Jefferson Ricardo Pereira
Papers
1
Total Citations
13
H-Index
1
About
Jefferson Ricardo Pereira is a researcher at the forefront of intelligent manufacturing and process monitoring, with a primary focus on welding automation and defect detection. His most-cited work, "Online Sound Based Arc-Welding Defect Detection Using Artificial Neural Networks" (2019, 13 citations), introduces a novel, non-invasive approach to quality control in heavy steel industries. By leveraging acoustic signals and artificial neural networks, Pereira’s research enables real-time identification of welding defects, directly addressing industry demands for cost reduction, improved reliability, and minimized material waste. This contribution stands out for its practical application in automating quality assurance, reducing reliance on manual inspection. Pereira’s work is particularly notable for bridging the gap between advanced machine learning techniques and traditional manufacturing challenges, offering a scalable solution for high-stakes environments like heavy steel fabrication. His research not only advances the field of welding process control but also underscores the potential of sound-based monitoring in industrial automation. With a growing citation impact, Pereira is recognized for his role in driving efficiency and quality in modern manufacturing, making his work essential reading for researchers and engineers in production engineering and industrial AI.
Research Focus
Key Achievements
Top Papers
- 1