Implementing Smart Factory of Industrie 4.0: An Outlook

By Shiyong Wang, Jiafu Wan, Di Li, Chunhua Zhang. Int. J. Distributed Sens. Networks, 2016

1,398 citations recorded by the source corpus.

With the application of Internet of Things and services to manufacturing, the fourth stage of industrialization, referred to as Industrie 4.0, is believed to be approaching. For Industrie 4.0 to come true, it is essential to implement the horizontal integration of inter-corporation value network, the end-to-end integration of engineering value chain, and the vertical integration of factory inside. In this paper, we focus on the vertical integration to implement flexible and reconfigurable smart factory. We first propose a brief framework that incorporates industrial wireless networks, cloud, and fixed or mobile terminals with smart artifacts such as machines, products, and conveyors. Then, we elaborate the operational mechanism from the perspective of control engineering, that is, the smart artifacts form a self-organized system which is assisted with the feedback and coordination blocks that are implemented on the cloud and based on the big data analytics. In addition, we outline the main technical features and beneficial outcomes and present a detailed design scheme. We conclude that the smart factory of Industrie 4.0 is achievable by extensively applying the existing enabling technologies while actively coping with the technical challenges.

Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/f5079168f914014e9cfe888bdfd58b7bac2b1fe9

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Citations in the corpus

Predictive Maintenance and Intelligent Sensors in Smart Factory: Review (2021, background): “The production system in smart factory is different—with more resources for small-lot products, dynamic routing of production line, comprehensive connections with high-speed network infrastructure, deep convergence of physical and digital world (digital twins), self-organisation control system, and ”
A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line (2017, methodology): “0 vision [6], [7] and the development of big data analytics [8], [9], faster algorithms, increased computation power, and amount of available data enable the simulation with ability of real-time control and optimization of products and production lines, which is referred to as a Digital Twin [10], u”
Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges (2018, background): “acteristics to meet rapidly-changing market needs [95], [96].”
Driving forces and barriers of Industry 4.0: Do multinational and small and medium-sized companies have equal opportunities? (2019, background): “They and may be addressed by developing and applying new technologies (Wang et al., 2016).”
Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review (2020, background): “0, namely, physical, digital, and biological worlds [16], significantly contribute to the improvement of the industrial environment, generating positive effects on governments’ economies and development plans.”
Smart manufacturing: Past research, present findings, and future directions (2016, neutral): “through a framework that supported vertical and horizontal integration of systems.(7) Anderl is showing the effects of Industry 4.”
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A Cross-Strait Comparison of Innovation Policy under Industry 4.0 and Sustainability Development Transition (2017, background): “Policy or industry development-related studies are scattered and tend to overlook industry vertical integration [5], industry overview and development models [6], summaries of cross-nation Industry 4.”
The industrial management of SMEs in the era of Industry 4.0 (2018, background): “Based on standard protocols, this communication mode allows the autonomous management of industrial organisations (Wang et al. 2016).”
Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination (2016, methodology): “In this article, based on a novel smart factory framework that integrates the autonomous agents with big data based feedback and coordination [24] , we focus on the key algorithms that can operate the smart factory.”
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