Automated Board Inspection System
A turnkey machine vision platform that replaces manual QC with 24/7 AI-powered surface inspection — delivering 100% board coverage, sub-millisecond decisions, and full production traceability.
Manual QC Was the Bottleneck
High-volume flat panel board manufacturing demanded inspection at a scale and consistency that human visual QC simply cannot sustain.
Inconsistent Inspection Quality
Inspector fatigue across long shifts caused defects to escape undetected — with no way to measure or correct the variability.
Massive Surface Area per Board
Boards up to 1240 × 2445 mm meant each unit required thorough multi-point coverage — impractical at speed with human operators.
Zero Data & Traceability
No defect logs, no yield data, no SPC capability. Root cause analysis and customer quality reports were impossible.
Throughput Constraints
Growing production volumes outpaced what manual inspection could handle without adding significant headcount and floor space.
No Standardised Classification
Defect identification was subjective — what one inspector flagged as a reject, another might pass. No objective benchmark existed.
Rising Labour & Compliance Costs
Training, supervision, audit trails, and regulatory reporting overhead grew with headcount. Defects that shipped cost far more to remedy.
End-to-End Vision Automation
Indus Vision designed and built a fully integrated, conveyor-fed machine vision platform — from mechanical frame to AI software — deployed as a single turnkey system.
6-Step Automated Inspection Cycle
Every board follows the same deterministic path — from infeed to result — with zero manual intervention required during normal operation.
Infeed
Board positioned on conveyor
Capture
10 camera array acquires frames
Stitch
Images merged into single mosaic
Infer
AI model detects defects
Decide
Pass / Fail + severity logged
Eject
Pneumatic or mechanical reject
What Goes In, What Comes Out
The system consumes five input streams and produces five outputs — transforming raw boards and data signals into quality decisions and traceability records.
Inputs
Physical Board
Conveyor-fed flat panel up to 2445 mm × 1240 mm
Conveyor Signal
Speed feedback & position pulse
Power Supply
24 VDC + 110/240 VAC
Configuration
Defect thresholds & material type
Network
Ethernet to ERP / MES / server
Outputs
Pass / Fail Signal
24 VDC relay to reject mechanism
Defect Report
Type, location, severity, confidence
Image Archive
Full-res stitched board images
SPC Data
Yield, defect type distribution, trends
API Integration
REST / MQTT to ERP, MES, historian
Guaranteed KPIs
Every deployment is governed by contractually defined performance targets — measurable outcomes from day one of commissioning.
What Changes After Deployment
From the moment the system goes live, the entire quality function is transformed — faster, more consistent, and fully data-driven.
100% Traceability
Every board inspected, every defect logged with image, location, and timestamp. Full SPC capability.
Defect record per unitInstant Quality Feedback
Real-time defect dashboard shows yield trends, root cause alerts, and anomaly detection across shifts.
Sub-second latencyZero Escapes (Contractual)
AI-powered defect detection removes human fatigue. Consistent decisions. Fewer defects reaching customers.
≥98% accuracyCost Reduction
Eliminates 3–5 full-time QC inspectors. Reduces rework, scrap, and customer warranty claims by 40%+.
ROI: 12–18 monthsHigher Throughput
Continuous 24/7 operation. No breaks, no fatigue. Capacity constrained only by mechanical line speed.
Boards/hour: unlimitedCompliance Ready
Automated audit trails, defect images, and SPC reports meet ISO 9001, IPC-A-610, and customer audits.
Auditable proofSystem at a Glance
Key hardware and software specifications for the Board Inspection Automation System.
| Category | Component | Specification |
|---|---|---|
| Imaging | Camera Array | 10× GigE PoE 5MP 12-bit sensors, 50 mm lens, ≥30 fps |
| Imaging | Frame Grabber | Simultaneous capture 1 Gbps network switch with PoE |
| Processing | AI Engine | NVIDIA Jetson AGX CUDA compute, 576 TOPS peak |
| Processing | Inference Runtime | ONNX / TensorRT Defect detection + classification |
| Control | PLC | Siemens S7-1200 Conveyor sync, reject mechanism, I/O control |
| Control | Connectivity | REST API + MQTT ERP / MES / historian integration |
| Database | Time-Series Storage | PostgreSQL + TimescaleDB Defect logs, images, metrics |
| Mechanical | Frame | Modular 80/20 Support for up to 2500 mm × 1300 mm boards |
| Mechanical | Conveyor | Variable speed belt 0.5–2.0 m/min adjustable, servo sync |
| Mechanical | Lighting | Diffuse LED + structured Full spectrum 380–700 nm, ≥3000 lux |
| Performance | Detection Accuracy | ≥98% Verified at commissioning |
| Performance | Decision Latency | <100 ms Image capture through PLC output |
| Performance | Coverage | 100% Zero blind spots |
| Performance | Uptime | ≥99% Excluding planned maintenance |
| Software | Dashboard | Web-based Real-time SPC, OEE, defect heatmaps |
| Software | Data Export | CSV / JSON / Parquet Reports, audit trails, compliance |
| Power | Consumption | ~3.5 kW peak Cameras + GPU + PLC + lighting + conveyor |
| Installation | Footprint | ~4 m × 2 m Modular, can be integrated inline |
| Installation | Timeline | 8 weeks From order to live production |
Ready to Automate Your Quality Control?
This system can be adapted for your board dimensions, production speed, and integration requirements. Let's talk about your application.
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