dragonfly 3D World 2027 brings documentation into the image-processing workflow, allowing reports to combine images and visualizations with measurements, statistics, histograms, profiles, and object properties. Reports can be exported in PDF and HTML formats, with additional capabilities for editable Microsoft Word documents.
Dragonfly Session Viewer offers another way to share results. Session and object files can be opened without a full Dragonfly installation, allowing stakeholders to interact with analysis results rather than relying solely on static images.
These capabilities help connect the analysis itself with the broader process of communicating findings and using them to inform further investigation.
Getting More from Battery CT Data
CT gives battery manufacturers a non-destructive way to inspect structures hidden inside assembled cells. Image processing makes it possible to turn those scans into measurable and repeatable results.
Segmentation can isolate relevant structures, quantitative tools can measure electrode positioning and other characteristics, and AI can reduce repetitive manual analysis. When these workflows are applied consistently across samples, individual inspections can also contribute to a broader understanding of manufacturing variability.
As battery manufacturing continues to scale, getting more information from existing CT data can help professionals make inspection more efficient and build a clearer picture of the products and processes they are evaluating.