Jove
Visualize
Contact Us

Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

300
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
300

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Novel Robotic Test Rig for Camshaft Geometry Measurement with a Collaborative Robot.

Sensors (Basel, Switzerland)·2026
Same author

Comparative Analysis of the Structure, Properties and Internal Stresses of MAG Welded Joints Made of S960QL Steel Subjected to Heat Treatment and Pneumatic Needle Peening.

Materials (Basel, Switzerland)·2025
Same author

Shaping the Structure and Properties of Stellite 6 Alloy by Addition of Ti and W via Laser Cladding.

Materials (Basel, Switzerland)·2025
Same author

Research on the Influence of HMFI and PWHT Treatments on the Properties and Stress States of MAG-Welded S690QL Steel Joints.

Materials (Basel, Switzerland)·2024
Same author

Microstructure and Erosion Wear of In Situ TiC-Reinforced Co-Cr-W-C (Stellite 6) Laser-Cladded Coatings.

Materials (Basel, Switzerland)·2024
Same author

Virtual Sensor for On-Line Hardness Assessment in TIG Welding of Inconel 600 Alloy Thin Plates.

Sensors (Basel, Switzerland)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 23, 2025

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

461

Assessing MMA Welding Process Stability Using Machine Vision-Based Arc Features Tracking System.

Wojciech Jamrozik1, Jacek Górka2

  • 1Department of Fundamentals of Machinery Design, Silesian University of Technology, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|December 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost camera system for real-time arc length monitoring in manual metal arc (MMA) welding. This feedback aids welder training and improves weld quality by tracking electrode tip position.

Keywords:
MMA weldingprocess monitoringvision systemwelding arcwelding arc stability

More Related Videos

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.4K
Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

8.5K

Related Experiment Videos

Last Updated: Nov 23, 2025

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

461
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

1.4K
Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation
05:30

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

Published on: September 29, 2019

8.5K

Area of Science:

  • Welding Engineering
  • Robotics and Automation
  • Computer Vision

Background:

  • Arc length significantly impacts manual metal arc (MMA) welding stability, arc voltage, and weld quality.
  • Welder skill is the primary determinant of MMA welding process control.
  • Real-time feedback on arc length and welding speed is valuable for welder training and production.

Purpose of the Study:

  • To develop and validate a cost-effective system for monitoring welding arc parameters.
  • To provide real-time feedback on arc length and welding speed to welders.
  • To enhance the training and production stages of MMA welding.

Main Methods:

  • Utilized affordable Complementary Metal Oxide Semiconductor (CMOS) cameras for image acquisition.
  • Implemented image processing techniques to track the welding electrode tip.
  • Estimated geometrical properties of the welding arc from camera data.
  • Measured arc voltage as a reference for process stability validation.

Main Results:

  • Successfully tracked the welding electrode tip using CMOS cameras.
  • Estimated key geometrical properties of the welding arc.
  • Demonstrated the correlation between image-processed parameters and measured arc voltage.
  • Validated the feasibility of using low-cost cameras for arc length estimation.

Conclusions:

  • A cost-effective CMOS camera system can effectively monitor welding arc length and related parameters.
  • The developed system offers valuable real-time feedback for MMA welding training and production.
  • Image processing provides a viable method for estimating welding process characteristics.