Industry 4.1 for Wheel Machining Automation

Cheng, Fan‐Tien; Tieng, Hao; Yang, Haw‐Ching; Hung, Min‐Hsiung; Lin, Yu-Chuan; Wei, Chun-Fan; Shieh, Zih-Yan

doi:10.1109/lra.2016.2517208

Cited by 54 publications

(29 citation statements)

References 22 publications

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“…A CPS is composed of machines, storage systems and production facilities that could autonomously exchange information, trigger actions and monitor each other (Kagermann, Wahlster, and Helbig 2013). According to Cheng et al (2016), a CPS links a manufacturing entity virtual (computing) and physical (machines) elements by integrating analogue/digital hardware. IoT provides the needed platform to connect the CPS using a network of sensors, actuators and devices.…”

Section: Historical Backgroundmentioning

confidence: 99%

“…Some authors were interested in defining more specifically IT enablers for Industry 4.0 and smart factories concepts. Cheng et al (2016) presented cyber-physical systems (CPS) to interconnect the physical and cyber world by integrating analogue/digital hardware and the cloud-based manufacturing (CM) characterised by its high scalability, agility, resource pooling, virtualisation, ubiquitous access, etc. Lee, Bagheri, and Kao (2015) developed a CPS architecture for manufacturing systems.…”

Section: It Enablersmentioning

confidence: 99%

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Internet of things and supply chain management: a literature review

Ben‐Daya

Hassini

Bahroun

2017

International Journal of Production Research

958

541

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This paper explores the role of Internet of Things (IoT) and its impact on supply chain management (SCM) through an extensive literature review. Important aspects of IoT in SCM are covered including IoT definition, main IoT technology enablers and various SCM processes and applications. We offer several categorisation of the extant literature, such as based on methodology, industry sector and focus on a classification based on major supply chain processes. In addition, a bibliometric analysis of the literature is also presented. We find that most studies have focused on conceptualising the impact of IoT with limited analytical models and empirical studies. In addition, most studies have focused on the delivery supply chain process and the food and manufacturing supply chains. Areas of future SCM research that can support IoT implementation are also identified.

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Section: Historical Backgroundmentioning

confidence: 99%

Section: It Enablersmentioning

confidence: 99%

Internet of things and supply chain management: a literature review

Ben‐Daya

Hassini

Bahroun

2017

International Journal of Production Research

958

541

View full text Add to dashboard Cite

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“…CPS and IoT devices act as the backbone of IIoT systems and thus generate massive amount of raw data streams, which result in big data [44]. Therefore, real-time analysis of these data can improve machine health and lead to defect-free product manufacturing [1,34,45].…”

Section: Rise Of Big Data In Iiot Systemsmentioning

confidence: 99%

The role of big data analytics in industrial Internet of Things

Rehman

Yaqoob

Salah

et al. 2019

Future Generation Computer Systems

305

117

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Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational-and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.

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“…CPS is an umbrella term that includes sensors, actuators, architectures, tools, databases, applications, and methodologies [1]. Prior publications in CPS for Industry 4.0 has extensively covered a wide array of theoretical and practical aspects [4][5][6][7][8][9][10]. CPS enables manufacturing and service innovation using integration of software-based embedded intelligence.…”

mentioning

confidence: 99%

“…Emerging CPS is expected to be coordinated, distributed, connected, robust and responsive [6]. While the current and future role of CPS is further emphasized in publications [7][8][9][10]. The cost of investment combined with lack of clarity on intellectual property dynamics discourages industries from CPS automation.…”

mentioning

confidence: 99%

Latent Dirichlet Allocation Modeling for CPS Patent Topic Discovery

Govindarajan

Kumar²

2019

Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

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Industry 4.0 is an organized framework to infuse the latest technology in the manufacturing sector. The inclusion of next-generation technologies such as Cyber-Physical Systems (CPS), cloud computing, big data and artificial intelligence approaches increases productivity and manufacturing output in today's dynamic industrial environments. This research is a Latent Dirichlet Allocation (LDA) topic modeling extension from a prior research on technology standards and patent portfolios for industrial CPS [1]. Topic modeling is a statistical approach for discovering topics that occur in a document corpus [2]. Latent Dirichlet Allocation (LDA) is an unsupervised technical approach in topic modeling for efficient and insightful data analysis. A collection of 1868 CPS patents from the US patent database has been used as input to group patents in several relevant topics for industrial CPS using LDA model in this research. Topic modeled patent groups allowed for the identification of relationships between terms and topics, enabling better visualizations of underlying intellectual property dynamics. Top assignees for each group are computed based on LDA results, these insights were unknown in prior investigations. Further, a graphical representation of the topic trend across groups present a direction of promising patents towards industrial application. The correlations presented enhances patent utilization and promotes cross-licensing commercialization.

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Industry 4.1 for Wheel Machining Automation

Cited by 54 publications

References 22 publications

Internet of things and supply chain management: a literature review

Internet of things and supply chain management: a literature review

The role of big data analytics in industrial Internet of Things

Latent Dirichlet Allocation Modeling for CPS Patent Topic Discovery

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