IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing

By Fei Tao, Ying Zuo, Lida Xu, Lin Zhang. IEEE Transactions on Industrial Informatics, 2014

553 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Management of Technology and Innovation

View Paper: https://www.semanticscholar.org/paper/65d0126364230c7fbdbda0c2d4507efd9f23d82f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Fei Tao
2. Qinglin Qi
3. Andrew Yeh-Ching Nee
4. Ying Cheng
5. Kai-Jian Bai
6. Bin He
7. Lin Zhang
8. Lida Xu

In Management of Technology and Innovation

1. Fei Tao
2. Ying Cheng
3. Qinglin Qi
4. Lian Zhao
5. Jingran Li
6. Kai-Jian Bai
7. Bin He
8. Lin Zhang

Citations in the corpus

• Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop (2018, background): “Integrating cloud technologies in the CPS cyber layer is also promising to ensure the scalability of storage, computation, and cross domain communication capabilities (Tao et al. 2014).”
• A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, background): “becomes unacceptable for data analysis and control decision making [83], [84].”
• Smart manufacturing: Past research, present findings, and future directions (2016, methodology): “70 Zhang et al. conducted researches on real-time manufacturing information capturing through the sensor-embedded manufacturing resources and the IoMT (Internet of Manufacturing Things) architecture based on RTMIIS (Real-Time Manufacturing Information Integration Service) that supported…”
• A Novel Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin (2019, methodology): “With the development of IoT, many methods and protocols are used for device access [39]–[41].”
• Implementing Smart Factory of Industrie 4.0: An Outlook (2016, background): “Recently, the emerging technologies (e.g., Internet of Things (IoT) [1–3], wireless sensor networks [4, 5], big data [6], cloud computing [7–9], embedded system [10], and mobile Internet [11]) are being introduced into the manufacturing environment, which ushers in a fourth industrial revolution.”
• Advanced manufacturing systems: socialization characteristics and trends (2015, methodology): “Especially, the developing cloud-based and IoT-basedmanufacturing service system (Tao et al. 2014) is making the ubiquitous services more and more possible and useful.”
• Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison (2018, methodology): “The manufacturing data are collected real-time and automatically by IoT [7].”
• Internet of things and supply chain management: a literature review (2019, unknown): “Tao et al. (2014) designed an IoT-based framework to support the cloud manufacturing with manufacturing resource intelligent perception and access.”
• CCIoT-CMfg: Cloud Computing and Internet of Things-Based Cloud Manufacturing Service System (2014, background): “, optical fiber sensors for online and real-time monitoring high-speed rotating equipment with high working temperature; majority works of IoT in manufacturing are emphasized on the data collection of manufacturing equipment and process, but studies on how to realize the intelligent data mining and ”
• Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, background): “0 realm, main open issues regard [104]: (i) the design of decision-making and negotiation mechanisms, in order to enforce inter-thing cooperation; (ii) the design of ‘‘ad hoc’’ broadband communication protocols for the localization and”
• A fast and robust convolutional neural network-based defect detection model in product quality control (2017, methodology): “With the rapid development of computer technology and the expansion of its application fields [5, 6], computer vision has been successfully applied in the quality inspection of various industrial products, including steel slabs, glass products, fabrics, polycrystalline solar wafers, and so on.”
• The internet of things: a survey (2014, background): “…of technologies are involved in IoT, such as wireless sensor networks (WSNs), barcodes, intelligent sensing, RFID, NFC, low energy wireless communications, cloud computing, and so on (Jiang et al. 2014; Kataev et al. 2013; Li et al. 2013a; Ren et al. 2012; Tao et al. 2014a, b; Wang et al. 2014).”
• Digital twin-based sustainable intelligent manufacturing: a review (2020, methodology): “Resource-intelligence and access systems were developed based on the IoTs to realize the manufacturing model of cloud manufacturing [171].”
• Big Data in product lifecycle management (2015, methodology): “…and what advanced algorithms are needed to be improved are both worth to be studied [96, 97], as well as the specific enabling techniques and methods for realizing the better combination of “ Big Data ” with multiple techniques like IoT, cloud capacity, and distributed processing techniques [98].”