Lithium Battery Appearance and Assembly InspectionComplete Application Case Study

Case Overview

Lithium Battery Appearance and Assembly Inspection is a public application case study for New Energy Vision Inspection. A vision inspection case for cells, electrodes, housings, modules and PACK assembly, covering defects, position deviation, polarity and traceability codes. The page is written for project evaluation and solution matching; it does not disclose customer names, production capacity, confidential drawings or unverified operating metrics. The purpose is to explain what a similar computer vision project needs to evaluate, how the technical route can be organized, and which deliverables should be confirmed before implementation.

Scenario Background

Lithium battery vision inspection covers cells, electrodes, shells, modules and PACK assembly. Material reflections, film texture, edge status and assembly tolerance vary greatly, so imaging, algorithms and traceability records need to form a closed loop. In project communication, the first step is to clarify the inspected object, station position, sample variation, cycle requirement, available installation space and interface target. For cell appearance, electrode inspection, module assembly, PACK recheck, the same visual concept may require different camera positions, lighting angles, lenses, triggering methods and acceptance rules. This is why JIVISION usually starts from sample review and imaging validation before software development.

User Requirements and Evaluation Points

The typical requirements include surface defects, polarity recognition, assembly deviation, traceability code recognition. The project also needs to evaluate whether the inspection result must be stored, whether images need to be retained, whether production recipes are required, and whether the output should connect with PLC, robot controller, MES, WMS or an existing upper-computer system. Key pain points include: Cell and module surfaces are complex, with reflection, films and edge textures affecting defect recognition. Assembly requires checking position, polarity, direction and marking consistency. Production requires complete traceability data and abnormal image retention. These questions are answered through sample testing and scenario analysis rather than by using fixed public metrics.

Technical Approach

The proposed approach combines Battery Inspection, Polarity Recognition, Traceability Code with an engineering delivery workflow. JIVISION first evaluates imaging stability, then designs the algorithm pipeline and system interface. The solution normally includes: Evaluate multi-station imaging for metal, film and label surfaces. Combine object detection, OCR/DM code recognition, region localization and rule-based checks. Output station data, abnormal images, traceability-code records and equipment interaction signals. In actual projects, traditional image processing, deep-learning detection, OCR, segmentation, point-cloud processing or rule-based review can be combined according to the target object and available data.

System Architecture

• multi-light imaging
• defect detection model
• position measurement module
• traceability records

Implementation Process

The implementation path includes requirement confirmation, sample collection, imaging experiment, PoC verification, algorithm training or rule development, interface definition, onsite deployment, acceptance testing and operation handover. During each stage, the project team records sample conditions, parameter versions, decision rules and abnormal cases. This makes the final system easier to maintain and supports later model iteration when new product models or new defect types appear.

Deliverables

• Multi-station inspection flow
• Defect and marking models
• Traceability data records
• Equipment interaction interfaces

Acceptance and Iteration

Acceptance indicators should be defined with customer samples, onsite tests and agreed inspection standards. Common evaluation dimensions include recognition accuracy, missed-detection risk, false-alarm handling, processing speed, stability under lighting variation, data traceability and maintainability. JIVISION does not recommend using generic public numbers as final acceptance criteria; the final criteria should come from the customer's actual samples and operating environment.

Applicable Scenarios

This case is suitable for cell appearance, electrode inspection, module assembly, PACK recheck and similar projects that require computer vision, machine vision, edge AI, 3D vision, robot vision or visual data services. It can also be used as a reference when the customer needs a phased path from feasibility assessment to prototype validation and production deployment.

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