Assist. Prof. Dr. Gan Li – High Performance Manufacturing – Excellence in Research

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Assist. Prof. Dr. Gan Li - High Performance Manufacturing - Excellence in Research

China University of Petroleum - China 

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🎓 Early academic pursuits

Gan Li began his academic journey with a dual bachelor's degree in mechanical design, manufacturing and automation, and engineering management from the china university of petroleum (east china) in 2018. his outstanding academic record and passion for high-precision engineering led him to pursue a ph.d. at the state key laboratory of high-performance precision manufacturing, school of mechanical engineering, dalian university of technology. completing his doctorate at just 26, he focused on pushing the boundaries of intelligent manufacturing and ultra-precision machining technologies, laying a strong foundation for his future innovations.

🚀 Professional endeavors

Dedicated to advancing cutting-edge engineering, dr. gan li currently serves as a lecturer at the college of mechanical and electronic engineering, china university of petroleum. he is also a vital member of the national engineering research center of marine geophysical prospecting and exploration and development equipment. his work encompasses diverse areas, including nontraditional machining, grinding technologies for hard and brittle materials, and the development of ultra-precision machine tools. his role as a researcher and educator has cemented his position as a rising star in china's manufacturing and engineering landscape.

🔬 Contributions and research focus

Dr. Li's research delves deep into intelligent manufacturing systems and ultra-precision machining. he has made significant breakthroughs in grinding technologies, particularly for materials with complex geometries and extreme hardness, such as tungsten alloys. his contributions to the development of advanced machining equipment and methodologies have led to enhanced High Performance Manufacturing precision and efficiency in industrial processes. with a strong focus on automation and simulation, his work supports the integration of new technologies in traditional manufacturing, creating innovative solutions for industrial challenges.

🏆 Accolades and recognition

Recognized for his technical ingenuity, dr. gan li has secured prestigious research grants including those from the ministry of education of the people's republic of china, the taishan scholars project, and the natural science foundation of shandong province. his innovative spirit is reflected in six granted patents, which encompass new techniques and devices for grinding operations High Performance Manufacturing and cnc machinery. each patent showcases his ability to address real-world challenges through engineering excellence, earning him a respected place among china's next generation of research leaders.

👨‍💼 Impact and influence

Dr. Li's research has directly influenced the design and manufacturing of next-generation ultra-precision equipment. his patented inventions, including grinding wheel measuring devices and High Performance Manufacturing pipe inspection robots, have paved the way for smarter, more efficient manufacturing solutions. his work not only impacts academia but also has strong implications for the mechanical and oil industries. his role in national research teams allows him to bridge academic innovation with practical application, contributing to the modernization of china’s high-performance manufacturing sector.

🌍 Legacy and future contributions

A visionary in his field, dr. gan li is committed to furthering research in ultra-precision technologies and intelligent systems. his future endeavors aim to enhance machining accuracy and automation while fostering collaboration between academic institutions and industries. by mentoring emerging researchers and engaging in transformative projects, he continues to shape the future of precision manufacturing. his legacy lies in building sustainable, intelligent machining technologies that will support innovation for years to come

Notable Publications 

  1. Title: A visualization method for cross-scale online monitoring of grinding state based on data-mechanism hybrid-driven digital twin system
    Authors: Gan Li, Haoxiang Lu, Hao Wang, Yichuan Ran, Renjie Ji, Yonghong Liu, Yanzhen Zhang, Baoping Cai, Xiaokang Yin
    Journal: Mechanical Systems and Signal Processing

  1. Title: Residual stress and subsurface damage prediction in tungsten heavy alloy face grinding
    Authors: Gan Li, Jinbo Liu, Hao Wang, Zhigang Dong, Renke Kang, Yan Bao
    Journal: Journal of Manufacturing Processes

  1. Title: Research on grinding wheel wear measurement methods: Current status and future perspectives
    Authors: Li Gan, Bao Yan, Wang Zhongwang, Kang Renke, Dong Zhigang
    Journal: 中国科学. 技术科学 (Science China Technological Sciences)

  1. Title: Wheel Wear of Tungsten Heavy Alloy Precision Grinding and Its Influence Mechanism on Surface Quality
    Authors: Li Gan, Kang Renke, Dong Zhigang, Wang Hao, Wang Zhongwang, Bao Yan
    Journal: Hunan Daxue Xuebao / Journal of Hunan University (Natural Sciences)

  1. Title: Undeformed chip thickness models for precise vertical-spindle face grinding of tungsten heavy alloy
    Authors: Gan Li, Renke Kang, Hao Wang, Yan Bao, Yidan Wang
    Journal: Precision Engineering

Prof. Liuyang song – Condition Based Maintenance – Best Researcher Award

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Prof. Liuyang song - Condition Based Maintenance - Best Researcher Award

Beijing University of Chemical Technology - China 

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Early academic pursuits 📖

Liuyang song embarked on his academic journey with a strong foundation in engineering at beijing university of chemical technology. he earned his bachelor’s degree in engineering in 2010, followed by a master’s degree in 2013, both from the same institution. his passion for mechanical and electrical engineering led him to pursue a doctorate at mie university, japan, where he completed his doctoral studies in 2017. during his phd, he delved deep into advanced mechanical systems, honing his expertise in fault diagnosis and intelligent machine learning applications.

Professional endeavors 🏛️

Liuyang song has built a distinguished career in academia, holding key positions at beijing university of chemical technology. he started as a postdoctoral researcher in 2018, gradually rising through the ranks to become a lecturer, associate professor, and ultimately, a full professor in 2024. he also gained international experience as a postdoctoral researcher and temporary researcher at mie university, japan. his extensive experience in mechanical and electrical engineering has contributed to the development of cutting-edge diagnostic techniques and performance evaluation methodologies.

Contributions and research focus 🔬

Liuyang song's research primarily focuses on intelligent fault diagnosis, machine learning applications in engineering, and vibro-acoustic signal processing. he has been at the forefront of developing innovative algorithms for aeroengine spindle bearing diagnostics using multi-task neural networks and graph-structured data. his work also explores adaptive sparse Condition Based Maintenance representation methods for vibro-acoustic multi-source signal separation, lightweight convolutional neural networks, and small-sample machine learning for rail transit train equipment diagnostics. his research has significantly improved the accuracy and efficiency of fault detection in complex mechanical systems.

Accolades and recognition 🏅

Throughout his career, liuyang song has been recognized for his outstanding contributions to mechanical and electrical engineering. his research has been supported by prestigious funding Condition Based Maintenance bodies such as the national natural science foundation of china and the beijing natural science foundation. he has successfully led multiple research projects that have advanced the field of intelligent diagnostics. his contributions to academia have solidified his reputation as a leading researcher in his domain.

Impact and influence 🌍

Liuyang song's research has had a significant impact on industrial applications, particularly in aerospace and rail transit systems. his advanced diagnostic methodologies have enhanced the Condition Based Maintenance reliability and performance of critical mechanical components, reducing operational failures and maintenance costs. his work in machine learning-driven fault diagnosis has influenced both academic and industrial research, paving the way for more intelligent and efficient monitoring systems in engineering applications.

Legacy and future contributions 🔮

With a commitment to advancing mechanical and electrical engineering, liuyang song continues to push the boundaries of research in fault diagnostics and intelligent systems. his future work aims to integrate cutting-edge artificial intelligence techniques with mechanical diagnostics to further improve the precision and adaptability of predictive maintenance strategies. as a professor, he is dedicated to mentoring the next generation of engineers and researchers, ensuring that his legacy in intelligent fault diagnosis and machine learning applications continues to thrive.

Notable Publications 

  • Task-adaptive unbiased regularization meta-learning for few-shot cross-domain fault diagnosis
    Authors: Huaqing Wang, Dongrui Lv, Tianjiao Lin, Changkun Han, Liuyang Song
    Journal: Engineering Applications of Artificial Intelligence
  • Representations aligned counterfactual domain learning for open-set fault diagnosis under speed transient conditions
    Authors: Shen Liu, Jinglong Chen, Zhen Shi, Liuyang Song, Shuilong He
    Journal: Knowledge-Based Systems
  • A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance
    Authors: Wang H., Zhang W., Han C., Fu Z., Song L.
    Journal: Measurement Science and Technology
  • A Convolutional Neural Network with Hybrid Loss Function for Bearing Fault Diagnosis
    Authors: Lv D., Fu Z., Su Z., Ni H., Wang H., Song L.
    Journal: Mechanisms and Machine Science
  • A lightweight improved residual neural network for bearing fault diagnosis
    Authors: Wang H., Fu Z., Lin T., Han C., Zhang W., Song L.
    Journal: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

Mr. Xiaopeng wang  – Intelligent Manufacturing – Best  Researcher Award 

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Mr. Xiaopeng wang  - Intelligent Manufacturing - Best  Researcher Award 

Hebei University of Science and Technology - China 

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🌟 Early academic pursuits

Xiaopeng wang embarked on his academic journey with a keen interest in welding engineering and materials science. he pursued his doctoral studies with a focus on innovative detection methods for welding defects, demonstrating a strong foundation in interdisciplinary research. his early academic work laid the groundwork for integrating deep learning algorithms with traditional welding technologies, showcasing his commitment to advancing this niche field.

🧑‍🏫 Professional endeavors

As an assistant professor at hebei university of science and technology, xiaopeng wang has contributed significantly to academia and industry. his professional career is marked by his expertise as an international welding engineer and his dedication to fostering innovation in welding defect detection. his collaborations with industry and academia reflect his role as a bridge between theoretical advancements and practical applications.

🔬 Contributions and research focus

Xiaopeng wang’s research explores the integration of deep learning with welding defect detection. his groundbreaking investigations on channel and spatial attention mechanisms have enhanced understanding of feature information entropy and improved model accuracy. his work demonstrates how attention mechanisms can amplify the focus on welding defect features, Intelligent Manufacturing leading to more precise detection and clustering of defects.

🏆 Accolades and recognition

Xiaopeng wang's contributions have earned him a respected reputation in his field. his research has been published in prestigious journals such as expert systems with applications and ndt&e international. with over 10 academic papers to his credit and a citation index of 40 on google scholar, Intelligent Manufacturing his work has gained significant acknowledgment from the scientific community.

🌍 Impact and influence

By advancing intelligent detection methods for welding defects, xiaopeng wang’s research has broad implications for both academic research and industrial applications. his insights into deep Intelligent Manufacturing learning and attention mechanisms have set new benchmarks in the field, improving defect detection processes and enhancing the efficiency and reliability of welding operations worldwide.

📚 Legacy and future contributions

Xiaopeng wang continues to push the boundaries of research, focusing on novel applications of deep learning in welding and materials science. his ongoing projects, supported by grants like the national natural science foundation of china (u2141216), reflect his dedication to pioneering advancements in his field. he aspires to mentor future researchers and foster global collaborations to expand the scope of intelligent welding technologies.

Notable Publications 

  • Title: Zoom in on the target network for the prediction of defective images and welding defects' location
    Author(s): Xiaopeng Wang, Baoxin Zhang, Xinghua Yu
    Journal: NDT & E International
  • Title: Image Analysis of the Automatic Welding Defects Detection Based on Deep Learning
    Author(s): Xiaopeng Wang, Baoxin Zhang, Jinhan Cui, Juntao Wu, Yan Li, Jinhang Li, Yunhua Tan, Xiaoming Chen, Wenliang Wu, Xinghua Yu
    Journal: Journal of Nondestructive Evaluation
  • Title: Understanding the effect of transfer learning on the automatic welding defect detection
    Author(s): Xiaopeng Wang, Xinghua Yu
    Journal: NDT & E International
  • Title: Binary classification of welding defect based on deep learning
    Author(s): Xiaopeng Wang, Xu Wang, Baoxin Zhang, Jinhan Cui, Xinpeng Lu, Chuan Ren, Weijia Cai, Xinghua Yu
    Journal: Science and Technology of Welding and Joining
  • Title: X-ray stress measurement process of aluminum alloy by analysis of the full width at half maxima
    Author(s): Xiaoyan Li
    Journal: (Not specified, please verify)