Papers
2
Total Citations
22
H-Index
2
About
Jinhui Lan is a leading researcher in intelligent manufacturing and optical sensing, with a primary focus on laser cleaning process optimization and surface quality assessment. Her work bridges computer vision and industrial automation, developing novel methods to estimate surface roughness—a critical parameter in evaluating laser cleaning effectiveness. In her 2022 study, Lan introduced an enhanced Gray-Level Co-occurrence Matrix (GLCM) feature extraction combined with an Improved Particle Swarm Optimization-Support Vector Regression (IPSO-SVR) model, achieving high-accuracy roughness estimation from visible-light camera images. This approach, cited 12 times, offers a non-contact, cost-effective alternative to traditional profilometry. Her earlier 2020 paper proposed a two-stage algorithm for automatic process parameter tuning and surface roughness estimation, using a Cartesian robot system to collect images and implement cleaning. With 10 citations, this work laid the foundation for adaptive laser cleaning systems that can self-optimize without manual intervention. Lan’s contributions are particularly impactful for industries requiring precision surface treatment, such as aerospace and automotive manufacturing. Her integration of machine learning with optical metrology represents a significant step toward fully automated, quality-controlled laser cleaning processes.
Research Focus
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Top Papers
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