K. Mazouz
Papers
1
Total Citations
3
H-Index
1
About
K. Mazouz’s research lies at the intersection of artificial neural networks and automated manufacturing inspection, with a particular focus on enhancing quality control through machine vision. His most cited work, “A printed circuit board inspection system using artificial neural network” (2002), introduced a pioneering approach that integrated a video camera, robot, and machine vision system with a backpropagation-trained neural network to distinguish between correct and faulty PCB assemblies. This contribution demonstrated how computational intelligence could automate defect detection, reducing reliance on manual inspection and improving production efficiency. While his citation count of 3 reflects the niche, early-stage nature of this work, it stands as a foundational reference in the field of intelligent manufacturing inspection. Mazouz’s research underscores the potential of combining robotics and neural networks for real-time quality assurance, offering a practical blueprint for engineers seeking to modernize assembly line processes. His work remains relevant for students and researchers exploring low-cost, AI-driven inspection systems in electronics manufacturing.
Research Focus
Key Achievements
Top Papers
- 1