By combining high-resolution imaging with Deep Learning-based OCR, the system accurately reads and validates tire markings in real time— eliminating manual inspection and ensuring that every tire meets quality and regulatory requirements before leaving the line.
Thanks to OCR
Thanks to the cameras location
depending on the manufacturing plant
This could result in defects going unnoticed until later stages, causing products to fail quality control checks and potentially leading to failed audits
Losing process traceability, leading to errors that could have been easily avoided
Operational and reputational risk caused by avoidable errors, which may even lead to financial losses due to non-compliance with OEM standards, as entire batches can be returned for inspection and the resulting costs and liabilities must be covered by the Tier 1 suppliers
With a fully automated 100% inspection process, defects can be detected quickly and consistently, enabling early identification of critical issues. The system can also be seamlessly integrated into the production line without causing any disruption to the existing process.
All information and data are fully digitalized, providing access to historical records and enabling the generation of more detailed process metrics to support performance analysis and continuous improvement initiatives.
With this system, not only are defects detected at an early stage, but it also reduces the need to allocate extensive resources to manual inspection tasks, allowing operators to be reassigned to higher-value activities and enabling a more efficient workflow structure.
Join this virtual event and discover how Machine Vision technologies are transforming automotive wheel production through automated surface inspection, error proofing, DOT code reading and robotic guidance solutions.
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