The Problem
- Human inspectors fatigue after 20-30 minutes of continuous inspection
- Subtle defects like hairline cracks and minor color variations are missed
- Inspection becomes a production bottleneck during high-volume runs
- Inconsistent rejection criteria between inspectors and shifts
- No objective documentation of defect types and frequencies
Modern Approach
- High-speed cameras capture every tablet/capsule at 100,000+ units per hour
- AI models detect 50+ defect types including cracks, chips, spots, and print errors
- Real-time rejection of defective units without stopping production
- Defect analytics dashboard shows trends by batch, machine, and time
- Integration with batch records for complete quality documentation
Frequently Asked Questions
What defects can AI detect in tablets and capsules?
AI systems detect cracks, chips, broken edges, surface spots, color variations, size deviations, print defects, and foreign particles. Modern systems identify 50+ distinct defect types.
How fast can AI inspection systems work?
Modern systems inspect 100,000+ tablets or capsules per hour, matching or exceeding typical production speeds. Inspection never becomes a bottleneck.
Is AI inspection accepted by FDA and other regulators?
Yes. AI-based inspection is accepted when properly validated. The system must be qualified, algorithms validated for each product, and ongoing monitoring documented. Most regulators view automated inspection favorably for its consistency.
How accurate is AI inspection compared to manual?
Validated AI systems typically achieve 99%+ detection rates for trained defect types, compared to 80-85% for manual inspection. False rejection rates are also lower due to consistent criteria.
What is the ROI of automated visual inspection?
ROI comes from reduced customer complaints, fewer batch rejections, lower inspection labor costs, and faster line speeds. Most pharma units see payback in 12-18 months.