Umair LatifVision Systems Architect
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Gnutti Carlo Group

Industrial Vision System Design

Lead Vision Systems Engineer / July 2025 - Present

Built the architecture and leading the team for a PLC-driven production vision system: retrofitting vision hardware in the existing production line, synchronized camera triggering, image acquisition, AI decision flow, traceability storage, commissioning checks, and support-ready diagnostics, designed methods for measurement system analysis (MSA).

PLC handshakeGigE VisionMulti-camera acquisitionAI inspectionTraceabilityCommissioningMeasurement System Analysis (MSA)

At a glance

Technical Overview and Key Facts

Role
Lead Vision Systems Engineer
System type
PLC-driven production vision system
Core stack
GigE Vision, PLC/S7, Python, OpenCV, AI inference, SQL traceability
Constraints
Retrofit integration, deterministic trigger timing, traceable image storage
Delivered
Acquisition architecture, AI decision flow, commissioning checks, support diagnostics

Problem

Production vision systems require precision and cycle time co-ordination when being retrofitted into existing production lines. Buying a standalone blackbox solution often leads to integration challenges and becomes expensive. They fail when triggering, lighting, camera settings, AI decisions, and PLC handshakes are treated as separate pieces. The challenge was to create a repeatable system that could capture the right images at the right moment, preserve traceability, and remain supportable during real production shifts and is cost effective.

My Role

Led the vision architecture and implementation strategy across PLC integration, multi-camera acquisition, image-saving structure, AI decision flow, traceability, commissioning validation, and production support.

Impact

Moved the system from project-specific logic toward a reusable production architecture: standardized triggering, image traceability, result handling, commissioning checks, and support diagnostics.

System Architecture

  • Hardware design and retrofitting: Designing and integrating vision hardware into existing production lines.
  • PLC trigger layer: receives production events and controls handshake timing.
  • Acquisition layer: captures synchronized frames from selected camera groups.
  • Inspection layer: applies processing and AI decision logic per inspection route.
  • Traceability layer: stores raw/processed images, metadata, and inspection outcomes.
  • Support layer: provides diagnostics for commissioning, troubleshooting, and long-term maintenance.

Tools

GigE Vision CamerasPLC/S7PythonOpenCVAI inferenceSQL traceabilityIndustrial lightingLinux Based SystemsNetwork Integration with IT/OTShopfloor training and support

Production Constraints

  • Retrofitting into existing production lines with minimal disruption.
  • Cycle-time-aligned acquisition triggered by PLC events.
  • Deterministic trigger and acknowledgement behavior with PLC-controlled production flow.
  • Repeatable camera, lighting, and exposure setup across stations.
  • Traceable raw and processed image storage that supports debugging, quality review, and dataset growth.
  • Commissioning checks for image quality, timing, and result reliability.

What I Delivered

  • Built a team and architecture for a PLC-driven production vision system.
  • Defined multi-camera acquisition and trigger-group architecture.
  • Built repeatable image-saving and traceability structure.
  • Integrated AI decision flow into production inspection logic.
  • Created commissioning and support patterns for validation and troubleshooting.
  • Standardized the architecture for reuse across future stations.

Validation & Commissioning

Commissioning is part of the architecture.

The system was designed around repeatable commissioning checks: trigger timing, camera exposure, image quality, frameset completeness, PLC handshake behavior, saved image structure, and traceability review.

  • Trigger timing and PLC handshake behavior.
  • Camera exposure and image-quality review.
  • Frameset completeness and saved-image structure.
  • Inspection result handling and traceability review.

Implementation Highlights

The useful work sits between camera signal and production decision.

Designed the system to meet the constraints that usually cause vision systems to fail: cycle time, precision, traceability, commissioning, and support.
Defined heartbeat, acknowledgement, timeout, and fault-handling patterns so the station fails visibly instead of silently.
Created versioned release and rollback practices for production delivery.
Converted commissioning lessons into SOPs, validation checklists, and repeatable rollout discipline.

Next Step

Need a vision system that survives production?

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