Novel strategies for global manufacturing systems interoperability

Jardim-Goncalves, Ricardo; Grilo, António; Popplewell, Keith

doi:10.1007/s10845-014-0948-x

Cited by 46 publications

(9 citation statements)

References 32 publications

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“…Communication with external entities (outside the implemented framework) may indeed be subject to syntactic and semantic interoperability issues. A number of solutions are found in the literature (Jardim-Goncalves et al 2016). For example, Khilwani and Harding (2016) focus on semantic web concepts and tools that enable computers to automatically process and understand information that both machines and humans can understand.…”

Section: Discussion On Implementation and Validationmentioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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This paper presents a smart supervisory framework for a single process controller, designed for Industry 4.0 shop floors. This digitization of a full supervisory suite for a single process controller enables self-awareness, self-diagnosis, self-prognosis, and self-healing (by definition, these "self" elements are missing from other supervisory frameworks diagnosing numerous controllers in parallel). The proposed framework is aligned with the concept of a Cyber Physical System (CPS), since its implementation generates a rich cyber physical entity of the controlled process. This CPS entity can either be considered as the process digital twin, or can provide a solid basis for generating it. Finally, the framework includes the main characteristics of Industry 4.0, such as advanced use of Artificial Intelligence (AI) and big data analysis. The framework is based on four modules: (1) Control and Awareness module-performing both continuous process control and adjustments, as well as machine learning (ML) and statistical process control (SPC) for identifying abnormalities that require further diagnosis; (2) Process-diagnosis module-performing continual (recurrent) analysis of the process state and trends; (3) Prognosis and Healing module-performing prognosis and automated intervention via parameter changes, re-configurations, and automated maintenance; (4) External Interaction Platform-an interactive module for interfacing with experts, presenting them with the process analysis information and obtaining feedback from them as part of a learning process. Using an implementation showcase to illustrate the methodological framework's applicability, we demonstrate its real-world potential. The proposed framework could serve as a guide for implementing smart process control and maintenance systems in Industry 4.0 shop floors. It could also provide a firm basis for comparison with future suggested frameworks. Future research directions could include pursuing improvements to the proposed process control framework and validating the framework by case studies of its implementation.

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Section: Discussion On Implementation and Validationmentioning

confidence: 99%

A smart process controller framework for Industry 4.0 settings

Cohen

Singer

2021

J Intell Manuf

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“…This integration enables to use experience-gathered knowledge to address a dynamical change of the way to work, leading to more flexible, efficient and smart processes. Today, this is one of the major trends in manufacturing (Jardim-Goncalves et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

A framework for manufacturing execution system deployment in an advanced additive manufacturing process

Bedolla

Laverne

Segonds

et al. 2017

IJPLM

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The deployment of additive manufacturing (AM) processes had a rapid and broad increase in the last years, and the same trend is expected to hold in the near future. A way to better exploit the advantages of such technology is the use of appropriate information tools. However, today there is a lack of software applications devoted to this innovative manufacturing process. To overcome this issue, in the present work the application of manufacturing execution systems (MES), a tool commonly used in traditional manufacturing processes, is extended to AM. Furthermore, a framework for the deployment of shop-floor data, acquired through a monitoring system, in the design phase is presented: hence, MES should cooperate with design for additive manufacturing (DFAM), a set of methods and tools helpful to design a product and its manufacturing process taking into account AM specificities from the early design stages. In order to better understand the advantages of such cooperation, a case study for a proof of concept has been developed: the obtained results are promising, thus an online implementation would be recommended.

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“…The main considerations related to the data component include: data modelling, data interoperability, data storage [13].…”

Section: A Conceptual Framework For Cyber-physical Systemsmentioning

confidence: 99%