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Updated: Jan 10, 2026

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
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Development of a Low-Cost Infrared Imaging System for Real-Time Analysis and Machine Learning-Based Monitoring of

Jairo José Muñoz Chávez1, Margareth Nascimento de Souza Lira1, Gerardo Antonio Idrobo Pizo2

  • 1Programa de Engenharia Metalúrgica e de Materiais, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro CEP 21941-972, RJ, Brazil.

Sensors (Basel, Switzerland)
|November 27, 2025
PubMed
Summary

A new infrared imaging system offers low-cost, real-time monitoring of Gas Metal Arc Welding (GMAW) bead geometry. This high-precision system achieves accuracy below 1%, enabling enhanced welding control and smart manufacturing applications.

Keywords:
GMAW monitoringinfrared imaginglow-cost sensorsreal-time image processingweld bead geometry

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Area of Science:

  • Materials Science and Engineering
  • Optical Engineering
  • Manufacturing Technology

Background:

  • Real-time monitoring of weld bead geometry is crucial for quality control in Gas Metal Arc Welding (GMAW).
  • Existing methods often face challenges with cost, complexity, or arc interference.
  • Need for a precise, affordable system for automated welding processes.

Purpose of the Study:

  • To develop and validate a novel, low-cost optical acquisition system for real-time weld bead geometry monitoring in GMAW.
  • To achieve high dimensional accuracy and demonstrate its utility in smart manufacturing.

Main Methods:

  • Utilized a commercial CCD camera with tailored filters for infrared imaging (1000-1150 nm) to capture molten pool radiation.
  • Employed a single-camera, mirror-based setup for simultaneous width and reinforcement measurement.
  • Real-time image processing using MATLAB algorithms for edge segmentation and geometric parameter extraction.
  • Camera calibration modeling ensured dimensional accuracy across varying welding parameters.

Main Results:

  • The system achieved real-time processing at 10 ms intervals.
  • Validation against laser profilometry and manual measurements showed dimensional errors below 1%.
  • The collected dataset successfully trained a Support Vector Machine model.

Conclusions:

  • The developed infrared imaging system provides a viable, high-precision, low-cost solution for real-time weld monitoring.
  • Demonstrated potential for integration into smart manufacturing environments for predictive modeling and enhanced automation.
  • Offers improved real-time control capabilities in welding applications.