Elsevier

Computers in Industry

Volume 81, September 2016, Pages 128-137
Computers in Industry

Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook

https://doi.org/10.1016/j.compind.2016.02.004Get rights and content

Highlights

  • Sensor-packed manufacturing systems will become ubiquitous.

  • Cybersecurity aspects are gaining importance within the manufacturing domain.

  • Manufacturing cyber-physical systems are expected to follow the trend set by other domains that benefited from the Internet of Things, Cloud Computing, and Big Data.

  • Outcomes of the implementation of manufacturing cyber-physical systems could be transformative to the extent that predictive manufacturing systems can become a reality.

Abstract

The recent advances in sensor and communication technologies can provide the foundations for linking the physical manufacturing facility and machine world to the cyber world of Internet applications. The coupled manufacturing cyber-physical system is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization. As a drawback, the resulting manufacturing cyber-physical system will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners. This work provides a review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art cloud manufacturing solutions and Big Data applications, this work also proposes a framework for the development of predictive manufacturing cyber-physical systems that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics.

Introduction

The current manufacturing global operations asks for more and stringent requirements than ever before, such as strict deadlines, low inventories, uncertain demand, standardization of manufacturing processes, product diversity, and security aspects [1]. Enhancing the manufacturing environment for more visibility and better control of the production processes becomes essential. Advances in sensor and communication technologies can provide the foundations for linking the physical facility and machine world to the cyber world of Internet applications and the software world. The coupled Manufacturing Cyber-Physical System (M-CPS) is envisioned to handle the actual operations in the physical world while simultaneously monitor them in the cyber world with the help of advanced data processing and simulation models at both the manufacturing process and system operational levels [2]. Moreover, a sensor-packed manufacturing system in which each process or piece of equipment makes available event and status information, coupled with market research for true advanced Big Data analytics, seem to be the right ingredients for event response selection and operation virtualization, and thus moving manufacturing operations closer to the cloud manufacturing paradigm [3]. As a drawback, the resulting M-CPS will be vulnerable to the inevitable cyber-attacks, unfortunately, so common for the software and Internet-based systems. This reality makes cybersecurity penetration within the manufacturing domain a need that goes uncontested across researchers and practitioners.

The globalization trend, exhibited by the world economy for a while now, comes with significant challenges for the manufacturing industries of both developed and under development countries. The new predictive manufacturing paradigm, which is referred to more and more in recent peer-reviewed publications and proposal solicitations for funding competitions, is transformative in name. But more so, this new paradigm will be transformative in its implementation [4]. While the tools seem to be already available, what is needed is their customization for the manufacturing domain, new integration architectures and control algorithms, and mostly the willingness of the manufacturing actors. Tools such as cyber-physical devices, Big Data, IT infrastructures are now ubiquitous available. Manufacturing domain needs to take a hard look at them and perform the necessary customized integration.

This work provides a comprehensive literature review of the current status of virtualization and cloud-based services for manufacturing systems and of the use of Big Data analytics for planning and control of manufacturing operations. In the enterprise context, cloud solutions usually consider the business layer and address the needed tighter interaction with the customer and the integration with suppliers, competition, and regulatory bodies [2]. Building on already developed cloud business solutions, cloud manufacturing is expected to offer improved enterprise manufacturing and business decision support. Based on the current state-of-the-art of cloud manufacturing solutions and Big Data manufacturing applications, this work also proposes a framework for the development of predictive M-CPS that include capabilities for attaching to the Internet of Things, and capabilities for complex event processing and Big Data algorithmic analytics [3]. The development challenges for the M-CPS as identified in the literature as well as uncovered by outlining and detailing the proposed framework are also discussed.

From this point forward the paper is structured as follows: Section 2 provides a review of the most important aspects of complex event processing, cloud computing and virtualization in manufacturing, Internet of Things, Big Data analytics, and cybersecurity within the manufacturing domain. Then, Section 3 presents modeling framework guidelines for the manufacturing cyber-physical system, detailing certain critical modeling aspects and instantiates the predictive manufacturing systems paradigm. Finally, the future outlook for manufacturing cyber-physical systems is sketched and needed research is outlined.

Section snippets

Manufacturing cyber-physical systems component technologies and processes review

The technologies and processes that make possible the creation of M-CPS are already in use in other domains, some of them also reaching a certain degree of maturity. However, the penetration of these technologies and/or processes into the manufacturing domain is slower compared to other domains. This is due to the nature of manufacturing operations, which need to deal with large pieces of hardware equipment, many of them being legacy systems, the high cost of manufacturing equipment, which

Manufacturing cyber-physical system model

Previous work [2], [3] provided a first glance at the M-CPS model, which includes both the physical world, where the traditional manufacturing system is located, and the cyber world, where the Internet connectivity resides and computing in the cloud is performed. In between the two worlds, there is a layer of cyber-physical devices, such as sensors and actuators, local area networks, and also application and cybersecurity software, that complete the cyber-physical system model depicted in Fig. 1

Manufacturing cyber-physical systems: future outlook

This research outlines the advancements made in recent years in regard to the manufacturing-based customization of technologies and domains such as: IoT, Big Data, virtualization, cloud-based services, and cybersecurity. While there is significant more basic and applied research work needed for manufacturing to catch-up with other the more technology savvy domains, the gap is clearly shrinking, and it is expected that virtual/cloud/cyber manufacturing, or what this research calls M-CPS, will

Dr. Babiceanu is an Associate Professor of Systems Engineering with the Department of Electrical, Computer, Software, and Systems Engineering at Embry-Riddle Aeronautical University. He received his Ph.D. degree in Industrial and Systems Engineering from Virginia Tech in 2005. Dr. Babiceanu’s research provides a systems engineering approach to modeling, operation, and performance improvement of large-scale complex systems, such as manufacturing, supply chain, and transportation systems. His

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    Dr. Babiceanu is an Associate Professor of Systems Engineering with the Department of Electrical, Computer, Software, and Systems Engineering at Embry-Riddle Aeronautical University. He received his Ph.D. degree in Industrial and Systems Engineering from Virginia Tech in 2005. Dr. Babiceanu’s research provides a systems engineering approach to modeling, operation, and performance improvement of large-scale complex systems, such as manufacturing, supply chain, and transportation systems. His work addresses the requirements, architecture, integration, and evaluation of systems, considering their lifecycle effectiveness and sustainability characteristics using methodologies such as systems analysis, engineering optimization, discrete-event and continuous simulation, computational intelligence techniques, and multi-agent systems. Dr. Babiceanu published more than 50 technical publications in reputed journals and conference proceedings.

    Dr. Seker is a Professor of Computer Science with the Department of Electrical, Computer, Software, and Systems Engineering at Embry-Riddle Aeronautical University. He received his Ph.D. degree in Computer Engineering from the University of Alabama at Birmingham in 2002. Dr. Seker’s research interests have a strong foundation in the areas of safety and security critical systems and computer forensics. His research is motivated by the trend in rapid penetration of computer-based technologies into our society. Dr. Seker published more than 60 technical publications and actively serves on ACM and IEEE Computer Society Computing Curriculum Committee. He also served as a Department of Homeland Security Software Assurance Forum Working Group member.

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