Modular smart controller for Industry 4.0 functions in machine tools

Barton, David K.; Gönnheimer, Philipp; Schade, Florian; Ehrmann, Christopher; Becker, Jürgen; Fleischer, Jürgen

doi:10.1016/j.procir.2019.04.022

Cited by 17 publications

(14 citation statements)

References 27 publications

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“…These require a dedicated solution close to the field level of the Industry 4.0 architecture. To allow for the integration of low-latency data processing with a full-featured Linux OS environment on a single CPU, a smart controller software architecture is proposed in [1]. It is based on the Jailhouse hypervisor [9] to run both a Linux and a real-time operating system (RTOS) in parallel and to isolate them from each other.…”

Section: Related Workmentioning

confidence: 99%

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Evaluation of a Hypervisor-Based Smart Controller for Industry 4.0 Functions in Manufacturing

Schade

Barton

Fleischer

et al. 2021

2021 IEEE 7th World Forum on Internet of Things (WF-IoT)

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Section: Related Workmentioning

confidence: 99%

“…To allow for low-effort adaption of interfaces, data preprocessing, and for the integration of low-latency monitoring functionality, a modular smart controller architecture for data communication and computing has been proposed in [1].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Hypervisor-Based Smart Controller for Industry 4.0 Functions in Manufacturing

Schade

Barton

Fleischer

et al. 2021

2021 IEEE 7th World Forum on Internet of Things (WF-IoT)

Self Cite

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“…It increases product reliability and availability and extends the lifetime of products [18] Real-time data covering the state of the machine tool components and the period the tool operated are collected through sensors technology and IoT [19]. After the data collection, Big Data and Machine Learning solutions would enable building predictive models and algorithms capable of identifying potential failures and reducing downtimes through the early detection of possible problems prior to asset failure [18,[20][21][22][23]. This predictive maintenance results in running continuously in machines and equipment are more efficient and prevent downtime [20,21].…”

Section: Digitalisation Of Machine Tool Industrymentioning

confidence: 99%

Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization

Akyazi

Goti

Oyarbide-Zubillaga

et al. 2020

Metals

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The machine tool industry, which is the starting point of all the metal producing activities, is presently undergoing rapid and continuous changes as a result of the fourth industrial revolution Industry 4.0. Manufacturing models are profoundly transforming with emerging digitalization. Smart technologies like artificial intelligence (AI), big data, the Internet of Things (IoT), digital twin, allow the machine tool companies to optimize processes, increase efficiency and reduce waste through a new phase of automation. These technologies, as well, enable the machine tool producers to reach the aim of creating products with improved performance, extended life, high reliability that are eco-efficient. Therefore, Industry 4.0 could be perceived as an invaluable opportunity for the machine tool sector, only if the sector has a competent workforce capable of handling the implementation of new business models and technological developments. The main condition to create this highly qualified workforce is reskilling and upskilling of the current workforce. Once we define the expected evolution of skills requirements, we can clarify the skills mismatch between the workers and job profiles. Only then, we can reduce them by delivering well-developed trainings. For this purpose, this article identifies the current and foreseen skills requirements demanded by the machine tool industry workforce. To this end, we generated an integrated database for the sector with the present and prospective skills needs of the metal processing sector professionals. The presented sectoral database is a fundamental structure that will make the sector acquire targeted industrial reforms. It can also be an essential instrument for machine tool companies, policymakers, academics and education or training centers to build well-designed and effective training programs to enhance the skills of the labor force.

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“…The test knowledge index is created by the SEA dataset development technique. 23 The dataset has 60 000 focused details, each with three features and a genuine category tag. The data is an arrangement for the time.…”

Section: Experimental Datamentioning

confidence: 99%

Internet of things assisted condition‐based support for smart manufacturing industry using learning technique

Tao

Liu

et al. 2020

Computational Intelligence

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Nowadays, countless industrial IIoT contraptions and sensors are conveyed a sharp plant to gather tremendous information regarding system conditions and a computerized bodily framework for handling industrial plant's mist point of convergence by using keen assem-How to cite this article: Li J, Tao H, Shuhong L, et al. Internet of things assisted condition-based support for smart manufacturing industry using learning technique.

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Modular smart controller for Industry 4.0 functions in machine tools

Cited by 17 publications

References 27 publications

Evaluation of a Hypervisor-Based Smart Controller for Industry 4.0 Functions in Manufacturing

Evaluation of a Hypervisor-Based Smart Controller for Industry 4.0 Functions in Manufacturing

Skills Requirements for the European Machine Tool Sector Emerging from Its Digitalization

Internet of things assisted condition‐based support for smart manufacturing industry using learning technique

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