Prof. Xianxian Wang, Beijing Polytechnic University, Mechanical and Electrical Engineering Institute, China
Dr. Xianxian Wang is a lecturer at Beijing University of Technology, specializing in intelligent sensor detection technology and structural health monitoring instrumentation. His research focuses on advancing non-destructive testing methods, particularly through micro-magnetic techniques and intelligent modeling. He has published over 10 research papers in journals such as Journal of Nondestructive Testing and Measurement Science and Technology, and contributed to the drafting of three group standards. Dr. Wang holds a Ph.D. and Master’s degree in Instrument Engineering from Beijing University of Technology, and a Bachelor’s degree in Electronic Information Engineering from Shandong University of Technology. His notable research achievements include the development of high-resolution Barkhausen noise microimaging techniques and neural network-based models for quantifying mechanical properties of ferromagnetic materials. These innovations have been applied in manufacturing settings, enhancing product quality and reliability. Dr. Wang has also received several honors, including the Third Prize of the Shandong Province Machinery Industry Science and Technology Progress Award and top distinctions in national equipment manufacturing industry competitions.
Professional Profile:
ORCID
Summary of Suitability:
Dr. Wang Xianxian, a lecturer at Beijing University of Technology, stands out as a dynamic and promising researcher in the fields of intelligent sensor testing and structural health monitoring technologies. His innovative contributions to non-destructive testing and intelligent instrumentation, along with practical industrial applications, make him a highly suitable candidate for the Best Researcher Award.
🎓 Education Background
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2019.09 – 2024.01
📍 Ph.D., Beijing University of Technology
🧪 Focus: Intelligent Sensor Testing & Structural Health Monitoring
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2016.09 – 2019.06
📍 M.Sc., Instrument Engineering, Beijing University of Technology
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2010.09 – 2014.07
📍 B.Eng., Electronic Information Engineering, Shandong University of Technology
💼 Work Experience
🧠 Research Achievements
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🧲 Developed Barkhausen noise microimaging techniques for high-resolution residual stress imaging in ferromagnetic materials.
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🛠️ Created micromagnetic non-destructive testing instruments to assess mechanical properties (hardness, strength, elongation) using neural networks.
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🤖 Proposed an intelligent modeling strategy based on “classification then regression” and adaptive transfer learning to improve robustness in non-uniform data scenarios.
🏆 Honors & Awards
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🥉Third Prize, Shandong Province Machinery Industry Science and Technology Progress Award
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🥇 First Prize, Second National Equipment Manufacturing Industry Skills Competition
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🏅Excellent Coach, Second National Equipment Manufacturing Industry Skills Competition
Publication Top Notes:
Micromagnetic and Quantitative Prediction of Yield and Tensile Strength of Carbon Steels Using Transfer Learning Method
Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods
Micromagnetic and quantitative prediction of yield strength and tensile strength in DP590 steels based on ReliefF + Clustering feature selection method
Micromagnetic and Robust Evaluation of Surface Hardness in Cr12MoV Steel Considering Repeatability of the Instrument
Surface Decarburization Depth Detection in Rods of 60Si2Mn Steel with Magnetic Barkhausen Noise Technique
Micromagnetic and Quantitative Prediction of Surface Hardness in Carbon Steels Based on a Joint Classification-Regression Method
FilterNet: A deep convolutional neural network for measuring plastic deformation from raw Barkhausen noise waveform