Yujia Ding
Papers
1
Total Citations
6
H-Index
1
About
Yujia Ding is a leading researcher in advanced manufacturing, specializing in precision grinding, process monitoring, and intelligent wear analysis for superalloy materials. Her most-cited work, "Image-based wear state evolution and in-process recognition method for abrasive belt grinding of GH4169" (2025, 6 citations), introduces a novel computer vision approach to track and classify abrasive belt wear in real time during the grinding of GH4169—a nickel-based superalloy critical to aerospace and energy industries. By integrating image processing with machine learning, Ding’s method enables non-contact, in-process wear recognition, significantly improving machining efficiency and surface quality while reducing downtime. This contribution addresses a long-standing challenge in adaptive manufacturing: accurately predicting tool degradation without interrupting production. Though early in her career, her work has already garnered attention for its practical impact on intelligent manufacturing systems. Ding’s research bridges materials science, sensor technology, and data-driven process control, offering a pathway toward fully autonomous grinding operations. Her achievements underscore a commitment to advancing Industry 4.0 solutions for high-value component fabrication.
Research Focus
Key Achievements
Top Papers
- 1