Jinsheng Liang | Engineering | Best Researcher Award

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Dr. Jinsheng Liang | Engineering | Best Researcher Award

PhD Candidate at Shenyang Institute of Automation, Chinese Academy of Science, China

Dr. Jinsheng Liang is a distinguished researcher specializing in ultra-precision machining and water jet-guided laser technology. He earned his Bachelor of Engineering from Wuhan University of Technology and is currently pursuing a Doctorate in Engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences. His research focuses on fluid flow characteristics, laser transmission mechanisms, and high-efficiency milling techniques, contributing to advancements in precision manufacturing. Dr. Liang has played a key role in national research projects, particularly in enhancing the stability and efficiency of light-guiding liquid beams in laser processing. He has published five high-impact papers in The International Journal of Advanced Manufacturing Technology and Optics & Laser Technology, demonstrating expertise in fluid simulation and mechanical manufacturing. With strong technical skills and a commitment to innovation, Dr. Liang continues to push the boundaries of laser machining technology, aiming to bridge the gap between academic research and industrial applications.

Professional Profile 

Education

Dr. Jinsheng Liang has a strong academic background in mechanical engineering and ultra-precision machining. He is currently pursuing a Doctor of Engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences, specializing in mechanical manufacturing and automation. His doctoral research focuses on water jet-guided laser technology, fluid flow simulation, and high-precision machining. Prior to this, he earned his Bachelor of Engineering in mechanical design, manufacturing, and automation from Wuhan University of Technology in 2019. Throughout his academic journey, Dr. Liang has gained extensive expertise in laser machining techniques, fluid dynamics, and numerical simulations, contributing to cutting-edge research in precision manufacturing. His educational background, combined with hands-on research experience, has positioned him as a promising expert in his field, bridging theoretical knowledge with practical applications to advance high-efficiency laser processing technologies.

Professional Experience

Dr. Jinsheng Liang has extensive research experience in ultra-precision machining and water jet-guided laser technology. Since 2019, he has been pursuing his Doctor of Engineering at the Shenyang Institute of Automation, Chinese Academy of Sciences, where he has been actively involved in national research projects. His key contributions include research on laser electrolysis composite high-efficiency milling technology and the stability of internal light-guiding liquid beams and laser transmission mechanisms. He has utilized Fluent software for fluid simulations, combining theoretical modeling with experimental validation to enhance laser machining precision. Dr. Liang has published five high-impact papers in renowned journals, solidifying his expertise in laser technology, fluid simulation, and mechanical manufacturing. His work significantly contributes to advancements in high-precision manufacturing, and his ability to integrate research findings with industrial applications underscores his potential as a leading researcher in laser machining and automation.

Research Interest

Dr. Jinsheng Liang’s research interests lie in the fields of laser technology, fluid simulation, and mechanical manufacturing, with a particular focus on ultra-precision machining and water jet-guided laser technology. His work explores fluid flow characteristics, laser transmission mechanisms, and high-efficiency milling techniques, aiming to improve the precision and stability of laser processing. He specializes in the numerical simulation of liquid-guided laser beams, using Fluent software to model fluid behavior and enhance machining accuracy. His research also extends to the development of advanced laser processing methods for complex materials, with potential applications in aerospace, electronics, and high-tech manufacturing. Through his studies, Dr. Liang seeks to bridge the gap between theoretical modeling and experimental validation, contributing to the advancement of next-generation laser machining technologies. His expertise in precision engineering and automation positions him as a key contributor to the future of high-precision manufacturing.

Award and Honor

Currently, there are no explicitly listed awards and honors for Dr. Jinsheng Liang. However, his significant contributions to ultra-precision machining and water jet-guided laser technology highlight his growing impact in the field of mechanical manufacturing and automation. As a doctoral researcher at the Shenyang Institute of Automation, Chinese Academy of Sciences, he has been actively involved in national research projects, demonstrating excellence in fluid simulation, laser transmission mechanisms, and high-efficiency milling techniques. His five high-impact publications in prestigious journals, such as The International Journal of Advanced Manufacturing Technology and Optics & Laser Technology, reflect the recognition of his work within the scientific community. Given his expertise and research accomplishments, Dr. Liang is a strong candidate for future academic awards, industry recognitions, and research grants. His contributions to precision laser machining and automation continue to position him as an emerging leader in the field.

Research Skill

Dr. Jinsheng Liang possesses advanced research skills in laser technology, fluid simulation, and mechanical manufacturing, with a strong focus on ultra-precision machining and water jet-guided laser technology. He is proficient in numerical simulation and computational fluid dynamics (CFD), utilizing Fluent software to analyze fluid flow characteristics and laser transmission mechanisms. His expertise extends to experimental validation, where he integrates simulation results with real-world laser machining processes to enhance precision and efficiency. Dr. Liang has a deep understanding of laser-material interactions, milling techniques, and high-efficiency processing methods, allowing him to contribute to cutting-edge manufacturing advancements. His ability to design and execute complex experiments, analyze large datasets, and optimize machining parameters makes him a valuable researcher in the field. With five high-impact journal publications, he demonstrates strong skills in technical writing, data interpretation, and problem-solving, essential for advancing high-precision laser processing technologies.

Conclusion

Jinsheng Liang is a strong candidate for the Best Researcher Award due to his specialized expertise, impactful research, and high-quality publications. His contributions to ultra-precision machining and laser technology are commendable, and his ability to conduct numerical simulations and experimental studies is impressive. Strengthening industry impact and international collaboration would further elevate his profile.

Publications Top Noted

Authors: Jinsheng Liang, Hongchao Qiao, Jibin Zhao, Yuting Zhang, Qing Zhang
Year: 2025
Journal: Optics and Laser Technology
Title: Simulation and experimental study on double staggered-axis air-assisted water jet-guided laser film cooling hole machining

Yi Sun | Engineering | Best Researcher Award

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Dr. Yi Sun | Engineering | Best Researcher Award

Southwest Jiaotong University, China

Dr. Yi Sun is a distinguished researcher specializing in equipment status monitoring, health indicator construction, and deep learning applications. Currently pursuing a Ph.D. in Mechanical and Electronic Engineering at Southwest Jiaotong University, he has an impressive academic track record with 12 published papers, including 7 SCI papers, 4 of which are in top-tier JCR Q1 journals. His research contributions include developing predictive maintenance algorithms, process parameter optimization, and aerodynamic identification models for hypersonic wind tunnels. He has also led industry projects in predictive maintenance systems and multi-source aerodynamic data fusion. Recognized with multiple National Scholarships and industry accolades such as Huawei’s “Rising Star” award, Dr. Sun demonstrates exceptional expertise in both academic research and practical applications. His work bridges the gap between theoretical advancements and industrial innovation, positioning him as a leading figure in mechanical engineering and deep learning-based monitoring systems.

Professional Profile 

Education

Dr. Yi Sun has a strong educational background in mechanical engineering and electronic systems. He earned his Bachelor’s degree in Mechanical Engineering and Automation from Zhengzhou University (2012-2016), where he built a solid foundation in engineering principles. He then pursued a Master’s degree in Mechanical Engineering at Southwest Jiaotong University (2017-2020), where he gained expertise in advanced manufacturing processes, equipment monitoring, and fault diagnosis. Currently, he is undertaking a Ph.D. in Mechanical and Electronic Engineering at Southwest Jiaotong University (2021-2025), focusing on deep learning applications, health indicator construction, and predictive maintenance for industrial systems. Throughout his academic journey, he has been recognized with prestigious honors, including National Scholarships and Outstanding Graduate Student awards. His education has provided him with a unique blend of theoretical knowledge and practical experience, enabling him to contribute significantly to both academia and industry in the fields of mechanical engineering and intelligent monitoring systems.

Professional Experience

Dr. Yi Sun has a diverse professional background spanning both academia and industry. He worked as an R&D Engineer at Huawei Technologies Co., Ltd. (2020-2021), where he contributed to cutting-edge research and development in predictive maintenance and equipment monitoring. His industry experience provided him with hands-on expertise in software and hardware integration, sensor selection, and algorithm development for real-world applications. As a Ph.D. researcher at Southwest Jiaotong University (2021-present), he has led multiple high-impact projects, including the development of predictive maintenance systems for CNC machine tools and multi-source aerodynamic data fusion models for the China Aerodynamics Research and Development Center. His research has resulted in 12 published papers, several in top-tier journals, and numerous awards for academic excellence. Dr. Sun’s professional journey demonstrates his ability to bridge the gap between theoretical research and industrial innovation, making significant contributions to mechanical engineering and deep learning-based monitoring technologies.

Research Interest

Dr. Yi Sun’s research interests lie at the intersection of mechanical engineering, deep learning, and intelligent monitoring systems. His work focuses on equipment status monitoring, health indicator construction, fault diagnosis, and predictive maintenance for industrial applications. He specializes in process parameter optimization, particularly in milling cutter status assessment, utilizing advanced signal analysis, noise reduction, and online monitoring techniques. His expertise extends to deep learning-based fault detection, including the development of aerodynamic force identification models and transfer learning techniques for aerodynamic data analysis in hypersonic wind tunnels. Dr. Sun is also engaged in multi-source data fusion, enhancing accuracy and consistency in industrial systems. His research aims to optimize mechanical performance, reduce downtime, and improve system reliability through AI-driven solutions. By integrating machine learning with mechanical systems, he contributes to advancing intelligent manufacturing, predictive maintenance, and next-generation industrial automation technologies.

Award and Honor

Dr. Yi Sun has received numerous prestigious awards and honors in recognition of his outstanding academic and research achievements. During his master’s and Ph.D. studies, he was awarded the National Scholarship, one of the highest academic honors in China, for his excellence in research and academics. He was also recognized as an Outstanding Graduate Student at both the university and provincial levels. His exceptional contributions to mechanical engineering and intelligent monitoring systems earned him the Mingcheng Award and the Comprehensive Quality A-Level Certificate during his postgraduate studies. In the corporate sector, he was honored as an Excellent Student in Huawei’s New Employee Training Camp and received the Huawei “Rising Star” Award for his innovative contributions. These accolades reflect his dedication, innovation, and leadership in academia and industry. Dr. Sun’s achievements highlight his remarkable research capabilities and his potential to drive advancements in intelligent manufacturing and predictive maintenance systems.

Research Skill

Dr. Yi Sun possesses exceptional research skills in mechanical engineering, deep learning, and intelligent monitoring systems. His expertise includes equipment status monitoring, fault diagnosis, health indicator construction, and predictive maintenance. He is proficient in signal processing, noise reduction, and multi-source data fusion, enabling accurate real-time monitoring and fault prediction for industrial systems. His strong foundation in deep learning and machine learning algorithms allows him to develop advanced models for aerodynamic force identification and process parameter optimization. Dr. Sun is skilled in software and hardware development, including sensor selection, data acquisition, edge computing, and algorithm integration for predictive maintenance systems. He also excels in scientific writing, publishing high-impact research in top-tier journals and presenting at international conferences. His ability to combine theoretical research with practical industrial applications demonstrates his versatility and innovation, making significant contributions to the advancement of intelligent manufacturing and mechanical system optimization.

Conclusion

Sun Yi is highly suitable for the Best Researcher Award due to his exceptional publication record, innovative contributions to equipment status monitoring and deep learning, industry experience, and leadership in research projects. While he could enhance his application with patents, tech commercialization, and broader collaborations, his current achievements make him a strong candidate for the award. 🚀

Publications Top Noted

  • L. Wei, Y. Sun, J. Zeng, S. Qu (2022). “Experimental and numerical investigation of fatigue failure for metro bogie cowcatchers due to modal vibration and stress induced by rail corrugation.” Engineering Failure Analysis, 142, 106810. Citations: 31

  • Y. Sun, L. Wei, C. Liu, H. Dai, S. Qu, W. Zhao (2022). “Dynamic stress analysis of a metro bogie due to wheel out-of-roundness based on multibody dynamics algorithm.” Engineering Failure Analysis, 134, 106051. Citations: 22

  • J. Mu, J. Zeng, C. Huang, Y. Sun, H. Sang (2022). “Experimental and numerical investigation into development mechanism of wheel polygonalization.” Engineering Failure Analysis, 136, 106152. Citations: 21

  • Y. Li, H. Dai, Y. Qi, S. Qu, Y. Sun (2023). “Experimental study of bogie instability monitoring and suppression measures for high-speed EMUs.” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit. Citations: 6

  • Y. Sun, L. Wei, H. Dai, C. Liu, S. Qu, Y. Qi (2023). “Influence of rail weld irregularity on dynamic stress of bogie frame based on vehicle-track rigid-flexible coupled model.” Journal of Vibration and Control, 29 (17-18), 4172-4185. Citations: 5

  • Y. Sun, L. Wei, S. Qu, H. Dai (2024). “Fatigue stress estimation of metro bogie frame through frequency response functions by using limited sensors.” Structural Health Monitoring, 23 (1), 421-442. Citations: 2

  • Y. Sun, L. Wei, H. Dai (2024). “Indirect Dynamic Stress Measurement of Metro Bogie Using LSTM Network in Frequency Domain.” IEEE Sensors Journal.