Ghinwa Ouaidat
Papers
1
Total Citations
4
H-Index
1
About
Ghinwa Ouaidat is a rising researcher at the intersection of robotics, manufacturing, and artificial intelligence. Her primary focus lies in advancing incremental sheet forming (ISF) through intelligent control systems, particularly by integrating deep learning with robotic manipulation. In her most cited work, she developed a novel trajectory control framework that uses deep neural networks as a force/torque compensator alongside a task-space error tracking controller for robotized ISF. This contribution directly addresses the critical challenge of maintaining process stability and part accuracy in flexible, die-less forming—a key enabler for low-volume, customized production in aerospace and automotive sectors. Though early in her career, her work has already garnered attention, with her top paper accumulating 4 citations since its 2025 publication. Ouaidat’s research is notable for bridging the gap between advanced neural network architectures and real-time robotic control, offering a pathway toward more adaptive and precise manufacturing processes. Her achievements signal a promising trajectory in smart manufacturing, where AI-driven robotics are reshaping traditional forming techniques.
Research Focus
Key Achievements
Top Papers
- 1