Current Standards Landscape for Smart Manufacturing Systems

Lu, Yan; Morris, Katherine C.; Frechette, Simon P.

doi:10.6028/nist.ir.8107

Cited by 242 publications

(159 citation statements)

References 10 publications

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“…A mapping of the standards landscape for smart manufacturing made by NIST [25] identified in over 100 standards, and several more have been developed since that study was made (published early 2016).…”

Section: Discussionmentioning

confidence: 99%

Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

Nardello

Møller

Gøtze

2017

J. Complex Syst. Inform. Model. Q.

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Abstract. The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is "Smart production". Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions.

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Section: Discussionmentioning

confidence: 99%

Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

Nardello

Møller

Gøtze

2017

J. Complex Syst. Inform. Model. Q.

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“…6a, based on the ARC Advisory Group's model for collaboration manufacturing management (ARC Advisory Group, 2002) and ISA95's Enterprise-Control System Integration hierarchical model (Barkmeyer, 1996), NIST describes a smart manufacturing ecosystem (Lu et al, 2016). The reference architecture model for Industry 4.0 is shown in Fig.…”

Section: Trends In the Development Of Unmanned Workhops/intelligent mentioning

confidence: 99%

Current trends in the development of intelligent unmanned autonomous systems

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

134

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Intelligent unmanned autonomous systems are some of the most important applications of artificial intelligence (AI). The development of such systems can significantly promote innovation in AI technologies. This paper introduces the trends in the development of intelligent unmanned autonomous systems by summarizing the main achievements in each technological platform. Furthermore, we classify the relevant technologies into seven areas, including AI technologies, unmanned vehicles, unmanned aerial vehicles, service robots, space robots, marine robots, and unmanned workshops/intelligent plants. Current trends and developments in each area are introduced.

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“…Examples of these paradigms include lean, flexible, agile, sustainable, digital, and cloud manufacturing. Smart manufacturing can enable aspects of these paradigms for all manufacturers from small businesses to large enterprises [4],[5]. One critical aspect of these systems is the use of data and information to make informed decisions.…”

Section: Introductionmentioning

confidence: 99%

Enabling Smart Manufacturing Technologies for Decision-Making Support

Helu

Libes

Lubell

et al. 2016

Volume 1B: 36th Computers and Information in Engineering Conference

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Smart manufacturing combines advanced manufacturing capabilities and digital technologies throughout the product lifecycle. These technologies can provide decision-making support to manufacturers through improved monitoring, analysis, modeling, and simulation that generate more and better intelligence about manufacturing systems. However, challenges and barriers have impeded the adoption of smart manufacturing technologies. To begin to address this need, this paper defines requirements for data-driven decision making in manufacturing based on a generalized description of decision making. Using these requirements, we then focus on identifying key barriers that prevent the development and use of data-driven decision making in industry as well as examples of technologies and standards that have the potential to overcome these barriers. The goal of this research is to promote a common understanding among the manufacturing community that can enable standardization efforts and innovation needed to continue adoption and use of smart manufacturing technologies.

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Current Standards Landscape for Smart Manufacturing Systems

Cited by 242 publications

References 10 publications

Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

Organizational Learning Supported by Reference Architecture Models: Industry 4.0 Laboratory Study

Current trends in the development of intelligent unmanned autonomous systems

Enabling Smart Manufacturing Technologies for Decision-Making Support

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