Related Experiment Video
Updated: Jan 10, 2026
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
Published on: April 28, 2022
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.
Abstract:
This research presents a novel, low-cost optical acquisition system based on infrared imaging for real-time weld bead geometry monitoring in Gas Metal Arc Welding (GMAW). The system uniquely employs a commercial CCD camera (1000-1150 nm) with tailored filters and lenses to isolate molten pool thermal radiation while mitigating arc interference. A single camera and a mirror-based setup simultaneously capture weld bead width and reinforcement. Acquired images are processed in real time (10 ms intervals) using MATLAB R2016b algorithms for edge segmentation and geometric parameter extraction. Dimensional accuracy under different welding parameters was ensured through camera calibration modeling. Validation across 35 experimental trials (over 6000 datapoints) using laser profilometry and manual measurements showed errors below 1%. The resulting dataset successfully trained a Support Vector Machine, highlighting the system's potential for smart manufacturing and predictive modeling. This study demonstrates the viability of high-precision, low-cost weld monitoring for enhanced real-time control and automation in welding applications.
