In-Sight 3900 Series
In-Sight 3900 is a high-speed AI machine vision system built for high-throughput production lines, with powerful computing and embedded AI for processing high-resolution images and advanced inspections.
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Cognex optimizes every stage of electrical vehicle (EV) battery pack inspection – from inspecting glue beads to wiring harnesses – by automating defect detection, guidance, and assembly verification using powerful AI, robust optics, and intuitive systems. With Cognex, manufacturers can also adapt quickly to part changeovers, evolving demands, and higher quality standards.
EV battery pack assembly, the final and most complex stage of EV battery manufacturing, demands accuracy, precision, and efficiency. The process involves interconnecting finished modules with wiring harnesses, connectors, and other electrical components inside a protective metal housing.
Ensuring EV battery components like wiring harnesses and connectors are assembled correctly is critical to safety and getting the best battery performance and lifespan. But manual inspection is hard, due to the complex and varied component arrangements as well as the need for extreme precision.
By automating the guidance and inspection of critical components, manufacturers can manage complex battery architectures, reduce errors, minimize scrap, and meet the highest standards for safety and performance
Our flexible vision systems use AI to surpass rule-based inspection, enabling EV battery assembly inspection, defect detection, and the ability to adapt to new challenges.
Even the smallest defects can affect EV battery safety and performance. Cognex In-Sight vision systems and AI-based tools to ensure strong bonds, exact alignment, and reliable connections throughout battery production. Cognex combines robust inspection, verification, and classification tools to accelerate EV battery assembly and elevate quality at every step:
3D vision solutions from Cognex inspect glue beads for volume and consistency
Glue beads are essential for bonding EV battery cells and forming modules, but their thin, transparent nature makes inspection difficult. Cognex vision systems use powerful optics to capture clear images in low-contrast conditions.
Since Cognex AI technology learns by analyzing images of acceptable and defective glue beads, it can separate legitimate flaws from cosmetic defects, reducing false rejects and scrap rates. Those AI tools easily detect defects like gaps, blobs, and uneven volume.
Using powerful AI and optics, Cognex machine vision systems inspect busbar welds for defects.
Flawed weld seams connecting busbars or other components can lead to power loss or slow energy transfer, but inspecting those welds is difficult because of complex surface textures and varying seams.
Cognex solutions capture sharp, high-speed images, while AI-driven software isolates defects such as missing, overpowered, or underpowered welds. Cognex AI solutions learn by examining sample images, making it easy to show them how to differentiate between acceptable and defective welds, while also accepting variations like overlapping seams to reduce false rejects and scrap rates
When EV battery modules are stacked into housings, Cognex vision systems boost throughput with vision-guided robotics for pick-and-place operations. Vision systems can be mounted on a robotic arm, installed in a machine, or used right on the line.
The system swiftly positions cell stacks by recognizing reference points for precise coordinate calculation. Cognex machine vision systems use a variety of communication protocols to seamlessly and accurately guide EV battery modules into pack housings. And with all the support you need, from a global network of experts to self-service resources, Cognex can deliver the precision your customers deserve and the reliability they trust.