Lens Visual Inspection: A Step-by-Step Guide

The Problem: Manufacturing Challenges

Precision optics demand consistent, micron-level surface integrity. Manual inspection can miss intermittent, angle-dependent defects and slows production.

• Scratches: Linear abrasions that scatter light and degrade MTF.
• Chips/Edge Cracks: Breaks on rims or chamfers that propagate during assembly.
• Coating Defects: Non-uniform AR coating, rainbowing, haze, or pinholes that alter transmission.
• Contamination/Particles: Dust, fibers, oil smears, or bubbles that create ghosting and stray light.

Human vision fatigues over shifts and is highly sensitive to glare and angle, leading to drift and missed defects.

The Solution: Automated Visual Inspection

Machine vision provides controlled optics, lighting, and repeatable measurements that outperform manual inspection. Compared to human inspection, it delivers consistent detection, high speed, and objective pass/fail criteria across all shifts.

Deep Learning extends beyond rule-based checks to capture complex textures and subtle coating anomalies. It learns real variation, enabling robust presence/absence checks, defect classification, and dimensional validation for lenses and assemblies.

Step 1: Imaging Setup

Click 'Configure Imaging'. Place object in view. Adjust 'Camera Settings' for clear image. Click 'Save'.

For lenses, minimize glare with diffuse dome or coaxial lighting and use polarization to suppress reflections. Keep the optical axis normal to the surface for uniform illumination.

Step 2: Image Alignment

Navigate to 'Template Image'. Capture Template. Add '+ Rectangle' region. Set 'Rotation Range' to 20 degrees.

This ensures consistent registration when lenses arrive at slight angles or with rotational variance in trays or nests.

Step 3: Inspection Region Selection

Navigate to 'Inspection Setup'. Rename 'Inspection Types'. Click '+ Add Inspection Region'. Resize yellow box over defect area. Click 'Save'.

Place the yellow box over critical zones such as the clear aperture, coating witness marks, and edge/chamfer where chips occur.

Step 4: Labeling Data

Label images as Good or Bad to train the recipe.

Keep a human-in-the-loop for edge cases, ensuring the model learns true defects versus acceptable process variation.

Step 5: Creating Rules

Set pass/fail logic based on Inspection Types.

Combine thresholds for different regions (e.g., clear aperture stricter than edge) to match your quality plan.

Key Outcomes & ROI

• Higher Throughput: Automated vision inspects at line speed with robotic handling.
• Reduced Scrap: Early detection of scratches, chips, and coating defects prevents downstream rework.
• Consistent Quality & Traceability: Objective, repeatable results support QA and end-to-end traceability.