Prof. Dr. Tao Peng
School of Mechanical Engineering, Zhejiang University, Hangzhou 310058, China
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Academic Career Length
15Years (2012-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2018 in International Journal of Precision Engineering and Manufacturing-Green Technology
Journal Article
Tao PengCCChao Chen
Chao Chen
1 June 2014 in The International Journal of Advanced Manufacturing Technology
Journal Article
22 February 2024 in Chinese Journal of Mechanical Engineering
Journal Article
QHQiqi He
Qiqi He
LLLi Li
Li LiDLDai Li
Dai LiTao PengXZXiangying Zhang
Xiangying Zhang
RTRenzhong Tang
Renzhong Tang +2
The human digital twin (HDT) emerges as a promising human-centric technology in Industry 5.0, but challenges remain in human modeling and simulation. Digital human modeling (DHM) provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis. However, it has limitations in real-time data usage, personalized services, and timely interaction. The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment. Hence, this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective. The framework comprises the physical twin, the virtual twin, and the linkage between these two. The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation. HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis, timely feedback, and bidirectional interactions. Finally, the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed. In general, this study outlines a human factors perspective on HDT for the first time, which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.
21 May 2020 in IEEE/ASME Transactions on Mechatronics
Journal Article
YWYuanbin Wang
Yuanbin WangJHJiakang Huang
Jiakang Huang
YWYuan Wang
Yuan Wang
SFSihang Feng
Sihang Feng
Tao PengJZJun Zou
Jun Zou +1
Additive manufacturing has been increasingly applied. As one of the most commonly used technologies, fused deposition modeling (FDM) still faces the challenge of instable performance. The appearance of the printed part is an important feature to assess its quality. As FDM processes usually take a long time, it is very important to timely identify the defects to avoid unnecessary waste of time and cost. At current stage, this identification work is usually done by the operators. However, it is difficult to realize continuous monitoring for multiple printers and identify surface defects shortly. With the advanced artificial intelligence techniques, a vision-based adaptive monitoring system is proposed in this article to achieve online monitoring with high efficiency and accuracy. The system design is introduced for common FDM printers that allows one camera to move to different angles and capture the images of the printing part. A heuristic algorithm is then proposed to achieve adaptive shooting position planning according to the part geometries. Furthermore, a convolutional neural network-based model is designed to achieve efficient defect classification with high accuracy. A series of experiments have been conducted to illustrate the effectiveness of the proposed system.
1 January 2020 in Computers & Industrial Engineering
Journal Article
AMArfan Majeed
Arfan MajeedYZYingfeng Zhang
Yingfeng Zhang
JLJingxiang Lv
Jingxiang Lv
Tao PengZAZahid Atta
Zahid Atta
AAAltaf Ahmed
Altaf Ahmed
7 August 2023 in Journal of Manufacturing Systems
Journal Article
About Me
Dr. Zhongwei Zhang received a Ph.D. degree in mechanical engineering from Zhejiang University, Hangzhou, China, in 2016. Since 2016, he has been an Assistant Professor at the School of Mechanical and Electrical Engineering, Henan University of Technology. He is the author of more than forty articles and more than ten inventions. His research interests include sustainable manufacturing, manufacturing system optimization and scheduling, and big-data applications in the manufacturing industry.
Dr. Tao Peng obtained his undergraduate degree in automation from Xi'an Jiao Tong University in 2006, his master's degree in control science and engineering from Xi'an Jiao Tong University in 2009, and his doctoral degree in mechanical engineering from the University of Auckland in 2014. From February 2012 to November 2013, he was a librarian at the Engineering Library of the University of Alberta. From January 2015 to December 2019, he served as
a lecturer at the Industrial Engineering Research Institute of the School of Mechanical Engineering at Zhejiang University. From January 2020 to the present, he has served as an associate professor at the Institute of Industrial
Engineering, School of Mechanical Engineering, Zhejiang University. Research: green intelligent manufacturing, smart technologies for collaborative manufacturing, big data science and analytics, logistics and supply chain design and management.