SDCWorks: A Formal Framework for Software Defined Control of Smart Manufacturing Systems

Potok, Matthew; Chen, Chien-Ying; Mitra, Sayan; Mohan, Sibin

doi:10.1109/iccps.2018.00017

Cited by 8 publications

(6 citation statements)

References 23 publications

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“…An example architecture for a manufacturing system that uses the terms defined in this section is shown in Figure 2. Centralized Smart Manufacturing System (CSMS)a Manufacturing System in which one controlling agent, termed "Central Controller" (CC) in this paper, determines the capability authority of the system [26]- [28]. This means that the CC either directly or indirectly dictates or establishes the capability boundary conditions of all the agents in the CSMS (agents may choose to further restrict boundary conditions).…”

Section: System-level Manufacturing Control Terminologymentioning

confidence: 99%

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Kovalenko

Moyne

et al. 2022

IEEE Access

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Manufacturers are constantly looking to enhance the performance of their manufacturing systems by improving their ability to address disruptions and disturbances, while reducing cost and maximizing quantity and quality. Even though innovative mechanisms for adaptability and flexibility continuously contribute to the smart manufacturing evolutionary process, they generally stop short of providing a capability for coordinated on-line learning. This is especially true when that learning requires exploration outside of established operational boundaries or uses artificial intelligence (as opposed to purely human intelligence) as part of the dynamic implementation of learning. In this work, we provide a vision for the development of an automated learning control architecture to extend the adaptability and flexibility capabilities of manufacturing systems. As part of this vision, we describe a set of requirements and objectives that, if addressed, provide an environment to allow distributed and automated learning across the manufacturing ecosystem. We then provide an example communication and control architecture that meets these requirements and objectives by gathering information, building a dynamic knowledge base, distributing intelligence, making decisions, and adapting the control commands sent to the equipment and people across the manufacturing ecosystem. The example architecture leverages both centralized and distributed control strategies and the ability to switch between the strategies to gather and learn from information in the system. Example case studies are provided illustrating how this architecture can be used to improve manufacturing system performance.

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Section: System-level Manufacturing Control Terminologymentioning

confidence: 99%

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Kovalenko

Moyne

et al. 2022

IEEE Access

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“…In industrial automation, however, effective virtual prototyping has proven elusive, although recent efforts have made progress on this front. SDCworks [43], for instance, provides a framework for formalizing and verifying controllers for smart manufacturing systems. Another model-based approach is discussed in [44].…”

Section: E Virtual Prototypingmentioning

confidence: 99%

Programmable Logic Controllers in the Context of Industry 4.0

Sehr

Lohstroh

Weber

et al. 2021

IEEE Trans. Ind. Inf.

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Programmable Logic Controllers (PLCs) are an established platform, widely used throughout industrial automation but poorly understood among researchers. This paper gives an overview of the state of the practice, explaining why this settled technology persists throughout industry and presenting a critical analysis of the strengths and weaknesses of the dominant programming styles for today's PLC-based automation systems. We describe the software execution patterns that are standardized loosely in IEC 61131-3. We identify opportunities for improvements that would enable increasingly complex industrial automation applications while strengthening safety and reliability. Specifically, we propose deterministic, distributed programming models that embrace explicit timing, event-triggered computation, and improved security.

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“…A concept of software-defined control (SDC) has emerged recently [19,27] which is inspired from the traditional software-defined networking (SDN), for programmatically configuring a communication network. A basic idea of SDC is to provide a central controller that has a global view of a system and separate decision making logic from the operations management solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several works [34,33,19] propose the digital twin based software architectures and foresee the importance of big data analysis and its linkage with the digital twin. The works in [4,18,30,27,16] focus more on the modelling aspects. Nevertheless, implementation details of the software architectures and their use case scenarios are somewhat abstracted.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nevertheless, implementation details of the software architectures and their use case scenarios are somewhat abstracted. In addition, most of works lack a comprehensive modelling framework except [27], whose target is yet limited to discrete event systems, and [16], which focuses more on simulation of cyber-physical systems in general rather than digital twin itself. The next section discusses digital twin in the context of CPPS.…”

Section: Literature Reviewmentioning

confidence: 99%

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Challenges in Digital Twin Development for Cyber-Physical Production Systems

Park

Easwaran

Andalam³

2019

Cyber Physical Systems. Model-Based Design

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The recent advancement of information and communication technology makes digitalisation of an entire manufacturing shop-floor possible where physical processes are tightly intertwined with their cyber counterparts. This led to an emergence of a concept of digital twin, which is a realistic virtual copy of a physical object. Digital twin will be the key technology in Cyber-Physical Production Systems (CPPS) and its market is expected to grow significantly in the coming years. Nevertheless, digital twin is still relatively a new concept that people have different perspectives on its requirements, capabilities, and limitations. To better understand an effect of digital twin's operations, mitigate complexity of capturing dynamics of physical phenomena, and improve analysis and predictability, it is important to have a development tool with a strong semantic foundation that can accurately model, simulate, and synthesise the digital twin. This paper reviews current state-of-art on tools and developments of digital twin in manufacturing and discusses potential design challenges.

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SDCWorks: A Formal Framework for Software Defined Control of Smart Manufacturing Systems

Cited by 8 publications

References 23 publications

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Toward an Automated Learning Control Architecture for Cyber-Physical Manufacturing Systems

Programmable Logic Controllers in the Context of Industry 4.0

Challenges in Digital Twin Development for Cyber-Physical Production Systems

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