Dr. Zhiwei Zhang, AVIC Manufacturing Technology Institute, China
Zhiwei Zhang, is a research engineer specializing in aviation manufacturing technology in China. He holds a bachelor’s and master’s degree in Automation from Shenyang Ligong University and earned his Ph.D. in Instrument Science and Technology from Yanshan University. His research focuses on digital radiographic and industrial CT nondestructive testing, computer vision, and ensemble learning algorithms for additive manufacturing. He has published seven SCI-indexed research papers and holds two authorized patents. Zhiwei Zhang also serves as a reviewer for the Journal of Computational Methods in Sciences and Engineering, reflecting his active contribution to the academic and industrial research community.
Professional Profile:
ORCID
Summary of Suitability for Best Researcher Award: Zhiwei Zhang
Zhiwei Zhang, a highly skilled research engineer in aviation manufacturing technology, has demonstrated outstanding contributions in the fields of nondestructive testing, computer vision, and ensemble learning for additive manufacturing. His innovative research integrates cutting-edge technologies like digital radiography, industrial CT, and machine learning, addressing critical challenges in the aerospace industry.
🎓 Education
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🏫 Bachelor’s Degree in Automation – Shenyang Ligong University
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🎓 Master’s Degree in Automation – Shenyang Ligong University
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🧪 Ph.D. in Instrument Science and Technology – Yanshan University
💼 Work Experience
🏆 Achievements
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📄 Published 7 SCI-indexed research papers in high-impact journals
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🧾 Granted 2 authorized patents
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🧑⚖️ Reviewer for the Journal of Computational Methods in Sciences and Engineering
🎖️ Awards & Honors
Publication Top Notes:
A Hybrid Framework for Metal Artifact Suppression in CT Imaging of Metal Lattice Structures via Radon Transform and Attention-Based Super-Resolution Reconstruction
Complex Defects Detection of 3-D-Printed Lattice Structures: Accuracy and Scale Improvement in YOLO V7
A Prediction Model for Maximum Stress of Additive Manufacturing Lattice Structures Based on Voting-Cascading
Deep convolution IT2 fuzzy system with adaptive variable selection method for ultra-short-term wind speed prediction
An improved meta heuristic IT2 fuzzy model for nondestructive failure evaluation of metal additive manufacturing lattice structure
An improved stacking ensemble learning model for predicting the effect of lattice structure defects on yield stress
Data-driven XGBoost model for maximum stress prediction of additive manufactured lattice structures
Adaptive Defect Detection for 3-D Printed Lattice Structures Based on Improved Faster R-CNN
A Hybrid Model Based on Jensen’s Inequality Theory for 3D Printed Lattice Structures Maximum Stress Prediction