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Add a real-time inspection layer to every electrode web with vision AI for battery electrode coating inspection. Built for the lines where a defect made at the coater rides through calendering, slitting, winding, filling, and formation before a test finally names it, a bright metal particle disappears into matte black coating until a separator finds it in the field, and every meter that escapes inspection multiplies in value while staying exactly as bad as it was. Whether you're coating anode or cathode, calendering, or slitting, Roboflow inspects every meter at line speed, with roll maps and records that keep bad sections out of good cells.
Coating and Surface Defects:
Foil, Calendering, and Contamination:
Rolls, Maps, and Systems Integration:
Bring intelligence to every meter today. Stop coating defects from becoming formation scrap, capacity-fade batches, or field failures with your cell's serial on them.
What is battery electrode coating inspection with Vision AI?
Battery electrode coating inspection with vision AI uses computer vision models to inspect electrode webs through coating, calendering, and slitting: pinholes, streaks, craters, and agglomerates in the coating, uncoated patches and edge registration, foil wrinkles and tears, and metal particle contamination. Models trained on your actual chemistries and webs inspect every meter at line speed, with roll maps and records that support IATF 16949 and customer cell qualification.
Can Vision AI find defects on matte black coating at line speed?
The electrode web is the hard case: matte black anode that swallows light, coating texture that hides subtle streaks, defects from tens of microns up, and a web moving fast enough that every miss is meters long. Deep-learning models trained on your actual coatings and lighting learn the difference between normal coating texture and true defects, hold that judgment across chemistries and coat weights, and flag uncertain regions on the roll map for review instead of guessing, so disposition decisions rest on findings rather than luck.
How does this fit with our coat-weight gauges and thickness measurement?
Beta and X-ray gauges keep measuring areal density and coat weight, and thickness measurement keeps its role at the calender; those are the dimensional instruments of record. Vision AI adds the visual defect layer those gauges can't see: the pinhole inside a coat weight that averages fine, the agglomerate about to be pressed flat, the bright particle sitting on the surface. Gauge data and visual findings land on the same roll map, which is what makes a disposition decision complete.
Can it integrate with our coaters, PLCs, and MES genealogy?
Yes. Roboflow Inference exposes a standard API and supports common industrial protocols, so electrode inspection events flow into your existing systems: coater, calender, and slitter PLCs from Allen-Bradley and Siemens, MES genealogy and roll tracking, and SCADA and HMI platforms like Ignition and AVEVA, through REST, MQTT, OPC UA, and direct database writes. Defect maps follow the roll through every step, and every event carries roll, lane, position, timestamp, and imagery, with a full audit trail behind every coated kilometer.
