Статті в журналах: "Smart Manufacturing Systems"

1

ElMaraghy, Hoda. "Smart changeable manufacturing systems." Procedia Manufacturing 28 (2019): 3–9. http://dx.doi.org/10.1016/j.promfg.2018.12.002.

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Tuptuk, Nilufer, and Stephen Hailes. "Security of smart manufacturing systems." Journal of Manufacturing Systems 47 (April 2018): 93–106. http://dx.doi.org/10.1016/j.jmsy.2018.04.007.

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Edgar, Thomas F., and Efstratios N. Pistikopoulos. "Smart manufacturing and energy systems." Computers & Chemical Engineering 114 (June 2018): 130–44. http://dx.doi.org/10.1016/j.compchemeng.2017.10.027.

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Etz, Dieter, Hannes Brantner, and Wolfgang Kastner. "Smart Manufacturing Retrofit for Brownfield Systems." Procedia Manufacturing 42 (2020): 327–32. http://dx.doi.org/10.1016/j.promfg.2020.02.085.

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Robert, Michel, Jean Michel Rivière, Jean Luc Noizette, and Frédéric Hermann. "Smart sensors in flexible manufacturing systems." Sensors and Actuators A: Physical 37-38 (June 1993): 239–46. http://dx.doi.org/10.1016/0924-4247(93)80041-e.

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Shahbazi, Zeinab, and Yung-Cheol Byun. "Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework." Processes 9, no. 1 (January 4, 2021): 92. http://dx.doi.org/10.3390/pr9010092.

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Анотація:

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.

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7

Bi, Zhuming, Wen-Jun Zhang, Chong Wu, Chaomin Luo, and Lida Xu. "Generic Design Methodology for Smart Manufacturing Systems from a Practical Perspective. Part II—Systematic Designs of Smart Manufacturing Systems." Machines 9, no. 10 (September 23, 2021): 208. http://dx.doi.org/10.3390/machines9100208.

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In a traditional system paradigm, an enterprise reference model provides the guide for practitioners to select manufacturing elements, configure elements into a manufacturing system, and model system options for evaluation and comparison of system solutions against given performance metrics. However, a smart manufacturing system aims to reconfigure different systems in achieving high-level smartness in its system lifecycle; moreover, each smart system is customized in terms of the constraints of manufacturing resources and the prioritized performance metrics to achieve system smartness. Few works were found on the development of systematic methodologies for the design of smart manufacturing systems. The novel contributions of the presented work are at two aspects: (1) unified definitions of digital functional elements and manufacturing systems have been proposed; they are generalized to have all digitized characteristics and they are customizable to any manufacturing system with specified manufacturing resources and goals of smartness and (2) a systematic design methodology has been proposed; it can serve as the guide for designs of smart manufacturing systems in specified applications. The presented work consists of two separated parts. In the first part of paper, a simplified definition of smart manufacturing (SM) is proposed to unify the diversified expectations and a newly developed concept digital triad (DT-II) is adopted to define a generic reference model to represent essential features of smart manufacturing systems. In the second part of the paper, the axiomatic design theory (ADT) is adopted and expanded as the generic design methodology for design, analysis, and assessment of smart manufacturing systems. Three case studies are reviewed to illustrate the applications of the proposed methodology, and the future research directions towards smart manufacturing are discussed as a summary in the second part.

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8

Lenz, Juergen, Dominik Lucke, and Thorsten Wuest. "Description Model of Smart Connected Devices in Smart Manufacturing Systems." Procedia Computer Science 217 (2023): 1086–94. http://dx.doi.org/10.1016/j.procs.2022.12.307.

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Adiga N, Achal, Avaneesh B. Ballal, Dileep P, Harishgowda M, Roopa T S, and Gangadhar Angadi. "Smart Automated Guided Vehicle for Flexible Manufacturing Systems." ECS Transactions 107, no. 1 (April 24, 2022): 13205–20. http://dx.doi.org/10.1149/10701.13205ecst.

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In the Flexible Manufacturing System, automation and the ability to restructure the manufacturing facility is important. The development of a discretely working Smart Automated Guided Vehicle is the need of the hour. Hence the objective is to develop a compact unit load Smart Automated Guided Vehicle to increase efficiency and productivity & to overcome the problems of conventional material handling systems and improve the efficacy of manufacturing. Smart Automated Guided Vehicle is provided with navigation, weight sensing, obstacle detection systems with other auxiliary systems instrumental in zonal setup for the Smart Automated Guided Vehicle as well as adaptable for frequent changes. This model of Smart Automated Guided Vehicle is helpful for a small operational manufacturing unit for multipurpose applications at very low cost and high customizability. The objective is to provide a safe environment to the Smart Automated Guided Vehicle & its surroundings also, to reduce human dependency.

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Horváth, Imre, Yong Zeng, Ying Liu, and Joshua Summers. "Smart designing of smart systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 2 (May 2021): 129–31. http://dx.doi.org/10.1017/s0890060421000093.

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Krauß, Markus, Florian Leutert, Markus R. Scholz, Michael Fritscher, Robin Heß, Christian Lilge, and Klaus Schilling. "Digital Manufacturing for Smart Small Satellites Systems." Procedia Computer Science 180 (2021): 150–61. http://dx.doi.org/10.1016/j.procs.2021.01.138.

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Garbie, Ibrahim H., and Abdelrahman I. Garbie. "Toward Smart Manufacturing Systems incorporating Reconfiguration Issues." International Journal of Industrial and Systems Engineering 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijise.2021.10041796.

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13

Vogel-Heuser, Birgit, Feng Ju, Cesare Fantuzzi, Yan Lu, and Dieter Hess. "Knowledge-Based Automation for Smart Manufacturing Systems." IEEE Transactions on Automation Science and Engineering 18, no. 1 (January 2021): 2–4. http://dx.doi.org/10.1109/tase.2020.3044620.

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May, Gökan, and Dimitris Kiritsis. "Special Issue on Smart Sustainable Manufacturing Systems." Applied Sciences 9, no. 11 (May 31, 2019): 2264. http://dx.doi.org/10.3390/app9112264.

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Barari, Ahmad, and Marcos Sales Guerra Tsuzuki. "Smart Manufacturing and Industry 4.0." Applied Sciences 13, no. 3 (January 25, 2023): 1545. http://dx.doi.org/10.3390/app13031545.

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16

Lu, Yan, and Feng Ju. "Smart Manufacturing Systems based on Cyber-physical Manufacturing Services (CPMS)." IFAC-PapersOnLine 50, no. 1 (July 2017): 15883–89. http://dx.doi.org/10.1016/j.ifacol.2017.08.2349.

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17

Lu, Jinzhi, Xiaochen Zheng, and Dimitris Kiritsis. "Special Issue: Smart Resilient Manufacturing." Applied Sciences 13, no. 1 (December 29, 2022): 464. http://dx.doi.org/10.3390/app13010464.

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Анотація:

During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc [...]

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18

Karadgi, Sachin. "A Framework Towards Realization of Smart Manufacturing Systems." IOP Conference Series: Materials Science and Engineering 1258, no. 1 (October 1, 2022): 012018. http://dx.doi.org/10.1088/1757-899x/1258/1/012018.

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Germany’s National Academy of Science and Engineering (acatech) published its proposals for implementing the key initiative Industry 4.0 in 2013, requiring horizontal integration and vertical integration within and across multiple enterprises and end-to-end digital integration across the product lifecycle. Likewise, a smart manufacturing system emphasizes enhancing the capabilities of manufacturing enterprises considering multiple objectives, like resource utilization and productivity, necessitating the realization of the business cycle for supply chain management, product development lifecycle, and production system lifecycle. However, realizing these individual lifecycles and integrating them as part of a smart manufacturing system is not straightforward due to manifold reasons (e.g., difficult to define the interface points necessary to interact with the various systems associated with these lifecycles). The current article elaborates a systematic framework considering these lifecycles to realize a smart manufacturing system. The framework is divided into different layers starting from the process layer at the bottom all the way up to the smart layer at the top. Finally, a use case from the end-to-end additive manufacturing process has been discussed that employs the previously elaborated framework.

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19

Jones, Albert T., David Romero, and Thorsten Wuest. "Modeling agents as joint cognitive systems in smart manufacturing systems." Manufacturing Letters 17 (August 2018): 6–8. http://dx.doi.org/10.1016/j.mfglet.2018.06.002.

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20

Zeid, Abe, Sarvesh Sundaram, Mohsen Moghaddam, Sagar Kamarthi, and Tucker Marion. "Interoperability in Smart Manufacturing: Research Challenges." Machines 7, no. 2 (April 2, 2019): 21. http://dx.doi.org/10.3390/machines7020021.

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Анотація:

Recent advances in manufacturing technology, such as cyber–physical systems, industrial Internet, AI (Artificial Intelligence), and machine learning have driven the evolution of manufacturing architectures into integrated networks of automation devices, services, and enterprises. One of the resulting challenges of this evolution is the increased need for interoperability at different levels of the manufacturing ecosystem. The scope ranges from shop–floor software, devices, and control systems to Internet-based cloud-platforms, providing various services on-demand. Successful implementation of interoperability in smart manufacturing would, thus, result in effective communication and error-prone data-exchange between machines, sensors, actuators, users, systems, and platforms. A significant challenge to this is the architecture and the platforms that are used by machines and software packages. A better understanding of the subject can be achieved by studying industry-specific communication protocols and their respective logical semantics. A review of research conducted in this area is provided in this article to gain perspective on the various dimensions and types of interoperability. This article provides a multi-faceted approach to the research area of interoperability by reviewing key concepts and existing research efforts in the domain, as well as by discussing challenges and solutions.

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21

Wang, Wenshan, Xiaoxiao Zhu, Liyu Wang, Qiang Qiu, and Qixin Cao. "Ubiquitous Robotic Technology for Smart Manufacturing System." Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/6018686.

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Анотація:

As the manufacturing tasks become more individualized and more flexible, the machines in smart factory are required to do variable tasks collaboratively without reprogramming. This paper for the first time discusses the similarity between smart manufacturing systems and the ubiquitous robotic systems and makes an effort on deploying ubiquitous robotic technology to the smart factory. Specifically, a component based framework is proposed in order to enable the communication and cooperation of the heterogeneous robotic devices. Further, compared to the service robotic domain, the smart manufacturing systems are often in larger size. So a hierarchical planning method was implemented to improve the planning efficiency. A test bed of smart factory is developed. It demonstrates that the proposed framework is suitable for industrial domain, and the hierarchical planning method is able to solve large problems intractable with flat methods.

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22

Wong, Kok-Seng, and Myung Ho Kim. "Privacy Protection for Data-Driven Smart Manufacturing Systems." International Journal of Web Services Research 14, no. 3 (July 2017): 17–32. http://dx.doi.org/10.4018/ijwsr.2017070102.

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The Industrial Internet of Things (IIoT) is a new industrial ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. The integration of industry and IoT creates various attack surfaces and new opportunities for data breaches. In the IIoT context, it will often be the case that data is considered sensitive. This is because data will encapsulate various aspects of industrial operation, including highly sensitive information about products, business strategies, and companies. The transition to more open network architectures and data sharing of IoT poses challenges in manufacturing and industrial markets. The loss of sensitive information can lead to significant business loss and cause reputational damage. In this paper, the authors discuss emerging issues that are related to IIoT data sharing, investigate possible technological solutions to hide sensitive information and discuss some privacy management techniques in smart manufacturing systems.

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23

Denno, Peter, Charles Dickerson, and Jennifer Anne Harding. "Dynamic production system identification for smart manufacturing systems." Journal of Manufacturing Systems 48 (July 2018): 192–203. http://dx.doi.org/10.1016/j.jmsy.2018.04.006.

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24

Mahmoud, Moamin A., and Jennifer Grace. "A Generic Evaluation Framework of Smart Manufacturing Systems." Procedia Computer Science 161 (2019): 1292–99. http://dx.doi.org/10.1016/j.procs.2019.11.244.

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25

Zheng, Meimei, and Kan Wu. "Smart spare parts management systems in semiconductor manufacturing." Industrial Management & Data Systems 117, no. 4 (May 8, 2017): 754–63. http://dx.doi.org/10.1108/imds-06-2016-0242.

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Purpose The purpose of this paper is to propose a smart spare parts inventory management system for a semiconductor manufacturing company. Design/methodology/approach With the development of the Internet of Things and big data analytics, more information can be obtained and shared between fabs and suppliers. Findings On the basis of the characteristics of spare parts, the authors classify the spare parts into two types, the consumable and contingent parts, and manage them through a cyber-physical inventory management system. Originality/value In this new business model, the real time information from machines, shop floors, spare parts database and suppliers are used to make better decisions and establish transparency and flexibility between fabs and suppliers.

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26

Sim, Hyun Sik. "Big Data Analysis Methodology for Smart Manufacturing Systems." International Journal of Precision Engineering and Manufacturing 20, no. 6 (May 7, 2019): 973–82. http://dx.doi.org/10.1007/s12541-019-00136-7.

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27

Lenz, Juergen, Valerio Pelosi, Marco Taisch, Eric MacDonald, and Thorsten Wuest. "Data-driven Context Awareness of Smart Products in Discrete Smart Manufacturing Systems." Procedia Manufacturing 52 (2020): 38–43. http://dx.doi.org/10.1016/j.promfg.2020.11.008.

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28

Qu, Yuanju, Xinguo Ming, Yanrong Ni, Xiuzhen Li, Zhiwen Liu, Xianyu Zhang, and Liuyue Xie. "An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 11 (December 20, 2018): 2210–24. http://dx.doi.org/10.1177/0954405418816846.

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Enterprise information systems play a significant role in the Industry 4.0 era and are the crucial component to realize smart manufacturing systems. However, traditional enterprise information systems have some limits: (1) lack of complete information, (2) only satisfy limited business needs, and (3) lack of seamless integration, business intelligence, value-driven processes, and dynamic optimization. Clearly, the existing enterprise information systems are unable to satisfy the requirements for smart manufacturing systems: (1) autonomous operation, (2) sustainable values, and (3) self-optimization. In addition, smart manufacturing systems have become more efficient and effective, demanding for seamless information flow in enterprise information systems, knowledge, and data-driven accurately decision. Therefore, a new enterprise information systems framework is needed to bridge gaps between the requirements for traditional manufacturing system and smart manufacturing system. In this article, the integrative framework is proposed based on the business process reengineering, lean thinking, and intelligent management methods, with inclusion of six enterprise information systems aspects to provide upgrading guidelines from traditional manufacturing to smart manufacturing. The procedure of this method contains three steps: (1) it identifies requirements and acquires best practices using AS-IS model, (2) it redesigns six aspects of enterprise information systems using TO-BE model, and (3) it proposes a new enterprise information systems framework. Finally, the proposed framework is validated by real cases.

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Tao, Fei, Qinglin Qi, Ang Liu, and Andrew Kusiak. "Data-driven smart manufacturing." Journal of Manufacturing Systems 48 (July 2018): 157–69. http://dx.doi.org/10.1016/j.jmsy.2018.01.006.

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30

Horváth, Imre. "Connectors of smart design and smart systems." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 2 (April 19, 2021): 132–50. http://dx.doi.org/10.1017/s0890060421000068.

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AbstractThough they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

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Hibino, Hironori, and Masaru Nakano. "Mini Special Issue on Smart Manufacturing." International Journal of Automation Technology 11, no. 1 (January 5, 2017): 3. http://dx.doi.org/10.20965/ijat.2017.p0003.

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Анотація:

Industry 4.0, a new industry initiative in Germany, is impacting strongly both on industry and on society. Many newspapers and technical magazines are publishing the state of the art articles on topics such as smart manufacturing based on IoT (Internet of Things), CPPS (Cyber Physical Production System), and cloud-based systems. Other parts of the world have started initiatives such as the IIC (Industrial Internet Consortium) in the US and the IVI (Industrial Value Chain Initiative) in Japan. Smart manufacturing is the key concept underlying these new initiatives. This special issue addresses the most advanced research on smart manufacturing. Subjects cover cyber-physical product-service systems, machinery production lines, manufacturing system simulation, lot-size energy-consumption dependence per production throughput unit, additive manufacturing processes, sensor network technology, production management technology, supply chain management technology, and smart manufacturing reviews. We thank the authors for their careful work and the reviewers for their incisive efforts without which this special issue would not have been possible. We hope that this special issue will trigger further research on smart manufacturing and its advances.

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32

Tran, Park, Nguyen, and Hoang. "Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context." Applied Sciences 9, no. 16 (August 13, 2019): 3325. http://dx.doi.org/10.3390/app9163325.

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The complexity and dynamic of the manufacturing environment are growing due to the changes of manufacturing demand from mass production to mass customization that require variable product types, small lot sizes, and a short lead-time to market. Currently, the automatic manufacturing systems are suitable for mass production. To cope with the changes of the manufacturing environment, the paper proposes the model and technologies for developing a smart cyber-physical manufacturing system (Smart-CPMS). The transformation of the actual manufacturing systems to the Smart-CPMS is considered as the next generation of manufacturing development in Industry 4.0. The Smart-CPMS has advanced characteristics inspired from biology such as self-organization, self-diagnosis, and self-healing. These characteristics ensure that the Smart-CPMS is able to adapt with continuously changing manufacturing requirements. The model of Smart-CPMS is inherited from the organization of living systems in biology and nature. Consequently, in the Smart-CPMS, each resource on the shop floor such as machines, robots, transporters, and so on, is an autonomous entity, namely a cyber-physical system (CPS) which is equipped with cognitive capabilities such as perception, reasoning, learning, and cooperation. The Smart-CPMS adapts to the changes of manufacturing environment by the interaction among CPSs without external intervention. The CPS implementation uses the cognitive agent technology. Internet of things (IoT) with wireless networks, radio frequency identification (RFID), and sensor networks are used as information and communication technology (ICT) infrastructure for carrying out the Smart-CPMS.

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Wang, Kung-Jeng, Shu-Hua Yang, and Sheng-Hsuan Chen. "Smart control of air conditioning systems in manufacturing systems facing uncertainty." Procedia CIRP 107 (2022): 770–75. http://dx.doi.org/10.1016/j.procir.2022.05.060.

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Li, Lianhui, Bingbing Lei, and Chunlei Mao. "Digital twin in smart manufacturing." Journal of Industrial Information Integration 26 (March 2022): 100289. http://dx.doi.org/10.1016/j.jii.2021.100289.

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Zenkert, Johannes, Christian Weber, Mareike Dornhöfer, Hasan Abu-Rasheed, and Madjid Fathi. "Knowledge Integration in Smart Factories." Encyclopedia 1, no. 3 (August 16, 2021): 792–811. http://dx.doi.org/10.3390/encyclopedia1030061.

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Анотація:

Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

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Mittal, Sameer, Muztoba Ahmad Khan, David Romero, and Thorsten Wuest. "Smart manufacturing: Characteristics, technologies and enabling factors." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 233, no. 5 (October 26, 2017): 1342–61. http://dx.doi.org/10.1177/0954405417736547.

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The purpose of this article is to collect and structure the various characteristics, technologies and enabling factors available in the current body of knowledge that are associated with smart manufacturing. Eventually, it is expected that this selection of characteristics, technologies and enabling factors will help compare and distinguish other initiatives such as Industry 4.0, cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing, which are frequently used synonymously with smart manufacturing. The result of this article is a comprehensive list of such characteristics, technologies and enabling factors that are regularly associated with smart manufacturing. This article also considers principles of “semantic similarity” to establish the basis for a future smart manufacturing ontology, since it was found that many of the listed items show varying overlaps; therefore, certain characteristics and technologies are merged and/or clustered. This results in a set of five defining characteristics, 11 technologies and three enabling factors that are considered relevant for the smart manufacturing scope. This article then evaluates the derived structure by matching the characteristics and technology clusters of smart manufacturing with the design principles of Industry 4.0 and cyber-physical systems. The authors aim to provide a solid basis to start a broad and interdisciplinary discussion within the research and industrial community about the defining characteristics, technologies and enabling factors of smart manufacturing.

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Qian, Feng. "Smart Process Manufacturing Systems: Deep Integration of Artificial Intelligence and Process Manufacturing." Engineering 5, no. 6 (December 2019): 981. http://dx.doi.org/10.1016/j.eng.2019.10.002.

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38

Andronie, Mihai, George Lăzăroiu, Mariana Iatagan, Cristian Uță, Roxana Ștefănescu, and Mădălina Cocoșatu. "Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems." Electronics 10, no. 20 (October 14, 2021): 2497. http://dx.doi.org/10.3390/electronics10202497.

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Анотація:

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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Bayart, M. "Smart devices for manufacturing equipment." Robotica 21, no. 3 (May 13, 2003): 325–33. http://dx.doi.org/10.1017/s0263574702004836.

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Анотація:

Smart devices used in continuous system, benefit from the addition of microelectronics and software that runs inside the device to perform control and diagnostic functions. Very small components, such as inputs/outputs blocks and overload relays, are too small to integrate data processing for technical-economic reason. However, it's possible to develop embedded intelligence and control for the smallest factory floor devices. In the paper, a generic model of smart equipment with reconfiguration functions is proposed. The interest of this functional model is that it can be used for smart devices but it can also be developed in modules for the nearest possible of the inputs and outputs in manufacturing equipment. This solution is economic for a great number of applications because it allows one to realise modular design and to standardise part of system in order to re-use it.

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Morella, P., M. P. Lambán, J. A. Royo, J. C. Sánchez, and O. Muñoz. "Cyber Physical Systems implementation to develop a Smart Manufacturing." IOP Conference Series: Materials Science and Engineering 1193, no. 1 (October 1, 2021): 012114. http://dx.doi.org/10.1088/1757-899x/1193/1/012114.

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Abstract This paper aims to show how the Cyber Physical Systems (CPS) are able to transform the actual manufacturing process. For that purpose, a case of study has been developed as an example of how to implement a CPS in a machine tool, specifically a 5-axis vertical milling machine of the Haas brand. This CPS transforms the acquisition of real-time data into worthy information for the industry. CPS implementation consists of 5 levels, which are explained and exemplified in this study. As a result of the implementation, it is shown a real-time indicator which takes part of our research. Our study concludes that CPS implementation enhance and speed up the decision-making of the companies.

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41

Alavian, Pooya, Yongsoon Eun, Semyon M. Meerkov, and Liang Zhang. "Smart production systems: automating decision-making in manufacturing environment." International Journal of Production Research 58, no. 3 (April 16, 2019): 828–45. http://dx.doi.org/10.1080/00207543.2019.1600765.

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42

Sprock, Timothy. "Self-similar architectures for smart manufacturing and logistics systems." Manufacturing Letters 15 (January 2018): 101–3. http://dx.doi.org/10.1016/j.mfglet.2018.02.002.

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43

Ndip-Agbor, Ebot, Jian Cao, and Kornel Ehmann. "Towards smart manufacturing process selection in Cyber-Physical Systems." Manufacturing Letters 17 (August 2018): 1–5. http://dx.doi.org/10.1016/j.mfglet.2018.03.002.

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44

Bank, Hasan Sinan, Sandeep D'souza, and Aditya Rasam. "Temporal Logic (TL)-Based Autonomy for Smart Manufacturing Systems." Procedia Manufacturing 26 (2018): 1221–29. http://dx.doi.org/10.1016/j.promfg.2018.07.159.

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45

Lin, Yu-Ju, Shih-Hsuan Wei, and Chin-Yin Huang. "Intelligent Manufacturing Control Systems: The Core of Smart Factory." Procedia Manufacturing 39 (2019): 389–97. http://dx.doi.org/10.1016/j.promfg.2020.01.382.

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46

Yao, Xifan, Jiajun Zhou, Yingzi Lin, Yun Li, Hongnian Yu, and Ying Liu. "Smart manufacturing based on cyber-physical systems and beyond." Journal of Intelligent Manufacturing 30, no. 8 (December 28, 2017): 2805–17. http://dx.doi.org/10.1007/s10845-017-1384-5.

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47

Lee, Y. T., S. Kumaraguru, S. Jain, S. Robinson, M. Helu, Q. Y. Hatim, S. Rachuri, D. Dornfeld, C. J. Saldana, and S. Kumara. "A Classification Scheme for Smart Manufacturing Systems’ Performance Metrics." Smart and Sustainable Manufacturing Systems 1, no. 1 (February 28, 2017): 20160012. http://dx.doi.org/10.1520/ssms20160012.

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48

Thomas, Andrew, Wyn Morris, Claire Haven-Tang, Mark Francis, and Paul Byard. "Smart Systems and Collaborative Innovation Networks for Productivity Improvement in SMEs." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 1 (December 23, 2020): 3. http://dx.doi.org/10.3390/joitmc7010003.

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The adoption of Smart Manufacturing Systems in manufacturing companies is often seen as a strategy towards achieving improvements in productivity. However, there is little evidence to indicate that UK manufacturing SMEs are prepared for the implementation of such systems. Through the employment of a triangulation research approach involving the detailed examination of 36 UK manufacturing SMEs from three manufacturing sectors, this study investigates the level of awareness and understanding within SMEs of Smart Manufacturing Systems. The development of a profiling tool is shown and is subsequently used to audit company awareness and understanding of the key technologies, collaborative networks and systems of SMS. Further information obtained from semi-structured interviews and observations of manufacturing operations provide further contextual information. The findings indicate that whilst the priority technologies and systems differ between manufacturing sectors, the key issues around the need for developing appropriate collaborative networks and knowledge management systems are common to all sectors.

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Kang, Kai, Bing Qing Tan, and Ray Y. Zhong. "Multi-attribute negotiation mechanism for manufacturing service allocation in smart manufacturing." Advanced Engineering Informatics 51 (January 2022): 101523. http://dx.doi.org/10.1016/j.aei.2021.101523.

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Towill, D. "Smooth is smart [automobile manufacturing]." Manufacturing Engineer 85, no. 2 (April 1, 2006): 18–23. http://dx.doi.org/10.1049/me:20060202.

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