Industrial Automation
Industrial Automation
A leading motorcycle manufacturer sought to improve the accuracy and efficiency of its tyre rim assembly process—one of the most critical steps in motorcycle production. During assembly, bearings are manually placed before being clamped onto the tyre rim. Any incorrect bearing orientation at this stage can result in assembly defects, increased rework, production delays, and potential safety risks in the finished motorcycle.
This challenge was further compounded by real-world manufacturing conditions commonly found in automotive environments:
Traditional manual inspection was time-consuming, inconsistent, and highly dependent on operator skill and experience. The manufacturer needed a high-precision, automated inspection solution capable of handling these complexities—while reducing operator workload and enabling fast, repeatable setup on the production line.
To overcome these challenges, the manufacturer deployed Omron’s FHV7-AI AI-Lite Smart Camera, fully integrated into the tyre rim assembly line and Omron’s automation ecosystem.
The FHV7-AI combines conventional rule-based vision with self-learning AI, enabling robust orientation inspection without the need for complex programming or specialized vision expertise. This hybrid approach makes it particularly well suited for automotive assembly applications where precision, and repeatability are essential.
Key capabilities include:
By embedding AI processing, the FHV7-AI eliminated the need for external PCs and complex system architectures—simplifying deployment and maintenance.
Following implementation, the manufacturer achieved significant gains across quality, productivity, and operational stability:
FHV7-AI can detect shiny and black surface bearings.
By adopting Omron’s FHV7-AI Smart Camera, the motorcycle manufacturer transitioned from manual, reactive inspection to a proactive, AI-driven quality control strategy. The solution enhanced product safety, ensured consistent assembly quality, and supported scalable automation for future vehicle models.
This success case demonstrates how AI-powered vision can be practically and effectively applied in automotive assembly lines—solving complex inspection challenges while reducing engineering effort and operational risk
Customer reference
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