Free AI Savings Calculator—Discover your automation potential in 2 minutes→
ZEROSLIDE
← All Case Studies
Manufacturing

Visual Quality Inspection

A precision parts manufacturer (automotive tier-2 supplier, 50K parts/day)

94% reduction in defect escape rate, $800K annual savings in rework and returns

KEY RESULTS

99.7%
Inspection accuracy, up from 80% manual. AI catches defects humans miss.
94%
Reduction in escaped defects reaching customers. From 2.3% to 0.14%.
$800K
Annual savings from reduced rework, returns, and warranty claims.
0.3 sec
Inspection time per part, down from 8 seconds. Zero production bottleneck.

Before & After

✗ Before

  • •Inspection accuracy: 80% (industry average for manual)
  • •Defect escape rate: 2.3%
  • •Inspection time per part: 8 seconds
  • •Inspector fatigue impact: 15% accuracy drop by shift end
  • •Annual rework/return costs: $850K
  • •Customer quality complaints: 12/month

✓ After

  • •Inspection accuracy: 99.7%
  • •Defect escape rate: 0.14%
  • •Inspection time per part: 0.3 seconds
  • •Fatigue impact: None (AI doesn't tire)
  • •Annual rework/return costs: $50K
  • •Customer quality complaints: 1/month average

The Challenge

This automotive tier-2 supplier produces 50,000 precision parts daily for major automakers. A 2.3% defect escape rate meant 1,150 bad parts reaching customers monthly—triggering costly returns, rework, and potential recalls. Manual inspection couldn't keep up: inspectors at 80% accuracy, dropping to 65% by end of shift due to fatigue.

PAIN POINTS

  • ×Manual visual inspection achieved only 80% accuracy—20% of defects escaped to next stage or customer
  • ×Inspector fatigue created quality variance: first-hour accuracy vs. eighth-hour accuracy differed by 15%
  • ×Shift changes created coverage gaps; rush periods led to skipped inspections
  • ×Customer quality audits demanded documentation the manual process couldn't provide
  • ×Training new inspectors took 6+ months to reach acceptable accuracy; turnover was 35% annually

Technology Stack

  • →Industrial cameras (5 angles per part) with specialized lighting
  • →Custom CNN model trained on 100K+ labeled part images
  • →Edge computing for sub-second inference at production line speed
  • →MES integration for automatic part routing based on AI decision
  • →Quality analytics dashboard with trend detection

Implementation Approach

1

Phase 1: Imaging Infrastructure (Weeks 1-4)

Installed industrial camera arrays at 3 inspection stations. Each station captures 5 angles per part with specialized lighting optimized for surface defect detection. Built conveyor integration for automatic part positioning. Designed for zero production slowdown.

2

Phase 2: Defect Classification Model (Weeks 5-10)

Collected and labeled 100K+ images across 47 defect types: surface scratches, dimensional variations, burrs, cracks, contamination, assembly errors. Trained CNN achieving 99.7% accuracy on held-out test set. Model runs inference in 0.3 seconds per part—faster than production line speed.

3

Phase 3: Production Integration (Weeks 11-14)

Integrated AI decisions with MES system. Good parts proceed automatically; defective parts divert to reject bin with defect type logged. Built override station for quality engineers to review borderline cases and provide feedback for model improvement.

4

Phase 4: Analytics & Continuous Improvement (Weeks 15-17)

Dashboard showing real-time defect rates by type, station, time, and production run. Trend detection alerts quality team when defect rates spike—often catching process drift before it becomes batch-level problem. Feedback loop continuously improves model accuracy.

Results Breakdown

99.7%

Inspection accuracy, up from 80% manual. AI catches defects humans miss.

94%

Reduction in escaped defects reaching customers. From 2.3% to 0.14%.

$800K

Annual savings from reduced rework, returns, and warranty claims.

0.3 sec

Inspection time per part, down from 8 seconds. Zero production bottleneck.

Key Learnings

  • 1.Lighting matters more than camera resolution. Proper lighting reveals defects; poor lighting hides them from any camera.
  • 2.Real defect data is gold. Collecting 100K labeled images took longer than building the model. Worth the investment.
  • 3.Edge computing is essential. Cloud round-trip latency doesn't work at production line speed. Inference must be local.
  • 4.Start with highest-volume parts. Prove value there before expanding to long-tail SKUs with fewer training examples.
  • 5.The data enables process improvement. Once you can see every defect, you can trace back to root causes and fix the process.

Industry Context

Intel's AI vision inspection saves $2M annually on wafer defects. Industry benchmarks show AI inspection achieves 99%+ accuracy while reducing inspection time by 70%. The cost of quality in manufacturing is typically 15-20% of sales—even small accuracy improvements translate to significant savings. The gap between AI and human inspection widens as production volume increases.

TIMELINE

120 days including hardware installation and model training

Get notified when we publish new case studies

We publish 2-3 deep dives per month. No spam, just real results from real deployments.

Ready to achieve similar results?

Let's discuss how we can transform your manufacturing operations.

Book a 15-min Call →