Relevant bibliographies by topics / Fog manufacturing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fog manufacturing'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Fog manufacturing"

1

Wang, Junliang, Peng Zheng, Youlong Lv, Jingsong Bao, and Jie Zhang. "Fog-IBDIS: Industrial Big Data Integration and Sharing with Fog Computing for Manufacturing Systems." Engineering 5, no. 4 (August 2019): 662–70. http://dx.doi.org/10.1016/j.eng.2018.12.013.

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Wang, Juan, and Di Li. "Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing." Sensors 19, no. 5 (February 28, 2019): 1023. http://dx.doi.org/10.3390/s19051023.

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Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.
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Sherlekar, Riddhiman, Binil Starly, and Paul H. Cohen. "Provisioned Data Distribution for Intelligent Manufacturing via Fog Computing." Procedia Manufacturing 34 (2019): 893–902. http://dx.doi.org/10.1016/j.promfg.2019.06.158.

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Chen, Xin. "Big Data Integration Method of Mathematical Modeling and Manufacturing System Based on Fog Calculation." Mathematical Problems in Engineering 2021 (July 9, 2021): 1–9. http://dx.doi.org/10.1155/2021/9987714.

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Using big data to promote economic development, improve social governance, and improve service and regulatory capabilities is becoming a trend. However, the current cloud computing for data processing has been difficult to meet the demand, and the server pressure has increased dramatically, so people pay special attention to the big data integration of fog computing. In order to make the application of big data meet people’s needs, we have established relevant mathematical models based on fog calculation, made system big data integration, collected relevant data, designed experiments, and obtained relevant research data by reviewing relevant literature and interviewing professionals. The research shows that big data integration using fog computing modeling has the characteristics of fast response and stable function. Compared with cloud computing and previous computer algorithms, big data integration has obvious advantages, and the computing speed is nearly 20% faster than cloud computing and about 35% higher than other computing methods. This shows that big data integration built by fog computing can have a huge impact on people’s lives.
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Shrestha, Hewan, Puviyarai T., Sana Sodanapalli, and Chandramohan Dhasarathan. "Evolution of Fog Computing Applications, Opportunities, and Challenges." International Journal of Fog Computing 4, no. 1 (January 2021): 1–17. http://dx.doi.org/10.4018/ijfc.2021010101.

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The emerging trend of internet of things in recent times is a blessing for various industries in the world. With the increasing amount of data generated by these devices, it makes it difficult for proper data flow and computation over the regular cloud architecture. Fog computing is a great alternative for cloud computing as it supports computation in devices over a large distributed geographical area, which is a plus for fog computing. Having applications in various domains including healthcare, logistics, design, marketing, manufacturing, and many more, fog computing is a great boon for the future. Evolving fog computing in various domains with different methods and techniques has shaped a clear future for it. Applicability of fog computing in vehicular communications and storage-as-a-service has made the term more popular these days. It is a review of all the possible fog computing-enabled applications and their future scope. It also prepares a basis for further research into fog computing domain-enabled services with low latency and minimum costs.
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Jiang, Yuan, Renjie Xu, Siru Liu, Guilian Liu, and Xiaohong Yan. "Electrostatic fog collection mechanism and design of an electrostatic fog collector with nearly perfect fog collection efficiency." Chemical Engineering Science 247 (January 2022): 117034. http://dx.doi.org/10.1016/j.ces.2021.117034.

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Basir, Rabeea, Saad Qaisar, Mudassar Ali, Monther Aldwairi, Muhammad Ikram Ashraf, Aamir Mahmood, and Mikael Gidlund. "Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges." Sensors 19, no. 21 (November 5, 2019): 4807. http://dx.doi.org/10.3390/s19214807.

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Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.
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Gupta, Rajni. "Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing." International Journal of Fog Computing 2, no. 2 (July 2019): 57–70. http://dx.doi.org/10.4018/ijfc.2019070104.

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Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.
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Barenji, Ali Vatankhah, Hanyang Guo, Yitong Wang, Zhi Li, and Yiming Rong. "Toward blockchain and fog computing collaborative design and manufacturing platform: Support customer view." Robotics and Computer-Integrated Manufacturing 67 (February 2021): 102043. http://dx.doi.org/10.1016/j.rcim.2020.102043.

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Wu, Dazhong, Shaopeng Liu, Li Zhang, Janis Terpenny, Robert X. Gao, Thomas Kurfess, and Judith A. Guzzo. "A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing." Journal of Manufacturing Systems 43 (April 2017): 25–34. http://dx.doi.org/10.1016/j.jmsy.2017.02.011.

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Dissertations / Theses on the topic "Fog manufacturing"

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Nallendran, Vignesh Raja. "Predicting Performance Run-time Metrics in Fog Manufacturing using Multi-task Learning." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102501.

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The integration of Fog-Cloud computing in manufacturing has given rise to a new paradigm called Fog manufacturing. Fog manufacturing is a form of distributed computing platform that integrates Fog-Cloud collaborative computing strategy to facilitate responsive, scalable, and reliable data analysis in manufacturing networks. The computation services provided by Fog-Cloud computing can effectively support quality prediction, process monitoring, and diagnosis efforts in a timely manner for manufacturing processes. However, the communication and computation resources for Fog-Cloud computing are limited in Fog manufacturing. Therefore, it is significant to effectively utilize the computation services based on the optimal computation task offloading, scheduling, and hardware autoscaling strategies to finish the computation tasks on time without compromising on the quality of the computation service. A prerequisite for adapting such optimal strategies is to accurately predict the run-time metrics (e.g., Time-latency) of the Fog nodes by capturing their inherent stochastic nature in real-time. It is because these run-time metrics are directly related to the performance of the computation service in Fog manufacturing. Specifically, since the computation flow and the data querying activities vary between the Fog nodes in practice. The run-time metrics that reflect the performance in the Fog nodes are heterogenous in nature and the performance cannot be effectively modeled through traditional predictive analysis. In this thesis, a multi-task learning methodology is adopted to predict the run-time metrics that reflect performance in Fog manufacturing by addressing the heterogeneities among the Fog nodes. A Fog manufacturing testbed is employed to evaluate the prediction accuracies of the proposed model and benchmark models. The proposed model can be further extended in computation tasks offloading and architecture optimization in Fog manufacturing to minimize the time-latency and improve the robustness of the system.
Master of Science
Smart manufacturing aims at utilizing Internet of things (IoT), data analytics, cloud computing, etc. to handle varying market demand without compromising the productivity or quality in a manufacturing plant. To support these efforts, Fog manufacturing has been identified as a suitable computing architecture to handle the surge of data generated from the IoT devices. In Fog manufacturing computational tasks are completed locally through the means of interconnected computing devices called Fog nodes. However, the communication and computation resources in Fog manufacturing are limited. Therefore, its effective utilization requires optimal strategies to schedule the computational tasks and assign the computational tasks to the Fog nodes. A prerequisite for adapting such strategies is to accurately predict the performance of the Fog nodes. In this thesis, a multi-task learning methodology is adopted to predict the performance in Fog manufacturing. Specifically, since the computation flow and the data querying activities vary between the Fog nodes in practice. The metrics that reflect the performance in the Fog nodes are heterogenous in nature and cannot be effectively modeled through conventional predictive analysis. A Fog manufacturing testbed is employed to evaluate the prediction accuracies of the proposed model and benchmark models. The results show that the multi-task learning model has better prediction accuracy than the benchmarks and that it can model the heterogeneities among the Fog nodes. The proposed model can further be incorporated in scheduling and assignment strategies to effectively utilize Fog manufacturing's computational services.
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Ranjan, Rajit. "Design for Manufacturing and Topology Optimization in Additive Manufacturing." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439307951.

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Scholtz, Robert L. (Robert Louis) 1972. "Strategies for manufacturing low volume semiconductor products in a high volume manufacturing environment." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/44608.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2002.
Includes bibliographical references (p. 82-83).
The rapid growth of the digital communications market has prompted several large semiconductor manufacturers, including Intel Corporation, to begin the design and manufacture of communication ICs. The communications ICs are currently produced in much lower volumes than products such as microprocessors and memory. These low-volume products have been reported to cause operational problems, such as excessive cost, slow throughput time, and low yield when manufactured in semiconductor fabs designed for high volume manufacturing. This thesis examines the operational problems caused by the manufacture of low-volume semiconductor products and explores potential improvements. A financial model was developed to compare the cost of manufacturing low-volume products using several different strategies in existing high-volume fabs. The model results demonstrated that mask set cost, a fixed cost, becomes a very large component of total production cost as the product volume is reduced. Further, this model identified multi-product wafers, a scheme of fabricating several products on a single wafer, as a strategy with potential for savings up to approximately 75% of the manufacturing cost of low-volume products. A second financial model was developed to consider more detailed aspects of fabricating products on multi-product wafers. This model considered the sensitivity of the potential cost savings to changes in demand and changes to the design of multi-product wafers. This model also demonstrated that significant savings are possible with the multi-product wafer strategy, especially if the products are carefully matched (by die size and demand) with other products on the multi-product wafer. Finally, a brief organizational study was conducted to analyze the implementation of a multi-product wafer manufacturing process for the production of low-volume CMOS ICs at Intel Corporation.
by Robert L. Scholtz, III.
S.M.
M.B.A.
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Correa, Manuel (Manuel Roza). "Implementing cellular manufacturing methodologies to improve the performance of a manufacturing operation." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66064.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 62).
Many traditional high-mix, low-volume manufacturing facilities utilize process villages, whereby similar operations are grouped together in an effort to gain efficiencies. While process villages can improve certain metrics and increase capacity utilization, many wastes can be created that outweigh most benefits. In many cases process villages operate with large batch sizes, which result in longer lead-times and increased inventories. A different approach, for an appropriate range of product mixes and volumes, is to form production cells for common products that group different processes together to form complete value streams. The manufacturing cells focus on completely finishing products before handing them off and result in reduced lead-times and inventories. This thesis presents a methodology for implementing such production cells in a manufacturing environment. The author spent six months at a leading aerospace company implementing cellular manufacturing principles in designing several production cells for a transmission component manufacturing department as part of a lean transformation effort. The cell design methodology implemented consisted of several key processes such as process flow design, material handling design, workplace organization, and staffing. The process flow design consisted of activities such as grouping products into families, designing value streams, and performing capacity analysis. Material handling design developed solutions for how products physically flow through the cell and managing work-in-process. Workplace organization focused on utilizing visual factory and 5S principles to ensure strong communication and information flow as well as first class equipment organization and housekeeping. Finally, workload analyses were performed to appropriately staff the cells to minimize costs and ensure efficient operations. Ultimately, the goal of any transformation effort is to reduce waste and add value, which would not be possible if the culture of the organization did not support the physical and operational design changes. Hence the final, and arguably most important piece of the transformation, which the author participated in, was engaging the workforce to drive the culture change.
by Manuel Correa.
S.M.
M.B.A.
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Lu, Ilyssa Jing. "Innovation enabling manufacturing processes." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44309.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2008.
Includes bibliographical references (p. 61-62).
Global operations for multinational companies today pose a particularly challenging environment for maintaining fluid knowledge transfer and effective communication methodologies. In a continuous drive for product innovation, process development often takes on lower priority to other initiatives that directly affect the design and delivery of a product. However, existing literature shows that process development and governance are critical to sustainable growth in the global marketplace. Multinational companies must recognize the need to integrate process development in a product centric enterprise to maintain effective information flow and clear communication channels. Cisco faces this challenging in maintaining effective cross-functional communication while growing through acquisition and new product developments. Cisco also faces additional complexity in managing a global network of outsourced manufacturing activities. This research analyzes two case studies in process development within the Manufacturing organization at Cisco. Specifically, these two case studies focus on driving early engagement of manufacturing concerns in the product lifecycle and effective means of facilitating this initiative.
by Ilyssa Jing Lu.
S.M.
M.B.A.
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Holman, Jason (Jason William) 1974. "Optical networking equipment manufacturing." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/44603.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2001.
Includes bibliographical references (leaf 70).
Celestica, a global contract manufacturer specializing in printed circuit board assembly and computer assembly, has recently begun manufacturing equipment for the optical networking equipment (ONE) industry. The expansion to include ONE manufacturing requires the development of new skills in handling optical fiber and components, a new supply chain strategy, and a new approach to manufacturing systems control. Celestica is developing a set of standards for ONE manufacturing that will support the rapid development of the new skills required for this industry. This work outlines the standards and explores the specific issues related to manufacturing with optical fiber, including the mechanical reliability and optical performance of various types of optical fibers. An overview of the telecommunications industry is provided, including an analysis of its supply chain structure. Observations are made on trends in the industry and the ways that these trends have affected Celestica in the past, and could impact Celestica in the future. Finally, Celestica's current approach to manufacturing systems control is evaluated, and suggestions are made for improving systems control and project management when manufacturing for such a rapidly evolving industry.
by Jason Holman.
S.M.
M.B.A.
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Terenzi, Marco. "Additive manufacturing e ottimizzazione topologica: massimizzare le prestazioni di una pinza freno per applicazioni motorsport." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18930/.

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I veicoli ad alte prestazioni sono soggetti ad elevati carichi per piccoli intervalli di tempo. Questo comporta diverse criticità sulle componenti che costituiscono la vettura: una di queste è la pinza freno. Al fine di renderla performante è necessario il possesso di due proprietà. In primo luogo, la pinza freno deve essere il più leggera possibile poiché essa conferisce un'inerzia nella risposta della sospensione del veicolo, procurando il distacco dello pneumatico dal suolo e causando perdita di aderenza. In secondo luogo, è necessario contenere le deformazioni della pinza freno garantendo un determinato feeling per il pilota. Il compito del progettista è ottimizzare questi due parametri che hanno effetti antitetici. Questa difficoltà porta il progettista a creare design molto complessi per raggiungere l’ottimale e non sempre le geometrie ottenute sono realizzabili con tecnologie convenzionali. Questo studio riguarda il miglioramento prestazionale di una pinza freno costruita con una lega di alluminio 7075-T6 e lavorato dal pieno. Gli obbiettivi sono quello di produrre il nuovo corpo in titanio TI6Al4V, dal momento che le temperature di esercizio portano a grandi decadute di caratteristiche meccaniche dell’alluminio, contenere il più possibile la massa a fronte dell’aumento di densità di materiale e ovviamente limitare le deformazioni. Al fine di ottenere gli obbiettivi prefissati sono utilizzati metodi agli elementi finiti in diverse fasi della progettazione: per acquisire una geometria di partenza (ottimizzazione topologica) e per la validazione delle geometrie ottenute. Le geometrie ricavate tramite l’ottimizzazione topologica devono essere ricostruite tramite software CAD affinché possano essere ingegnerizzate. Durante la modellazione è necessario valutare quale tecnologia è più vantaggiosa per produrre il componente. In questo caso studio si utilizza un processo di addizione di materiale, più specificatamente una tecnica Selective Laser Melting (SLM).
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Joing, Matthew J. (Matthew John) 1972. "Applicability of lean manufacturing and quick response manufacturing in a high-mix low-volume environment." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/34767.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.
Includes bibliographical references (p. 66).
As today's manufacturers face increasing pressure to improve costs and compete globally, many are turning to the philosophy of Lean Manufacturing as exemplified by the Toyota Production System. Lean is most successful when production is characterized by a few high-volume products, but may not be the answer as the production mix increases and volume decreases. This thesis focuses on this high-mix, low-volume type of production in addition to two other key production system characteristics: demand variability and degree of customization. A manufacturer's position along these four characteristics is very important to the applicability of Lean theory. The alternative philosophy of Quick Response Manufacturing (QRM) is compared to Lean and shown to offer a better fit in some cases. One such case where Lean does not fit neatly is circuit card assembly at Raytheon Systems Limited in Glenrothes, Scotland, where the author conducted his six-month LFM internship. Five steps towards manufacturing improvement are focused on in this thesis: choosing metrics, reorganizing the factory, selecting lot sizes, implementing a production control strategy, and deciding on a material presentation method. The recommended steps to improve circuit card assembly include ideas from both Lean and QRM. This mix of ideas was implemented at Raytheon before the end of the internship and resulted in marked improvement. On-time delivery and customer satisfaction dramatically improved while lead times and inventories dropped significantly. Using Lean Manufacturing as the sole guideline for improvement was not appropriate for this particular manufacturing system. The final takeaway from the internship and thesis is that there is no one-size-fits-all manufacturing philosophy.
by Matthew J. Joing.
S.M.
M.B.A.
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Chan, Chi-fung. "Computer-aided design and manufacturing of tactile maps." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B37895722.

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Chan, Chi-fung, and 陳智鋒. "Computer-aided design and manufacturing of tactile maps." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B37895722.

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Books on the topic "Fog manufacturing"

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Purchasing for manufacturing. New York: Industrial Press, 1996.

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Withnell, Stephen, and W. Van Puymbroeck, eds. Communications for Manufacturing. London: Springer London, 1990. http://dx.doi.org/10.1007/978-1-4471-1820-6.

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Dauch, Richard E. Passion for manufacturing. Dearborn, Mich: Society of Manufacturing Engineers, 1993.

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Elhajjar, Rani, and Tracy Gill. Studies into Additive Manufacturing for In-Space Manufacturing. Warrendale, PA: SAE International, 2016. http://dx.doi.org/10.4271/srp-001.

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Communication networks for manufacturing. Englewood Cliffs, N.J: Prentice Hall, 1990.

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A, Curtis Mark. Tool design for manufacturing. New York: Wiley, 1986.

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W, Hunt William, ed. Manufacturing processes for technology. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2001.

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Ross, Wallach Paul, ed. Blueprint reading for manufacturing. Albany, N.Y: Delmar Publishers, 1988.

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Madsen, Lynnette D., and Erik B. Svedberg, eds. Materials Research for Manufacturing. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23419-9.

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Camarinha-Matos, Luis M., Hamideh Afsarmanesh, and Vladimir Marik, eds. Intelligent Systems for Manufacturing. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-0-387-35390-6.

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Book chapters on the topic "Fog manufacturing"

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Pal, Surjya Kanta, Debasish Mishra, Arpan Pal, Samik Dutta, Debashish Chakravarty, and Srikanta Pal. "Data Communication-Edge, Fog, and Cloud Computing." In Springer Series in Advanced Manufacturing, 293–335. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81815-9_5.

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Liang, Y. C., W. D. Li, X. Lu, and S. Wang. "Fog Computing and Convolutional Neural Network Enabled Prognosis for Machining Process Optimization." In Springer Series in Advanced Manufacturing, 13–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66849-5_2.

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Liang, Y. C., W. D. Li, P. Lou, and J. M. Hu. "Thermal Error Prediction for Heavy-Duty CNC Machines Enabled by Long Short-Term Memory Networks and Fog-Cloud Architecture." In Springer Series in Advanced Manufacturing, 125–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66849-5_6.

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Mihai, Viorel, Dan Popescu, Loretta Ichim, and Cristian Drăgana. "Fog Computing Monitoring System for a Flexible Assembly Line." In Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future, 197–209. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27477-1_15.

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Schmidt, Cameron, and Jake Ellis. "Manufacturing." In Enterprise Cloud Computing for Non-Engineers, 77–86. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018. | “A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.”: Auerbach Publications, 2018. http://dx.doi.org/10.1201/9781351049221-4.

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Dangel, Rainer. "Manufacturing Technologies." In Injection Moulds for Beginners, 269–93. München: Carl Hanser Verlag GmbH & Co. KG, 2016. http://dx.doi.org/10.3139/9781569906323.009.

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Dangel, Rainer. "Manufacturing Technologies." In Injection Molds for Beginners, 273–97. München: Carl Hanser Verlag GmbH & Co. KG, 2020. http://dx.doi.org/10.3139/9781569908198.009.

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Guo, Jiajie, and Kok-Meng Lee. "Intelligent Manufacturing." In Flexonics for Manufacturing and Robotics, 109–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2667-7_5.

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Hattersley, Roy. "Manufacturing Matters." In Economic Priorities for a Labour Government, 91–103. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1007/978-1-349-18608-2_7.

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Garbie, Ibrahim H., and Hamid R. Parsaei. "Manufacturing Complexity." In Reconfigurable Manufacturing Enterprises for Industry 4.0, 37–46. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780429200311-5.

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Conference papers on the topic "Fog manufacturing"

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Wang, Lening, Yutong Zhang, and Ran Jin. "A Monitoring System for Anomaly Detection in Fog Manufacturing." In 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS). IEEE, 2020. http://dx.doi.org/10.1109/icps48405.2020.9274741.

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Wu, Dazhong, Janis Terpenny, Li Zhang, Robert Gao, and Thomas Kurfess. "Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8559.

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Abstract:
Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fog-enabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.
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Zhang, Yutong, Lening Wang, Xiaoyu Chen, and Ran Jin. "Fog Computing for Distributed Family Learning in Cyber-Manufacturing Modeling." In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). IEEE, 2019. http://dx.doi.org/10.1109/icphys.2019.8780264.

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Al Sunny, S. M. Nahian, Xiaoqing Liu, and Md Rakib Shahriar. "Development and optimization of an MTConnect based edge computing node for remote monitoring in cyber manufacturing systems." In 2020 IEEE International Conference on Fog Computing (ICFC). IEEE, 2020. http://dx.doi.org/10.1109/icfc49376.2020.00014.

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Chen, Xiaoyu, Lening Wang, Canran Wang, and Ran Jin. "Predictive offloading in mobile-fog-cloud enabled cyber-manufacturing systems." In 2018 IEEE Industrial Cyber-Physical Systems (ICPS). IEEE, 2018. http://dx.doi.org/10.1109/icphys.2018.8387654.

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Wang, Lening, Yutong Zhang, Xiaoyu Chen, and Ran Jin. "Online Computation Performance Analysis for Distributed Machine Learning Pipelines in Fog Manufacturing." In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020. http://dx.doi.org/10.1109/case48305.2020.9216979.

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Mocanu, Stefan, Giorgiana Geampalia, Oana Chenaru, and Radu Dobrescu. "Fog-Based Solution for Real-Time Monitoring and Data Processing in Manufacturing." In 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2018. http://dx.doi.org/10.1109/icstcc.2018.8540783.

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Breunig, David Albert, Alain Roedel, and Thomas Bauernhansl. "Extending Service-oriented Architectures in Manufacturing towards Fog and Edge Levels." In 2020 IEEE 18th International Conference on Industrial Informatics (INDIN). IEEE, 2020. http://dx.doi.org/10.1109/indin45582.2020.9442152.

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Sun, Q. D., Y. F. Shao, L. D. Wang, J. C. Zuo, and L. Gui. "Design scheme of FPGA-based digital closed loop control system for FOG." In The 2015 International Conference on Design, Manufacturing and Mechatronics (ICDMM2015). WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814730518_0042.

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Xiao, Yunhao, Wenbin Liu, and Dequan Liu. "Research on Key Technologies of Oil Mist Lubrication and Residual Fog Recovery." In 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM). IEEE, 2019. http://dx.doi.org/10.1109/wcmeim48965.2019.00069.

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Reports on the topic "Fog manufacturing"

1

Taylor, Richard A. Cyber for Manufacturing. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1614833.

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Peterson, Dominic S. Additive Manufacturing for Ceramics. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1119593.

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Scott, Troy J., Travis J. Beaulieu, Ginger D. Rothrock, and Alan C. O'Connor. Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Additive Manufacturing. National Institute of Standards and Technology, October 2016. http://dx.doi.org/10.6028/nist.gcr.16-006.

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Gallaher, Michael P., Zachary T. Oliver, Kirsten T. Rieth, and Alan C. O'Connor. Economic Analysis of Technology Infrastructure Needs for Advanced Manufacturing: Smart Manufacturing. National Institute of Standards and Technology, October 2016. http://dx.doi.org/10.6028/nist.gcr.16-007.

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Emory S. De Castro, Yu-Min Tsou, Mark G. Roelofs, and Olga Polevaya. Integrated Manufacturing for Advanced MEAs. Office of Scientific and Technical Information (OSTI), March 2007. http://dx.doi.org/10.2172/901566.

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Davis, Wayne J., and Albert T. Jones. Hierarchies for computer-integrated manufacturing :. Gaithersburg, MD: National Institute of Standards and Technology, 1989. http://dx.doi.org/10.6028/nist.ir.88-3744.

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Charbon, C., and R. A. LeSar. Modeling for environmentally conscious manufacturing. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/515630.

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Beghini, Lauren L., Michael Stender, and Michael Veilleux. Process Modeling for Additive Manufacturing. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1562431.

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Testroet, Frank B. Manufacturing Guide for Elastomeric Seals. Fort Belvoir, VA: Defense Technical Information Center, December 1989. http://dx.doi.org/10.21236/ada227511.

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Stocker, Michael, and Eric Stanfield. Metrology for Fuel Cell Manufacturing. Office of Scientific and Technical Information (OSTI), February 2015. http://dx.doi.org/10.2172/1210884.

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