Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

By Pai Zheng, Honghui Wang, Z. Sang, Ray Y. Zhong, Yongkui Liu, Chao Liu, Khamdi Mubarok, Shiqiang Yu, Xun Xu. Frontiers of Mechanical Engineering, 2018

867 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/8955a195db9b3c3f6487f56c2556fdb34d515fce

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In Industrial and Manufacturing Engineering

Citations in the corpus

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives (2019, methodology): “DT technology is an effective tool to fulfill the requirements of smart manufacturing by reflecting the physical status of systems in a virtual space (Tao et al. 2018c; Zheng et al. 2018d).”
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, background): “Smart manufacturing is a value-creation process from design to production, logistics, and service, in which multiple subjects participate [43].”
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, background): “Therefore, the use of AI in tool condition monitoring (TCM) of machining processes is hot topic of research (Zheng et al., 2018) and the review of existing literature developments in this area is required to show the status of this research topic while identifying the most promising research of…”
A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications (2019, background): “First of all, our study was not focused on searching among papers framed in speciﬁc ﬁelds of applications, whilst many reviews on DTs present deep analyses focusing exclusively on the manufacturing domain [16], [37]–[40].”
From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where (2022, background): “0 advances manufacturing factories more intelligent, adaptable, and productive by implementing and equipping them with smart sensors, devices, and independent, autonomous systems [81].”
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning (2020, background): “Some work [49, 50] discussed the design of intelligent production systems by integrating modern AI technology.”
A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation (2020, background): “Zheng et al. (2018) identified significant applications of SMS in design, machining, monitoring, control and scheduling.”