Examples of application solutions for lithium battery appearance and assembly inspection

Program overview

Examples of lithium battery appearance and assembly inspection application solutions are examples of public industry application solutions in the direction of new energy visual inspection. For the battery core, pole piece, casing, module and PACK assembly process, a visual solution for appearance defects, position deviation, polarity identification and traceability code detection is presented. This page is geared towards project evaluation and solution matching. It does not disclose customer names, production line scales, business data or unconfirmed operating indicators. It focuses on what similar computer vision projects need to be evaluated, how to organize the technical route, how to confirm the delivery content, and what information should be prepared for early communication.

Project background

Lithium battery visual inspection covers battery cells, pole pieces, casings, modules and PACK assembly. The surface reflection, film texture, edge state and assembly tolerance of different materials are significantly different, which requires a complete closed loop between imaging, algorithms and traceability records. In project communication, it is first necessary to clarify the detection objects, work station locations, sample differences, rhythm requirements, installation space, existing equipment interfaces and acceptance methods. For scenarios such as battery cell appearance, pole piece inspection, module assembly, and PACK re-inspection, the same visual concept may require different camera angles, light source methods, lens focal lengths, trigger methods, and judgment rules at different sites. Therefore, JIVISION usually starts with sample analysis and imaging verification, rather than directly applying a fixed template.

Customer needs and assessment points

Typical requirements for this type of project include appearance defects, polarity identification, assembly deviations, and traceability code identification. In addition to the identification itself, it is also necessary to evaluate whether the test results need to retain pictures, whether the formula needs to be switched according to product model, whether it needs to be connected with PLC, robot, MES, WMS or host computer system, and whether batch reports need to be generated. The main pain points include: the surface materials of the cells and modules are complex, and reflection, film layers and edge textures will affect defect identification; the consistency of position, polarity, direction and identification information needs to be checked during the assembly process; the production process has clear requirements for the integrity of traceability data and the retention of abnormal pictures. These issues need to be confirmed through sample testing and field condition analysis, and are not suitable to be replaced by unverified universal indicators.

Technical solution

The technical solution focuses on lithium battery detection, polarity identification, and traceability codes, and is implemented in conjunction with the project delivery process. JIVISION will first evaluate the imaging stability, and then design the algorithm link and system interface. The plan usually includes: evaluation of multi-station imaging solutions for metal, film and label surfaces; multiple inspections combined with target detection, OCR/DM code identification, regional positioning and rule determination; output of station data, abnormal pictures, traceability code records and equipment linkage signals. In actual projects, traditional image processing, deep learning detection, OCR recognition, instance segmentation, point cloud processing or rule review processes can be combined according to object characteristics, so that the system can both identify targets and explain the basis for judgment.

System architecture

• Multiple light source imaging
• Defect detection model
• Position measurement module
• Traceability records

Implementation process

The implementation process generally includes requirement confirmation, sample collection, imaging experiments, solution verification, algorithm training or rule development, interface definition, on-site deployment, acceptance testing, and operation and maintenance handover. At each stage, sample conditions, parameter versions, judgment rules, and abnormal sample records will be precipitated to facilitate subsequent review. When customers subsequently add product models, defect categories or on-site workstations, they can continue to iterate based on the original data and interfaces.

Delivery content

• Multi-station inspection process
• Defect and mark recognition model
• Traceability data records
• Device linkage interface

Acceptance and iteration

The acceptance indicators should be determined based on customer samples, on-site test results and testing standards confirmed by both parties. Common evaluation dimensions include recognition accuracy, risk of missed detection, false alarm handling, processing speed, stability under illumination changes, data traceability and maintenance convenience. The solution example description on the public page is used to illustrate the technical route and delivery method. The final project indicators should be based on the customer's real samples, deployment environment and contract agreements.

Applicable scenarios

This solution example is suitable for cell appearance, pole piece inspection, module assembly, PACK re-inspection and similar needs. It is also suitable for reference by customers who are evaluating machine vision inspection, computer vision algorithms, edge AI vision, 3D visual measurement, robot vision or visual data training. For projects with uncertain technical routes, sample evaluation, imaging testing, and small-scale solution verification can be conducted first to confirm feasibility before proceeding to system development and on-site deployment.

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