Papers
1
Total Citations
12
H-Index
1
About
Jianyue Ge is a researcher whose work bridges optical engineering and intelligent manufacturing, with a primary focus on laser cleaning and surface quality assessment. His most notable contribution is a novel method for estimating workpiece surface roughness after laser cleaning, detailed in his 2022 paper “Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR,” which has garnered 12 citations. In this work, Ge pioneered the use of a Cartesian robot and visible-light camera to capture extensive surface images, then applied enhanced Gray-Level Co-occurrence Matrix (GLCM) features combined with an Improved Particle Swarm Optimization-Support Vector Regression (IPSO-SVR) model to accurately predict roughness. This approach offers a non-contact, efficient alternative to traditional measurement techniques, directly impacting quality control in industrial cleaning processes. Ge’s research is particularly valuable for applications in automotive, aerospace, and cultural heritage restoration, where precise surface characterization is critical. By integrating machine vision with advanced optimization algorithms, he has opened new pathways for real-time, automated inspection in laser material processing.
Research Focus
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Top Papers
- 1