Khalid Kouiss
Papers
1
Total Citations
5
H-Index
1
About
Khalid Kouiss is a researcher at the forefront of integrating artificial intelligence with industrial automation, with a particular focus on advanced manufacturing and robotic systems. His work centers on leveraging deep learning to enhance precision and efficiency in complex industrial processes, most notably in welding applications for the automotive sector. Kouiss’s major contribution lies in pioneering methodologies for automating the generation and annotation of large-scale datasets, a critical bottleneck for training robust AI models in real-world factory settings. His most-cited paper, "Automating the Dataset Generation and Annotation for a Deep Learning Based Robot Trajectory Adjustment Application for Welding Processes in the Automotive Industry" (2022, 5 citations), directly addresses this challenge by enabling robots to autonomously correct their trajectories, thereby increasing the level of automation and building smarter, more adaptive machinery. This work demonstrates a practical pathway for industries to harness AI for process control and monitoring, moving beyond theoretical frameworks to tangible, high-impact solutions. Kouiss’s research is instrumental in bridging the gap between cutting-edge AI and the demanding, real-world requirements of modern manufacturing.
Research Focus
Key Achievements
Top Papers
- 1