Soodabeh Ramezani
Papers
1
Total Citations
6
H-Index
1
About
Soodabeh Ramezani is a researcher at the forefront of integrating machine learning into advanced manufacturing and quality control. Her work primarily focuses on the application of data-driven techniques to improve industrial processes, with a particular emphasis on welding defect classification and automation. Her most-cited paper, "Application of Machine Learning in Automotive Stud Weld Defect Classification" (2023), has garnered 6 citations and addresses a critical challenge in arc stud welding—a process vital to automotive assembly. By developing a machine learning-based framework, Ramezani enables real-time detection of low-quality welds, preventing costly structural failures and reducing waste. This contribution not only enhances manufacturing efficiency but also sets a benchmark for intelligent quality assurance in production lines. Her research bridges the gap between traditional metallurgical processes and modern artificial intelligence, offering scalable solutions for industry 4.0. Through her innovative approach, Ramezani demonstrates how machine learning can transform legacy manufacturing practices, making them more reliable and sustainable. Her work is a valuable resource for students and engineers seeking to apply AI in real-world industrial settings.
Research Focus
Key Achievements
Top Papers
- 1