Y
Yingfeng Zhang
Researcher at Northwestern Polytechnical University
Publications - 124
Citations - 5008
Yingfeng Zhang is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Energy consumption & Scheduling (production processes). The author has an hindex of 35, co-authored 109 publications receiving 3571 citations. Previous affiliations of Yingfeng Zhang include Shaanxi University of Technology & Southern University of Science and Technology.
Papers
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A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products
TL;DR: An overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed that integrated big data analytics and service-driven patterns that helped to overcome barriers in the implementation of CP.
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A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions
Shan Ren,Yingfeng Zhang,Yang Liu,Yang Liu,Tomohiko Sakao,Donald Huisingh,Cecília M.V.B. Almeida +6 more
TL;DR: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and more efficient as discussed by the authors. But, the benefits of smart manufacturing are limited.
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RFID-based wireless manufacturing for real-time management of job shop WIP inventories.
TL;DR: In this article, the authors present an affordable approach to shop floor performance improvement by using wireless manufacturing (WM), which relies substantially on wireless devices such as RFID (radio frequency identification) or auto-ID sensors and wireless information networks for the collection and synchronization of the real-time field data from manufacturing workshops.
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Real-time information capturing and integration framework of the internet of manufacturing things
TL;DR: A real-time information capturing and integration architecture of the internet of manufacturing things (IoMT) is presented to provide a new paradigm by extending the techniques of IoT to manufacturing field.
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A Framework for Smart Production-Logistics Systems Based on CPS and Industrial IoT
TL;DR: A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration mechanisms and potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems.