Xuanze Wang
Papers
1
Total Citations
2
H-Index
1
About
Xuanze Wang is a researcher specializing in mechanical engineering and image-based classification, with a particular focus on applying fractal geometry to industrial inspection. His most cited work, “Image classification and recognition method to mechanical parts based on fractal dimension” (2017), introduces a novel approach that leverages the statistical self-similarity and irregularity of complex mechanical components. By using fractal dimension as a quantitative measurement, Wang demonstrates how these values can effectively classify and recognize parts, offering a robust alternative to traditional shape-based recognition methods. This work has garnered 2 citations and provides a foundational framework for integrating fractal analysis into automated quality control and manufacturing processes. Wang’s contributions lie at the intersection of computer vision and mechanical design, where his fractal-based methodology enhances the precision and adaptability of part identification systems. His research is particularly valuable for industries requiring high-accuracy sorting or defect detection in irregularly shaped components. Through this work, Xuanze Wang advances the practical application of fractal theory in engineering, offering a scalable solution for modern industrial automation and intelligent manufacturing.
Research Focus
Key Achievements
Top Papers
- 1