Xnovo Technology has deployed an automated 3D computed tomography (3D-CT) inspection system at the BMW Group‘s Cell Manufacturing Competence Centre (CMCC) in Parsdorf, Germany. The system combines deep learning, image processing, and high-throughput computing for quality assurance of battery cells on the production floor.
Xnovo’s automated 3D-CT inspection system analyzes battery cells for anomalies at the BMW Group’s Cell Manufacturing Competence Centre. (Image: Xnovo Technology)
3D-CT provides a non-destructive method for inspecting the internal structure of battery cells. While laboratory CT scans can take hours, production environments require scans to be completed within seconds. Faster scanning can introduce additional noise, CT artifacts, and lower contrast, making conventional inspection methods less reliable.
Xnovo developed the system in collaboration with BMW Group engineering teams and integrated BMW’s analysis methods into the inspection pipeline to align with its battery production and quality-control processes.
The inspection workflow includes automated image pre-processing, beam-hardening correction, volume alignment, and battery-component segmentation. The system then uses deep learning-based analysis and Xnovo’s anomaly-detection technology to analyze the CT data and generate automated reports.
The system connects with factory data systems through OPC UA and Q-DAS interfaces, providing operators with real-time information on production quality. Its distributed computing architecture is designed to scale with battery-cell production throughput.
Filed Under: Batteries, Technology News