Assoc Prof Dr. Zhifen Zhang | Intelligent Manufacturing | Best Researcher Award

Assoc Prof Dr. Zhifen Zhang, Intelligent Manufacturing, Best Researcher Award

Zhifen Zhang at Xi’an Jiao Tong University, China

Professional Profile

Summary:

Assoc. Prof. Dr. Zhifen Zhang is a leading figure in intelligent manufacturing, specializing in the integration of advanced monitoring technologies for metal processing. Currently a tenure-track associate professor at Xi’an Jiao Tong University, her research spans laser welding, laser shock peening, and additive manufacturing. With over 15 major projects and funding of up to 7 million RMB, Dr. Zhang has made significant strides in multisensory data fusion and explainable deep learning applications. Her innovative approach addresses online detection of porosity defects, crucial for the aerospace industry, and ensures quality and stability in manufacturing processes. Dr. Zhang’s contributions have solidified her reputation as a pioneer in her field.

👩‍🎓Education:

  • Ph.D. in Mechanical Engineering
    • Xi’an Jiao Tong University, China
  • Postdoctoral Research in Mechanical Engineering
    • Xi’an Jiao Tong University, China (2015.10 – 2018.10)

🧬 Professional Experience:

  • Associate Professor (Tenure)
    • School of Mechanical Engineering, Xi’an Jiao Tong University, China
    • 2022.09 – Present
  • Associate Professor
    • School of Mechanical Engineering, Xi’an Jiao Tong University, China
    • 2019.11 – 2022.09
  • Lecturer
    • School of Mechanical Engineering, Xi’an Jiao Tong University, China
    • 2015.10 – 2019.11
  • Postdoctoral Researcher
    • School of Mechanical Engineering, Xi’an Jiao Tong University, China
    • 2015.10 – 2018.10

 

Research Interests:

Assoc. Prof. Dr. Zhifen Zhang’s research interests lie at the intersection of advanced monitoring technologies and intelligent manufacturing processes. With a focus on metal processing, her work spans laser welding, laser shock peening, machining, and additive manufacturing. Dr. Zhang specializes in the integration of acoustic, optical, and thermal infrared sensing techniques to develop innovative monitoring systems. She is particularly interested in the fusion of heterogeneous information sources and the application of explainable deep learning methods in manufacturing. Her research addresses critical challenges in defect detection, residual stress characterization, and stability evaluation, with implications for aerospace components and the broader manufacturing industry. Dr. Zhang’s interdisciplinary approach and commitment to innovation contribute to advancements in quality control and process optimization, ensuring the reliability and efficiency of manufacturing operations.

 

Publications Top Noted:

Paper Title: An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals
  • Authors: Qin, R., Huang, J., Zhang, Z., He, W., Chen, X.
  • Journal: Mechanical Systems and Signal Processing
  • Volume: 208
  • Pages: 111031
  • Year: 2024
  • Citations: 2
Paper Title: A novel physically interpretable end-to-end network for stress monitoring in laser shock peening
  • Authors: Qin, R., Zhang, Z., Huang, J., Wen, G., He, W.
  • Journal: Computers in Industry
  • Volume: 155
  • Pages: 104060
  • Year: 2024
  • Citations: 1
Paper Title: Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network
  • Authors: Huang, J., Zhang, Z., Qin, R., Cheng, W., Chen, X.
  • Journal: Journal of Manufacturing Systems
  • Volume: 72
  • Pages: 93–103
  • Year: 2024
  • Citations: 1
Paper Title: Disturbance-Compensation-Based Predictive Sliding Mode Control for Aero-Engine Networked Systems With Multiple Uncertainties
  • Authors: Song, P., Yang, Q., Li, D., Zhang, Z., Peng, J.
  • Journal: IEEE Transactions on Automation Science and Engineering
  • Year: 2024
  • Citations: 1
Paper Title: On-line defect recognition of MIG lap welding for stainless steel sheet based on weld image and CMT voltage: Feature fusion and attention weights visualization
  • Authors: Wang, J., Zhang, Z., Bai, Z., Huang, J., Wen, G.
  • Journal: Journal of Manufacturing Processes
  • Volume: 108
  • Pages: 430–444
  • Year: 2023
  • Citations: 1