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Manufacturing Case Study

Computer Vision Quality Control Achieves 99.2% Accuracy

Electronics Manufacturer (2,000+ employees, India) automated PCB defect inspection using YOLOv8 computer vision, replacing manual quality control with AI-powered detection.

ANNUAL SAVINGS
$680K/Year Saved
ROI achieved in just 5 months
Defect Detection
Edge AI
23 Defect Types
1,200 PCBs/Hour
Technology Stack
PyTorch • YOLOv8 • NVIDIA Jetson
Client
Electronics Manufacturer
Industry
Manufacturing / Electronics
Timeline
14 Weeks
Team Size
5 Engineers
ROI
5 Months

The Challenge

A leading electronics manufacturer in India needed to inspect 50,000+ PCBs daily across multiple production lines. Their manual inspection process suffered from an 8-12% human error rate, with 24 quality inspectors working in rotating shifts. Missed defects were causing costly product recalls exceeding $1.2M annually, damaging customer relationships and brand reputation.

50,000+
PCBs Inspected Daily

Across multiple production lines requiring 24/7 inspection coverage

8-12%
Human Error Rate

Fatigue, inconsistency, and micro-defects missed by human eyes

24
Quality Inspectors in Shifts

High labor costs with significant training and turnover challenges

$1.2M+
Annual Recall Costs

Product recalls and warranty claims from undetected defects

Our AI Solution

We designed and deployed an end-to-end computer vision quality control system built for industrial-grade performance and reliability.

YOLOv8 Object Detection

Computer Vision

Custom-trained YOLOv8 model on 100,000+ annotated PCB images, capable of identifying 23 distinct defect categories including solder bridges, missing components, misalignments, and trace defects -- all at production line speed.

  • Trained on 100,000+ PCB images
  • 23 defect categories identified
  • Real-time detection at production line speed

Edge AI Deployment

Infrastructure

Deployed on NVIDIA Jetson AGX Orin for on-premises processing with zero cloud dependency, ensuring sub-50ms inference latency. Integrated directly with industrial GigE cameras mounted above the production conveyor.

  • NVIDIA Jetson AGX Orin for on-premises processing
  • Industrial camera integration
  • No cloud dependency for real-time inspection

Production Integration

Deployment

Seamlessly integrated with existing conveyor systems and automated reject mechanisms. Piloted on a single production line, validated over 4 weeks, then rolled out across all 3 active lines with zero production downtime.

  • Integrated with conveyor systems
  • Automatic reject mechanism
  • Pilot on 1 line, then rollout to 3 lines

Quality Dashboard

Analytics

A real-time analytics dashboard providing defect trend monitoring, shift performance tracking, and automated quality reports. Management can drill down into defect types, line performance, and historical trends.

  • Real-time defect analytics
  • Trend monitoring & shift performance tracking
  • Automated quality reports

Results & Impact

Measurable outcomes delivered within the first 6 months of deployment

99.2%
Detection Accuracy

vs 88-92% human accuracy

8x
Faster Inspection

150 to 1,200 PCBs per hour

$680K
Annual Savings

Reduced 24 to 4 inspectors

87%
Fewer Customer Defects

Defect rate: 2.4% to 0.3%

$1.2M
Recall Costs Eliminated

2 product recalls prevented

5 Mo
ROI Achieved

Fastest payback across all projects

Technology Stack

Industrial-grade AI systems purpose-built for production environments

Computer Vision

PyTorch, YOLOv8, OpenCV

Edge Computing

NVIDIA Jetson AGX Orin

Industrial

GigE cameras, conveyor integration

Analytics

Real-time dashboard, automated reports

"Bytesar's computer vision system has revolutionized our quality control. We're catching 99% of defects compared to 90% before, and we're doing it 8x faster. Customer complaints have dropped to nearly zero."

Rajesh Kumar
VP of Manufacturing Operations

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