Dinuka Ravimal
Papers
2
Total Citations
36
H-Index
2
About
Dinuka Ravimal is a researcher advancing automated surface inspection and precision manufacturing through machine vision and optical metrology. His primary research areas include image-based surface texture analysis, lapping process automation, and non-contact roughness measurement for industrial mold production. Ravimal’s most cited work, “Image-Based Inspection Technique of a Machined Metal Surface for an Unmanned Lapping Process” (2019, 29 citations), introduces a novel machine vision framework that replaces skilled manual inspection with automated classification of surface textures on large-scale mold products for automotive, television, and refrigerator manufacturing. This contribution directly addresses the bottleneck of human-dependent quality control in lapping processes. In related work, “Field surface roughness levelling of the lapping metal surface using specular white light” (2022, 7 citations), he explores the use of specular white light to achieve real-time roughness leveling, enhancing precision and repeatability. Ravimal’s research bridges computer vision and manufacturing engineering, offering practical solutions for unmanned, high-throughput production environments. His work is particularly impactful for industries seeking to reduce labor costs and improve consistency in surface finishing.
Research Focus
Key Achievements
Top Papers
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