Pain Points of Optical Inspection Machines

I. Technical Challenges

1. Stringent Environmental Demands

   • Sensitive Lighting: Ambient light changes, reflections, and shadows interfere with results, requiring strict lighting control.

   • Vulnerability to Vibration: Mechanical vibrations cause image blur, necessitating costly isolation measures.

2. Inherent Detection Limitations

   • Blind Spots & Dead Angles: Internal structures, deep recesses, and back sides are often impossible to image.

   • Material & Color Limitations: Transparent, mirror-like, dark, or variably colored objects are difficult to image, leading to high false reject rates.

   • Ambiguous Defects: Subtle scratches, gradual color shifts, and texture variations are hard to quantify algorithmically.

3. Complex Setup & Maintenance

   • Tedious Parameter Tuning: Requires skilled engineers to adjust lighting, optics, and algorithm parameters iteratively.

   • Frequent Calibration & Maintenance: Regular recalibration is needed to compensate for component drift or light source decay.

II. Cost & Operational Pain Points

1. High Initial Investment

   • Expensive Hardware: High-res cameras, precision lenses, and specialized lighting are costly.

   • High Integration Costs: Custom mechanical design, software development, and system integration add significant expense.

2. Low Flexibility, Difficult Changeover

   • Highly Dedicated: Designed for specific products. Changeovers require new fixtures, optical adjustments, and program rewrites.

   • Poor Algorithm Adaptability (pre-deep learning): Traditional algorithms struggle with new products or defect types.

3. Speed vs. Accuracy Trade-off

   • High accuracy demands high resolution, leading to large image data and longer processing times, impacting throughput.

   • High-speed inspection may compromise accuracy or require more expensive (higher-performance) processors.

III. Application & Reliability Issues

1. Balancing False Calls is Critical

   • Overly Strict Criteria: High false reject rates increase cost by discarding good products.

   • Overly Lenient Criteria: High false accept rates allow defects to pass, creating quality risks.

   • Finding the optimal threshold requires extensive data and continuous tuning.

2. Burden of Data Management & Analysis

   • Massive image data requires robust servers and software for storage and management.

   • Defect traceability and analysis need specialized tools and personnel to extract value from data.

3. High Demand for Skilled Personnel

   • Requires multidisciplinary talent (optics, mechanics, software, process knowledge) for operation and maintenance, which is scarce.

Summary: The pain points of optical inspection machines stem from the fundamental challenge of balancing technical limits, economic cost, and operational complexity. Successful implementation requires not only advanced hardware and algorithms but also deep process understanding, continuous engineering optimization, and expert operational support.