Implant 3D Inspection and Bin Picking using Denso COBOTTA

Industry > Life Sciences·PharmaApplication > InspectionApplication > Bin PickingSector > Manufacturing

This quality inspection automation solution utilizes the Denso COBOTTA compact collaborative robot and QRS 3D vision to automate the bin picking of dental implants.

This system is designed to handle the loading and inspection of implants during the machining process. Even for small and complex workpieces like implants, the QRS 3D vision system enables highly effective picking and sorting operations.

Components

Robot	Robot: Denso Cobot COBOTTA 6-axis articulated collaborative robot, payload 0.5kg, max reach 342.5mm, weight 4kg. Repeatability: ±0.05mm, Max tool speed 500mm/s. Features a built-in controller and IP30 rating.

Workflow

STEP 1.	3D Recognition & Picking: The 3D vision system identifies the implants on the loading pallet, and the robot performs the pick.
STEP 2.	Inspection: A camera captures the exterior of the picked workpiece to determine if the shape passes quality standards.
STEP 3.	Sorting & Unloading: The items are sorted into "Accepted" or "Rejected" categories and unloaded based on the inspection results.

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Features

Fast and Precise Vision Performance

Advanced Recognition: Rapidly identifies the shape and position of complex workpieces using 3D vision.

Reduced Cycle Time: Performs vision (position) recognition simultaneously with robot movement to maximize efficiency.

High-Speed Scanning: Features an ultra-fast scanning time of less than 1 second.

Safe and Compact Installation with Collaborative Robots

Space Optimization: The use of a compact collaborative robot allows for installation in tight spaces without the need for safety fences.

Collision Safety: Equipped with external force sensing to trigger an emergency stop during abnormal situations or collisions, ensuring worker safety.

Results

ROI Period	24 months
Key Benefits	Significant reduction in labor required for manual quality inspection.
Client Feedback	Because the parts are small and have complex geometries, we never imagined they could be automated. However, through Seojung’s solution, we were able to successfully verify and implement the system.