A web-based simulator for a discrete manufacturing system

Mohammed, Wael M.; Lobov, Andrei; Ferrer, Borja Ramis; Iarovyi, Sergii; Lastra, José L. Martínez

doi:10.1109/iecon.2016.7793563

Cited by 14 publications

(9 citation statements)

References 12 publications

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“…This is achieved within servers which provide web services and clients which can request such services. In the context of web services, several standards and protocols have been constructed in order to manage and unify the exchange of information [21]. This may be implemented within a service-oriented architecture (SOA).…”

Section: Web Services and Hypermediamentioning

confidence: 99%

“…Furthermore, to avoid the potential risks, mechanical and electrical problems related to the assembly line and, in turn, in order to reduce the setup time and running costs, a simulation of the real production line was developed. This was implemented as a web-based interface, named as the FASTory Simulator, that mimics the FASTory line operation [21].…”

Section: Fastory Simulator As Use Casementioning

confidence: 99%

“…This research work approach is demonstrated within a specific implementation for monitoring the performance of the FASTory line. One of the principal reasons of selecting the FASTory Simulator as the Manufacturing Plant component, shown in Figure 2, is its capability of mimicking the behavior of the assembly line [21]. The system KB, hosted by the KBSS, is implemented as an OWL ontology.…”

Section: Components Implementation Interaction and Kpi Formulas Calcmentioning

confidence: 99%

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Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

et al. 2018

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The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization.

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Section: Web Services and Hypermediamentioning

confidence: 99%

Section: Fastory Simulator As Use Casementioning

confidence: 99%

Section: Components Implementation Interaction and Kpi Formulas Calcmentioning

confidence: 99%

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Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

et al. 2018

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“…Figure 3. Industrial controllers, devices and connections for monitoring and controlling building automation and production systems at TUT facilities A detailed description of the FASTory Production Line is shown in [56]. In terms of production process, the FASTory Production Line produces drawings of different mobile phones.…”

Section: A Use Case Definition For Implementing An Industrial Cpsosmentioning

confidence: 99%

Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments

Ferrer

Mohammed

Lastra

et al. 2018

2018 IEEE 16th International Conference on Industrial Informatics (INDIN)

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Cyber-physical Systems (CPS) in industrial manufacturing facilities demand a continuous interaction with different and a large amount of distributed and networked computing nodes, devices and human operators. These systems are critical to ensure the quality of production and the safety of persons working at the shop floor level. Furthermore, this situation is similar in other domains, such as logistics that, in turn, are connected and affect the overall production efficiency. In this context, this article presents some key steps for integrating three pillars of CPS (production line, logistics and facilities) into the current smart manufacturing environments in order to adopt an industrial Cyber-Physical Systems of Systems (CPSoS) paradigm. The approach is focused on the integration in several digital functionalities in a cloud-based platform to allow a real time multiple devices interaction, data analytics/sharing and machine learning-based global reconfiguration to increase the management and optimization capabilities for increasing the quality of facility services, safety and energy efficiency and industrial productivity. Conceptually, isolated systems may enhance their capabilities by accessing to information of other systems. The approach introduces particular vision, main components, potential and challenges of the envisioned CPSoS. In addition, the description of one scenario for realizing the CPSoS vision is presented. The results herein presented will pave the way for the adoption of CPSoS that can be used as a pilot for further research on this emerging topic.

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“…The FASTory line contains 12 workstations equipped with IoT devices following the eScop project approach which described in (Ondřej Severa and Roman Pišl, 2015). These workstations contain robot resource and conveyor resource that are represented by IoT devices as discussed in (Mohammed et al 2016). In total, 24…”

Section: 4kpi Case Studymentioning

confidence: 99%

Generic platform for manufacturing execution system functions in knowledge-driven manufacturing systems

Mohammed

Ferrer

Iarovyi

et al. 2017

International Journal of Computer Integrated Manufacturing

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Information technologies grow rapidly nowadays with the advance and extension of computing capabilities. This growth affects several fields, which consume these technologies. Industrial Automation is not an exception. This publication describes a general and flexible architecture for implementing Manufacturing Execution System (MES) function, which can be deployed in multiple industrial case. These features are achieved by combining the flexibility of knowledge-driven systems with the vendor-independent property of RESTful web services. With deployment of this solution, MES functions may gain more versatility and independency. This research work is a continuation of the development of the OKD-MES (Open Knowledge-Driven Manufacturing Execution System) framework during the execution of the eScop project. The OKD-MES framework consists on a semanticbased solution for controlling and enhancing the flexibility and re-configurability of MES. In such scope, this research presents MES functions architecture that might be implemented in the OKD-MES framework in order to increase the flexibility of event-driven manufacturing systems.

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A web-based simulator for a discrete manufacturing system

Cited by 14 publications

References 12 publications

Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

Implementing and Visualizing ISO 22400 Key Performance Indicators for Monitoring Discrete Manufacturing Systems

Towards the Adoption of Cyber-Physical Systems of Systems Paradigm in Smart Manufacturing Environments

Generic platform for manufacturing execution system functions in knowledge-driven manufacturing systems

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