Dalian University of Technology - China
Author Profile
Scopus
🎓 Early Academic Pursuits
Dr. Zhang Pengfei began his academic journey in mechanical manufacturing and automation, earning a Bachelor of Science degree from Henan University of Science and Technology in 2014. His exceptional academic performance earned him several honors, including the BOSCH Scholarship and the National Inspirational Scholarship. Building on his undergraduate success, he pursued a Master of Science at Yanshan University, graduating in 2017 as an Outstanding Graduate. His dedication to excellence culminated in a Ph.D. from Harbin Institute of Technology in 2023, where his doctoral dissertation was recognized as Outstanding.
💼 Professional Endeavors
Currently a postdoctoral researcher under the guidance of Prof. Liming Shu at Dalian University of Technology, Dr. Zhang has actively contributed to cutting-edge projects in advanced manufacturing. He played pivotal roles in national key R&D programs, such as developing collaborative in-situ processing technologies for multi-mobile robots on large thin-walled components and creating intelligent monitoring and control systems for complex cutting tools. These projects underscore his expertise in deep hole machining and smart factory innovations.
🔬 Contributions and Research Focus
Dr. Zhang’s research interests lie in smart factory technologies, cutting processes and mechanisms, and process monitoring and control. His contributions to deep hole machining have advanced the understanding and practical applications of precision manufacturing. His work emphasizes developing intelligent monitoring systems to ensure the efficiency and accuracy of complex machining processes, which has significant implications for modern manufacturing.
🌍 Impact and Influence
Dr. Zhang’s research has had a considerable impact on the field of mechanical manufacturing. His supervision of graduate and undergraduate students highlights his commitment to fostering the next generation of engineers. His achievements, including the World Top University Strategic Cooperation Program scholarship and numerous academic honors, reflect his influence in the academic and industrial domains. His contributions to deep hole machining have been pivotal in improving machining precision and process reliability, influencing both academia and industry.
🏆Academic Cites
Dr. Zhang’s scholarly contributions have been widely recognized, with his research cited in academic publications focusing on advanced manufacturing and machining technologies. His work on deep hole machining has provided valuable insights and methodologies, furthering innovation in precision manufacturing. The high citation rate of his publications underscores the relevance and significance of his research.
🌟 Legacy and Future Contributions
As Dr. Zhang continues his postdoctoral research, his focus on integrating intelligent systems with advanced manufacturing processes promises to revolutionize the field. His ongoing work in deep hole machining and smart factories is poised to address critical challenges in modern manufacturing, solidifying his legacy as a pioneer in mechanical engineering. By mentoring students and collaborating on cutting-edge projects, Dr. Zhang ensures his contributions will resonate with future advancements in the field.
📝Deep Hole Machining
Dr. Zhang Pengfei’s expertise in deep hole machining has driven significant advancements in precision manufacturing and intelligent monitoring systems. His contributions to deep hole machining have enhanced process accuracy and reliability, making him a leader in this area. Future innovations in deep hole machining and smart manufacturing under his guidance are set to shape the future of mechanical engineering.
Notable Publication
📝Fast extraction of cutter-workpiece engagement for milling force prediction in multi-axis machining
Authors: Zhang, X., Wang, X., Zhang, P., Chen, K., Cao, F.
Journal: Measurement: Journal of the International Measurement Confederation
Year: 2024
Citations: 2
📝Quasi-real-time monitoring of variable milling parameters during multi-axis machining
Authors: Zhang, X., Wang, X., Cao, F., Zhang, P.
Journal: Mechanical Systems and Signal Processing
Year: 2024
Citations: 2
📝Boolean operation-based fast calculation of cutter-workpiece engagement during peripheral milling
Authors: Wang, X., Zhang, X., Zhang, W., Zhang, P., Chen, K.
Journal: MM Science Journal
Year: 2023
Citations: 0
📝Intelligent tool wear monitoring based on multi-channel hybrid information and deep transfer learning
Authors: Zhang, P., Gao, D., Hong, D., Wang, Z., Liao, Z.
Journal: Journal of Manufacturing Systems
Year: 2023
Citations: 25
📝Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network
Authors: Zhang, P., Gao, D., Hong, D., Zan, S., Liao, Z.
Journal: Mechanical Systems and Signal Processing
Year: 2023
Citations: 21
📝Development and testing of a wireless multi-axis toolholder dynamometer for milling and drilling process
Authors: Zhang, P., Gao, D., Lu, Y., Xie, Z., Wang, Z.
Journal: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Year: 2023
Citations: 3