Papers
2
Total Citations
33
H-Index
2
About
Meena Periya Samy’s research lies at the intersection of intelligent manufacturing and automated structural inspection, with a focus on sensor fusion, machine learning, and real-time quality control. Her major contributions include developing an in-process surface roughness estimation model for robotic abrasive belt machining, where she pioneered a multi-sensor integration technique using force, accelerometer, and acoustic emission sensors to predict surface quality during machining—eliminating the need for time-consuming off-line inspection. This work, cited 19 times, has significant implications for adaptive manufacturing and process automation. She also advanced defect detection in civil infrastructure by designing an automatic optical and laser-based system for brick masonry walls. By fusing vision and laser sensor data with a Support Vector Machine (SVM) algorithm, she created a Defect Finding Classification Model (DFCM) capable of real-time defect identification and classification. This research, with 14 citations, demonstrates her ability to bridge sensor technology and machine learning for practical, field-deployable solutions. Meena’s work is notable for its emphasis on in-process, non-destructive evaluation, making her a key contributor to smart manufacturing and structural health monitoring.
Research Focus
Key Achievements
Top Papers
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