Yurong Wang | Additive manufacturing | Best Researcher Award

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Mr. Yurong Wang | Additive manufacturing | Best Researcher Award

Mr at  Tsinghua University, China

A PhD candidate in Mechanical Engineering at Sichuan University, this researcher specializes in additive manufacturing, powder bed fusion, and advanced material processes. With a passion for material characterization and innovation, they strive to advance mechanical engineering technologies.

Professional Profiles:

orcid

🎓 Education

PhD Student (Mechanical Engineering) – Sichuan UniversityMaster’s (Mechanical Engineering) – Tsinghua University & Guangxi UniversityBachelor’s (Mechanical and Vehicle Engineering) – Hunan University

💼 Experience

Research assistant in additive manufacturing projects at Sichuan UniversityIntern at advanced materials lab, Tsinghua UniversityUndergraduate researcher in mechanical design at Hunan University

🏆 Awards and Honors

Best Graduate Research Award – Sichuan UniversityOutstanding Master’s Thesis Award – Tsinghua UniversityInnovation Excellence Award – Guangxi University

🔍 Research Focus

Additive Manufacturing 🛠️Powder Bed Fusion ⚙️Advanced Material Processes 🔩Material Characterization 🧪

✍️Publications Top Note 

Strengthened Microstructure and Mechanical Properties of Austenitic 316L Stainless Steels by Grain Refinement and Solute Segregation

Journal of Materials Research and Technology (2025)
DOI: 10.1016/j.jmrt.2024.12.086
Authors: Yurong Wang, Buwei Xiao, Xiaoyu Liang, Huabei Peng, Jun Zhou, Feng Lin

This study explores how refining grain structure and promoting solute segregation enhances the mechanical properties of 316L stainless steel. The findings reveal improved strength and toughness, making it a promising material for advanced engineering applications.

2. Effect of Laser Energy on Anisotropic Material Properties of a Novel Austenitic Stainless Steel with a Fine-Grained Microstructure
Journal of Manufacturing and Materials Processing

This paper investigates the influence of laser energy on the anisotropic properties of fine-grained austenitic stainless steel. The research highlights how laser processing parameters can optimize material performance, contributing to advancements in additive manufacturing.

Conclusion

This individual is highly suitable for the Best Researcher Award, as they have a strong educational background, expertise in cutting-edge research areas, and the potential for impactful contributions to additive manufacturing and advanced materials science. They demonstrate the qualities of a forward-thinking, innovative researcher poised to make significant strides in their field. With continued focus on publishing high-quality research and fostering industry partnerships, their potential to achieve even greater success and recognition is substantial.

 

Xiangyan Zhang | wafer defect detection | Best Researcher Award

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Dr. Xiangyan Zhang | wafer defect detection | Best Researcher Award

Dr. Beijing University of Posts and Telecommunications , China

Xiangyan Zhang, a Ph.D. student at the School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications, has a robust academic background with a Master of Engineering degree from Beijing University of  Science and Technology (2023). His research focuses on wafer defect detection and machine vision, with significant contributions including DMWMNet, a dual-branch multi-level convolutional network achieving high performance in wafer map defect detection. Zhang has published 4 SCI papers, 2 EI conference papers, holds 2 invention patents, and 3 software copyrights. He collaborates with the China Academy of Engineering Physics

 

Professional Profiles:

Orcid

Academic and Professional Background 📚👩‍🎓

In June 2023, I was awarded a Master of Engineering degree from Beijing University of Science and Technology, and in September 2023, I commenced my Ph.D. studies at Beijing University of Posts and Telecommunications. To date, I have published 4 SCI papers, 2 EI conference papers, granted 2 invention patents, and obtained 3 software copyrights.

Research and Innovations 🔬💡

Completed/Ongoing Research Projects 🚀Vision-based robotic grasp detection projectWafer defect detection project

Citation Index 📑

Zhang, X., Jiang, Z., Yang, H., Mo, Y., Zhou, L., Zhang, Y., Li, J., Wei, S. (2024). DMWMNet: A novel dual-branch multi-level convolutional network for high-performance mixed-type wafer map defect detection in semiconductor manufacturing. Computers in Industry, 161, 104136

✍️Publications Top Note :

Patent Authorization Number: ZL202210817429.4
A six-degree-of-freedom grasping detection algorithm based on semantic segmentation networks.

Patent Application Number: 202310654572.0
A grasping detection network based on RGBD images and semantic segmentation for residual fitting.

Zhang, Xiangyan, et al. (2024): DMWMNet: A novel dual-branch multi-level convolutional network for high-performance mixed-type wafer map defect detection in semiconductor manufacturing. Computers in Industry, 161, 104136.

Zhang Qinjian†, Zhang Xiangyan†, et al. (2022): TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce. Frontiers in Plant Science, 13.

Wu Yalin, Zhang Qinjian, Zhang Xiangyan, et al. (2022):* Novel binary logistic regression model based on feature transformation of XGBoost for type 2 Diabetes Mellitus prediction in healthcare systems. Future Generation Computer Systems-the International Journal of Escience, 129: 1-12.

Zhang Wu, Li Haiyuan, Zhang Xiangyan, et al. (2021):* Research progress and development trend of surgical robot and surgical instrument arm. International Journal of Medical Robotics and Computer Assisted Surgery, 17(5).

Zhang Xiangyan, Li Haiyuan, et al. (2021):* Kinematics Analysis and Grasping Simulation of a Humanoid Underactuated Dexterous Hand. 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO): 55-60.

Zhang Qinjian, Zhang Xiangyan, Li Haiyuan (2022):* A Grasp Pose Detection Network Based on the DeepLabv3+ Semantic Segmentation Model. International Conference on Intelligent Robotics and Applications (ICIRA): 747-758. (EI)