Jiewu Leng

Dr. Jiewu Leng

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Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2021 in Journal of Manufacturing Systems
Verified
Journal Article
Closed access
JLJiewu Leng
Jiewu Leng
DWDewen Wang
Dewen Wang
Weiming ShenWeiming Shen
Weiming Shen
XLXinyu Li
Xinyu Li
DZDing Zhang
Ding Zhang
XCXin Chen
Xin Chen
1 October 2020 in Renewable and Sustainable Energy Reviews
Verified
Journal Article
Closed access
JLJiewu Leng
Jiewu Leng
GRGuolei Ruan
Guolei Ruan
PJPingyu Jiang
Pingyu Jiang
KXKailin Xu
Kailin Xu
QLQiang Liu
Qiang Liu
CLChao Liu
Chao Liu
25 October 2017 in IEEE Access
Verified
Journal Article
Open access
HZHao Zhang
Hao Zhang
QLQiang Liu
Qiang Liu
XCXin Chen
Xin Chen
DZDing Zhang
Ding Zhang
JLJiewu Leng
Jiewu Leng
Various new national advanced manufacturing strategies, such as Industry 4.0, Industrial Internet, and Made in China 2025, are issued to achieve smart manufacturing, resulting in the increasing number of newly-designed workshops in both developed and developing countries. Under the individualized designing demands, more realistic virtual models mirroring the real worlds of manufacturing workshops are essential to bridge the gap between design and operation. This paper presents a digital twin-based approach for rapid individualized designing of the hollow glass production line. The digital twin merges physics-based system modeling and distributed real-time equipment/process data to generate an authoritative digital design of the manufacturing system at pre-production phase. A digital twin-based analytical decoupling framework is also developed to provide engineering analysis capabilities and support the decision-making over the system designing and solution evaluation. Three key enabling techniques as well as a case study in hollow glass production line are addressed to validate the proposed approach.
8 May 2018 in International Journal of Production Research
Verified
Journal Article
Closed access
QLQiang Liu
Qiang Liu
HZHao Zhang
Hao Zhang
JLJiewu Leng
Jiewu Leng
XCXin Chen
Xin Chen
Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology.
1 January 2021 in Journal of Manufacturing Systems
Verified
Journal Article
Closed access
QLQiang Liu
Qiang Liu
JLJiewu Leng
Jiewu Leng
DYDouxi Yan
Douxi Yan
DZDing Zhang
Ding Zhang
LWLijun Wei
Lijun Wei
XCXin Chen
Xin Chen
18 December 2020 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
Verified
Journal Article
Closed access
JLJiewu Leng
Jiewu Leng
SYShide Ye
Shide Ye
MZMan Zhou
Man Zhou
JZJ. Leon Zhao
J. Leon Zhao
DZDing Zhang
Ding Zhang
LFLeijie Fu
Leijie Fu
Blockchain is a new generation of secure information technology that is fueling business and industrial innovation. Many studies on key enabling technologies for resource organization and system operation of blockchain-secured smart manufacturing in Industry 4.0 had been conducted. However, the progression and promotion of these blockchain applications have been fundamentally impeded by various issues in scalability, flexibility, and cybersecurity. This survey discusses how blockchain systems can overcome potential cybersecurity barriers to achieving intelligence in Industry 4.0. In this regard, eight cybersecurity issues (CIs) are identified in manufacturing systems. Ten metrics for implementing blockchain applications in the manufacturing system are devised while surveying research in blockchain-secured smart manufacturing. This study reveals how these CIs have been studied in the literature. Based on insights obtained from this analysis, future research directions for blockchain-secured smart manufacturing are presented, which potentially guides research on urgent cybersecurity concerns for achieving intelligence in Industry 4.0.