Production as a Service: A Digital Manufacturing Framework for Optimizing Utilization

Balta, Efe C.; Lin, Yikai; Barton, Kira; Tilbury, Dawn M.; Mao, Z. Morley

doi:10.1109/tase.2018.2842690

Cited by 42 publications

(23 citation statements)

References 28 publications

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“…In summary, DT roadmaps and vision generally point to an increased proliferation of and reliance on DT technology. The DTs will be more integrated (vertically and horizontally), will eventually need to be dynamically created, configured, and verified and validated, and operate in an integrated environment that extends beyond the "four-walls" to the entire ecosystem [1], [5], [4]. The DTs will take advantage of advancements in analytics, data, and understanding to provide higher quality and more expansive capabilities.…”

Section: Dts Of the Future: Near-term And Longer-termmentioning

confidence: 99%

“…• Extending the DT O-O specification to the entire SM ecosystem. The boundaries of manufacturing will become increasingly blurred with new manufacturing paradigms incorporating optimization throughout the SM ecosystem from raw material production all the way through the end customer experience [4], [5]. DT technology will provide benefits throughout this ecosystem, with many of these arising from the coordination of DT instances (see Section VI for examples).…”

Section: • Addressing Security Of Dt Data and Intellectual Propertymentioning

confidence: 99%

“…The evolution of manufacturing during the new smart manufacturing (SM) or Industrie 4.0 era is really part of a continuum that has existed for many decades punctuated by greater integration, vertically and horizontally, across the manufacturing ecosystem; big data trends in the "5 'V' areas of volume, velocity, veracity (data quality), variety (data merging), and value (including better and better use of analytical techniques); more distributed and coordinated intelligence especially using internet technology; and improvements in capabilities and use of virtual representations of components and systems [1], [2], [3], [4], [5]. The terms "smart manufacturing" and "Industrie (Industry) 4.0" are often used interchangeably.…”

Section: Introductionmentioning

confidence: 99%

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A Requirements Driven Digital Twin Framework: Specification and Opportunities

et al. 2020

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Among the tenets of Smart Manufacturing (SM) or Industry 4.0 (I4.0), digital twin (DT), which represents the capabilities of virtual representations of components and systems, has been cited as the biggest technology trend disrupting engineering and design today. DTs have been in use for years in areas such as model-based process control and predictive maintenance, however moving forward a framework is needed that will support the expected pervasiveness of DT technology in the evolution of SM or I4.0. A set of requirements for a DT framework has been derived from analysis of DT definitions, DTs in use today, expected DT applications in the near future, and longer-term DT trends and the DT vision in SM. These requirements include elements of re-usability, interoperability, interchangeability, maintainability, extensibility, and autonomy across the entire DT lifecycle. A baseline framework for DT technology has been developed that addresses many aspects of these requirements and enables the addressing of the requirements more fully through additional specification. The baseline framework includes a definition of a DT and an object-oriented (O-O) architecture for DTs that defines generalization, aggregation and instantiation of DT classes. Case studies using and extending the baseline framework illustrate its advantages in supporting DT solutions and trends in SM.

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Section: Dts Of the Future: Near-term And Longer-termmentioning

confidence: 99%

Section: • Addressing Security Of Dt Data and Intellectual Propertymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Requirements Driven Digital Twin Framework: Specification and Opportunities

et al. 2020

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“…More specific applications can be found, e.g., in the semiconductor domain [31], [37]. To optimize resource utilization via the productionas-a-service paradigm, Balta et al [38] specify a minimalist ontology to compare a customer order to manufacturer capabilities. They do not explicitly model resources because the approach is based on a description of available services.…”

Section: B Knowledge Bases In the Context Of Agent-based Systemsmentioning

confidence: 99%

A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies

Ocker

Kovalenko

Barton

et al. 2019

IEEE Robot. Autom. Lett.

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Mass customization and global competition require Cyber-Physical Systems of Systems (CPSoS) to become increasingly flexible. Modern CPSoS have to be able to create a wide and versatile variety of products, which takes centralized approaches to their limits. In addition, they have to produce these products as quickly as possible. Hence, they must be able to react promptly if problems arise, such as the failure of a single machine. Modern agent-based production systems provide the flexibility required to cope with these challenges. While resource agents (RAs) represent the available resources, i.e., machines, such as robots, individual customer orders can be represented by so-called product agents (PAs). However, a challenge in the design of agent-based production systems is still the amount of communication and computation that is necessary online. The PAs have to communicate their requests and the RAs their capabilities and capacities. On this basis, PAs must compute the appropriate production sequence. We propose to automatically initialize every agent with a knowledge base (KB) created a priori using semantic web technologies (SWT). On the one hand, the KBs of RAs describe the RAs' capabilities in terms of product features and production processes. Every KB of a PA, on the other hand, expresses all possible production sequences based on the customer specification and the CPSoS in question. This allows consistency checks regarding the specification as well as more purposeful communication that focuses on aspects that actually need to be determined at runtime, such as the resources' current capacities or failures. The framework presented aims to reduce both the communication and computational load necessary at runtime for agent-based CPSoS.

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“…In recent years, the endless competition in the global market drives manufacturing firms towards “digitalization” (Balta et al 2018 ). Collaboration is a strategic and practical way to gain a competitive advantage for the digital firms where the core enabler of the collaboration is the “trust” (Helo et al 2019 ).…”

Section: Introductionmentioning

confidence: 99%

A blockchain technology based trust system for cloud manufacturing

Barenji

2021

J Intell Manuf

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Cloud manufacturing (CM) is a new networked manufacturing model that delivers various on-demand manufacturing capabilities to the consumers from the providers. In this model, the provider and consumer never meet each other, thus "trust" is the major enabler for starting a collaboration. In another word, a user must be sure that the requested capability will not be provided with malicious results, and the provider should ensure that the payment will be made on time. In this paper, a novel Blockchain Technology (BCT)-based trust system called "Blocktrust" is proposed to address the trust problem of the CM. First, the CM framework that contains the digital firm, capability pool, and digital certificate issuing units is developed, and then, the private blocktrust peer-to-peer network is proposed and implemented based on Hyperledger fabric. Finlay, the feasibility of the blocktrust is examined under different testing scenarios. The reason for using a private network instead of the public is placing restrictions on who is allowed to participate in the network and also enjoying a network with fast transaction speed. Experiments show that the proposed blocktrust embedded CM is credible and practical.

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Production as a Service: A Digital Manufacturing Framework for Optimizing Utilization

Cited by 42 publications

References 28 publications

A Requirements Driven Digital Twin Framework: Specification and Opportunities

A Requirements Driven Digital Twin Framework: Specification and Opportunities

A Framework for Automatic Initialization of Multi-Agent Production Systems Using Semantic Web Technologies

A blockchain technology based trust system for cloud manufacturing

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