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Updated: Jan 13, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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Published on: August 29, 2025

Integration of Machine Vision and PLC-Based Control for Scalable Quality Inspection in Industry 4.0.

Maksymilian Maślanka¹, Daniel Jancarczyk¹, Jacek Rysinski¹

¹Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, 43-309 Bielsko-Biala, Poland.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

This study demonstrates a robust machine vision and programmable logic controller (PLC) integration for automated quality assurance in Industry 4.0. The system achieved over 95% detection accuracy, enhancing manufacturing intelligence.

Keywords:

PLC integration industrial automation intelligent manufacturing machine vision quality control

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Area of Science:

Industrial Automation
Machine Vision Systems
Industry 4.0

Background:

Automated quality assurance is critical for Industry 4.0.
Integrating machine vision with programmable logic controllers (PLCs) enhances real-time synchronization and deterministic communication.
Practical industrial deployment requires robust and efficient vision-PLC integration.

Purpose of the Study:

To present an applied case study of vision-PLC integration for automated quality assurance.
To evaluate the performance of a system combining a smart camera with a PLC.
To highlight the benefits and limitations of PLC-driven inspection in industrial settings.

Main Methods:

Integration of a Cognex In-Sight smart camera with an Allen-Bradley Compact GuardLogix PLC using Ethernet/IP.
Case studies involving 3D-printed prototypes, automotive connectors (using supervised learning), and fiber optic tubes.
Implementation of PLC-level triple verification for enhanced accuracy.

Main Results:

Detection accuracy exceeded 95% across all investigated scenarios.
PLC-level triple verification reduced false classifications by 28% compared to camera-only operation.
Demonstrated benefits include robustness, real-time performance, and dynamic tolerance adjustment.

Conclusions:

The developed integration framework is replicable and supports intelligent manufacturing.
PLC-driven inspection offers significant advantages for automated quality assurance.
Future work will explore hybrid AI-PLC architectures and industrial production line validation.