Mahsa Mohammadikaji
Papers
1
Total Citations
5
H-Index
1
About
Mahsa Mohammadikaji is a researcher at the intersection of computer vision, optical metrology, and automated inspection. Her primary contributions lie in advancing **Computer-Aided Inspection Planning (CAIP)** for machine vision systems, particularly in the domain of automated optical inspection (AOI). Her most cited work, "Image formation simulation for computer-aided inspection planning of machine vision systems" (2017, 5 citations), introduces a powerful simulation toolset that models image formation to optimize the design and planning of inspection setups. By integrating a versatile two-robot verification system, she enables engineers to narrow the vast design space of optical configurations, reducing costly trial-and-error in industrial quality control. This work bridges simulation and real-world deployment, offering a systematic approach to planning high-precision inspection tasks. Her research is pivotal for industries relying on defect detection and dimensional measurement, where accurate simulation can dramatically improve system reliability and efficiency. Mohammadikaji’s contributions underscore the growing importance of virtual prototyping in manufacturing, making her a notable figure in applied computer vision and metrology.
Research Focus
Key Achievements
Top Papers
- 1