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Large auto parts manufacturer

AI + 3D Vision-Guided Machine Tending of Drum Brakes

Customer Demand

A major auto-parts manufacturer required an automated solution for high-volume loading and unloading of drum brakes. The parts are relatively large and heavy, often have reflective or oily surfaces after machining, and come in a range of shapes and sizes. The on-site environment contains strong and complex ambient lighting ( > 30,000 lx), making visual perception difficult. The customer needed an AI-powered 3D vision solution that could reliably perceive reflective, contaminated parts and deliver flexible, high-throughput machine tending.

Mech-Mind General-Purpose "Eye + Brain" for Robots

AI + 3D Vision-Guided Machine Tending of Drum Brakes Project Site

Workflow:

Mech-Mind's "Eye + Brain" solution (including high-precision 3D cameras and AI software suites) equips robots to accurately detect reflective, oil-contaminated drum brakes and autonomously perform loading and unloading tasks. The high-precision, large-field-of-view 3D camera generates detailed point clouds even under strong ambient light. The AI software performs robust pose estimation, plans collision-free pick paths, and controls real-time pick execution to achieve stable, repeatable pick-and-place operations.

Advantages:

  1. Mech-Eye LSR L-GL high-precision 3D camera captures high-quality 3D point clouds of reflective drum brakes with fine details.

  2. Achieves reliable recognition even under ambient lighting interference.

  3. Enables stable robotic pick-and-place operations without collisions, powered by our advanced AI algorithms.

  4. Quickly adapts to a variety of drum brake specifications to shorten changeovers.

  5. No fixture customization required, delivering fast, out-of-the-box efficiency.

Results:

  • Stabilized cycle times and improved pick accuracy.

  • Increased throughput and minimized idle time.

  • Enabled fast deployment and shorter changeovers.

  • Achieved highly efficient, flexible, and intelligent production.

Click here to watch the real-world case video.