Xinyan Wen
Papers
1
Total Citations
21
H-Index
1
About
Xinyan Wen is a leading researcher in the field of intelligent manufacturing and machine vision, with a primary focus on advancing quality control systems for industrial production. Her most notable contribution is the development of an embedded machine vision-based detection algorithm for identifying surface defects in lithium batteries, a critical innovation for the rapidly growing energy storage sector. This work, published in 2021 and cited 21 times, directly addresses the industry's reliance on slow, error-prone manual inspection by introducing an automated, robotic visual inspection solution. By integrating embedded systems with machine learning, Wen's algorithm significantly reduces inspection workload and error rates, enhancing both production efficiency and product safety. Her research sits at the intersection of computer vision, embedded systems, and industrial automation, demonstrating a practical, high-impact approach to solving real-world manufacturing challenges. Wen’s work is particularly valuable for researchers and engineers seeking to deploy cost-effective, real-time defect detection in high-stakes environments like battery production, where quality is paramount.
Research Focus
Key Achievements
Top Papers
- 1