Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems

Vogel‐Heuser, Birgit; Wildermann, Stefan; Teich, Jürgen

doi:10.1007/s11740-017-0765-0

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…There has been a small but growing volume of studies [51][52][53][54]58,68,[75][76][77][128][129][130][131][132]152,153] claiming that industrial enterprises advance as wireless sensor networks to constantly control the operations of their plants. Internet of Things-based real-time production logistics, product decision-making information systems, and deep learning-assisted smart process planning facilitate continuous monitoring of smart shop floors.…”

Section: Discussionmentioning

confidence: 99%

“…Deployment of artificial intelligence-based decision-making algorithms, deep learningassisted smart process planning, real-time sensor networks, and cloud technologies [75][76][77] are instrumental in remote maintenance support. Leveraging massive volumes of data gathered on equipment and their processing through product decision-making information systems enable the adoption of groundbreaking techniques for condition-based maintenance.…”

Section: Artificial Intelligence-based Decision-making Algorithms Smart Factory Performance and Industry 40-based Manufacturing Systems Imentioning

confidence: 99%

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Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

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Section: Discussionmentioning

confidence: 99%

Section: Artificial Intelligence-based Decision-making Algorithms Smart Factory Performance and Industry 40-based Manufacturing Systems Imentioning

confidence: 99%

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

et al. 2021

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“…Vogel-Heuser et al [19] introduced a design flow for the evolution of CPPS by combining models from the control-level design and the electronic system-level design for automatic deployment. The methodology includes a design space exploration that performs a multiobjective optimization.…”

Section: Optimized Distribution and Coevolutionmentioning

confidence: 99%

(Re)deployment of Smart Algorithms in Cyber–Physical Production Systems Using DSL4hDNCS

et al. 2021

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Intelligent algorithms and learning are the basis for evolving smart cyber-physical production systems (CPPSs) and Industrie 4.0. A smart image detection algorithm shall be added to reduce downtime due to the extended operation of glass bottles in a yogurt producing plant. To support the engineers in doing so, a comprehensive domain-specific language (DSL), DSL4hDNCS, is introduced, enabling rapid analysis, addressing hardware/software architectures and networkrelated delays and uncertainties. DSL4hDNCS is defined by a metamodel to avoid ambiguity and enriched by aspects, such as safety, calculation power, and network transmission time. DSL4hDNCS is used to compare (re)deployment alternatives using different technologies, such as edge, fog, and cloud to implement the additional smart algorithm. The evaluation of DSL4hDNCS using an acknowledged Industrie 4.0 demonstrator plant as a case study confirmed the benefit for engineers during the redesign.

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“…Used for the first time at the Hanover Fair in 2011, the term "Industry 4.0" refers to the fourth industrial revolution and is often understood to be the application of the generic concept of cyber-physical systems (CPSs) to industrial production systems (cyber-physical production systems) (Drath & Horch, 2014;Perez et al, 2015;Jiang, 2017;Jazdi, 2014). The use of CPSs can provide, for example, a combined approach between the process and the product in automated production systems, as proposed in Vogel-Heuser et al (2017).…”

Section: Industry 40mentioning

confidence: 99%

Evaluation of OPC-UA communication in an autonomous advanced manufacturing cell implementation

et al. 2020

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The recent development of advanced manufacturing concepts brings new challenges for developing countries in order to prepare their workforce and industries for local and global markets. To explore and disseminate advanced manufacturing concepts, the first autonomous advanced manufacturing cell developed in Brazil as a result of a partnership between global and local companies and academia was designed and evaluated. This paper aims to describe the advanced manufacturing cell, the equipment composing the infrastructure, and the network topology and equipment that could deal with the heterogeneity of equipment and protocol. Moreover, the behavior of the Open Platform Communications Unified Architecture (OPC UA) communication framework (and related protocols) as the integration element between the equipment composing the cell was evaluated. The results indicated that the OPC UA communication framework promotes a low traffic overhead and can also be used to carry data from the traditional protocols in their fields without restrictions that could hinder production. The paper also discusses how the exchange of data occurred between the equipment used in the production and the control systems via the protocol studied. The OPC UA communication framework protocols presented a messaging structure and data transport characteristics that satisfy the integration needs between equipment of several manufacturers that composed an autonomous cell of advanced manufacturing. This paper also presents some key challenges to integrating all equipment not addressed by the OPC UA reference model.

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Towards the co-evolution of industrial products and its production systems by combining models from development and hardware/software deployment in cyber-physical systems

Cited by 13 publications

References 26 publications

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

(Re)deployment of Smart Algorithms in Cyber–Physical Production Systems Using DSL4hDNCS

Evaluation of OPC-UA communication in an autonomous advanced manufacturing cell implementation

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