Smart manufacturing: Past research, present findings, and future directions

By H. Kang, J. Y. Lee, Sang-Su Choi, H. Kim, Jun Hee Park, J. Son, B. Kim, S. Noh. International Journal of Precision Engineering and Manufacturing - Green Technology, 2016

1,196 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/50ab0482224df90b59f5972f28c07626d89a1fab

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In Industrial and Manufacturing Engineering

Citations in the corpus

Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company (2018, background): “Moreover, due to the novelty of these technologies, several studies focused on individual technologies of I40, and not on the overall cyber-physical system [27].”
The Potential of Additive Manufacturing in the Smart Factory Industrial 4.0: A Review (2019, background): “The outcome of this combination is that a user-specific product can be produced within each machine [5].”
Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review (2020, methodology): “0 environments, the conﬂuence of a variety of AMT technologies together comprises CIM and its evolution toward what is known as CPS (Kang et al. 2016; Monostori et al. 2016).”
Digital twin-driven product design framework (2019, background): “Throughout the above national manufacturing strategies, one common goal is smart manufacturing (Kang et al. 2016).”
Industry 4.0: A Bibliometric Review of Its Managerial Intellectual Structure and Potential Evolution in the Service Industries (2019, background): “In its broadest connotation, the phenomenon has been labelled by several scholars and practitioners as “the fourth industrial revolution” (European Commission, 2016b; Kang et al., 2016; Liao et al., 2017; Schwab, 2016; Skilton and Hovsepian, 2017).”
Evaluation of the Relation between Lean Manufacturing, Industry 4.0, and Sustainability (2019, background): “Additive manufacturing [9] and collaborative robots [11], for instance, are expected to play a crucial role in this direction, but also suitable organizational structures and business models, and along with appropriate production and decision methods and supporting tools are going to be necessary to”
Smart manufacturing: Characteristics, technologies and enabling factors (2019, background): “Various studies have been done to decrease the use of energy in manufacturing systems.(30) If a system can reuse its products, then the amount of energy required will decrease in most cases.”
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, background): “W ITH the requirement of economic considerations and the advancement of technology, many countries, including the United States, Germany, and China, have successively put forward plans to revitalize manufacturing [1].”
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2020, background): “…around the integration of AI at nearly every level to drive predictive maintenance, future business strategies, and floor production, with humans working alongside AI to make vital decisions for the company (Erol et al., 2016; Kang et al., 2016; Lee et al., 2015; Mabkhot et al., 2018; Omron, 2020).”
Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance (2017, background): “An intelligent/smart factory operates using advanced sensors and information technologies; thus, large amounts of data are generated and collected in a smart factory, requiring big data processing technology to build an integrated environment in which the production process can be represented transp”