Tianci Jiang
Papers
1
Total Citations
39
H-Index
1
About
Tianci Jiang is a researcher advancing the intersection of intelligent manufacturing and signal processing, with a primary focus on robotic machining and surface quality prediction. His most-cited work introduces a mutual cross-attention fusion network that integrates internal and external sensor signals to predict surface roughness in robotic machining processes. This innovative approach, published in 2025 and garnering 39 citations, addresses a critical challenge in precision manufacturing by enabling real-time, data-driven quality control. Jiang’s contributions lie in developing deep learning architectures that fuse heterogeneous data streams—such as force, vibration, and acoustic signals—to enhance prediction accuracy and robustness. His work has significant implications for adaptive machining, reducing waste and improving efficiency in automated production lines. By leveraging attention mechanisms, Jiang’s research pushes the boundaries of how machines interpret complex, multi-modal sensor data, offering a pathway toward smarter, more autonomous manufacturing systems. His achievements highlight a growing trend in applying advanced neural networks to industrial processes, making his research valuable for students and engineers interested in cyber-physical systems, digital twins, and intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1