Bumper Inspection

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Streamlining Bumper Inspection: A Case Study in Robotic Vision

A major automotive manufacturer faced the intricate challenge of inspecting 37 distinct variants of their SUV model, each with unique front and rear bumpers. With a complex child part assembly featuring 140 inspection points, the company needed a solution to automate variant classification and child part inspection. Their innovative response was a robotic vision system, in partnership with ABB robots. This system seamlessly integrated machine vision, enabling continuous, on-the-go inspections without production line interruptions. To tackle parts with the same color as the background, a structured pattern projector and machine learning pattern recognition algorithm were deployed, drastically reducing inspection time from 8 minutes to just 52 seconds. The system’s adaptability, allowing for remote updates to accommodate new models, solidified its role in enhancing manufacturing efficiency and quality control in the automotive industry.


 A renowned automobile manufacturer faced the challenge of producing the Jeep Compass, featuring 37 different variants, each with unique front and rear bumpers. The complexity was in inspecting a highly detailed child part assembly, consisting of 140 inspection points. The company required an automated solution to classify variants and efficiently inspect child parts.

Solution: To address this intricate challenge, the company developed an advanced robotic vision system:

  • Integration with ABB Robots: The company collaborated with ABB robots to design and implement a robotic vision system.
  • Seamless Machine Vision Integration: This system seamlessly communicated with various components, including the robot controller, fixture PLC, label printer, and user interface.
  • Continuous Inspection Capability: The system was engineered to facilitate continuous on-the-go inspections, eliminating the need for frequent production line stoppages.
  • Structured Pattern Projector: For parts that shared the same color as the background, a structured pattern projector and a machine learning pattern recognition algorithm were deployed.
  • Efficiency Enhancement: By swiftly detecting child part presence, the system significantly reduced inspection time from 8 minutes (as in manual inspection) to just 52 seconds.
  • Remote Model Updates: The company incorporated the ability to remotely update the system, ensuring its adaptability to accommodate new vehicle models without disrupting production.

Results: The implementation of this cutting-edge robotic vision system yielded significant improvements:

Enhanced Efficiency: The system streamlined the inspection process, reducing inspection time and boosting overall manufacturing efficiency.

Precision and Consistency: Leveraging the structured pattern projector and pattern recognition algorithm improved inspection accuracy and consistency, eliminating human errors.

Adaptability: Remote model updates ensured that the system remained flexible, allowing it to swiftly adapt to new vehicle models.

In summary, this automobile manufacturer’s innovative solution for bumper inspection serves as a prime example of how automation and robotic vision can elevate manufacturing processes, especially in complex production environments with numerous variants. This case study underscores how technology-driven solutions can lead to enhanced quality control and operational efficiency within the automotive industry.

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