Secure data storage and service automation for cyber physical production systems through distributed ledger technologies
1
Cyber-Physical Production Systems, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
2
Chair of Machine Tools Development and Adaptive Controls, TU Dresden, Germany
Submission date: 2020-11-09
Final revision date: 2020-12-15
Acceptance date: 2020-12-22
Online publication date: 2021-03-29
Publication date: 2021-03-29
Corresponding author
Kilian Armin Nölscher
Cyber-Physical Production Systems, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
Cyber-Physical Production Systems, Fraunhofer Institute for Machine Tools and Forming Technology IWU, Germany
Journal of Machine Engineering 2021;21(1):89-97
KEYWORDS
TOPICS
ABSTRACT
In this paper, we use the blockchain technology to design a prototype to secure process data from a 3D-printer. Datastreams are gathered from various sources such as OPC UA servers and autonomous retrofit sensor nodes. This is followed by pre-processing for data reduction, storage in a data model, and the generation of a unique hash value over it. The hash values are stored in a blockchain using appropriate consensus methods, taking into account their temporal origin and production identification number. This also includes the context-related influence of sensor signals on the production process Restrictive access regulations using smart contracts make a partially or fully automated machine tool calibration possible. In this context, we show to realize a process partial or full automation through smart contracts. Physical machine tools and virtual simulations are integrated into the blockchain network to document the stability and performance.
REFERENCES (23)
1.
BÖHLER T., 2020, So Schafft Einzelteilrückverfolgung Mehr Transparenz, https://www.produktion.de/tech... -schafft-einzelteilrueckverfolgung-mehr-transparenz-262.html, (14.05. 2020).
2.
FEDKENHAUER T., Datenaustausch Als Wesentlicher Bestandteil der Digitalisierung, https://www.pwc.de/de/ digitale -transformation/studie-datenaustausch-digitalisierung.pdf, (14.05.2020).
3.
BOTTHOF A., HARTMANN E.A., 2015, Zukunft der Arbeit in Industrie 4.0. Springer Berlin Heidelberg, 99.
4.
GSMA HEAD OFFICE, 2020, Opportunities and Use Cases for Distributed Ledger Technologies in IoT, https://www.gsma.com/iot/wp-co..., 14.05.2020.
5.
NEUGEBAUER R., 2018, Digitalisierung: Schlüsseltechnologien für Wirtschaft und Gesellschaft, 154f, Springer.
6.
WITT E., ANTON C., 2020, Additive Fertigung. Entwicklungen, Möglichkeiten und Herausforderungen Stellung-nahme, Deutsche Akademie Der Naturforscher Leopoldina e.V., Halle (Saale).
7.
KLAHN C., MEBOLDT M., HÖFFKEN S., 2020, So Funktioniert Qualitätssicherung in der Additiven Fertigung, https://www.mission-additive.d..., (01.11. 2020).
8.
NEUGEBAUER R., 2018, Digitalisierung: Schlüsseltechnologien für Wirtschaft und Gesellschaft, 197. Springer.
9.
GEBHARDT M., WEGENER K., 2013. Temperatureinfluss auf Werkzeugmaschinen, Machine Tools and Machinery (Manufacturing Technology), ETH Zürich, 59–63.
11.
REINERO B., 2017, Transitioning from Relational Databases to MongoDB – Data Models, https://www.mongo db.com/blog/post/transitioning-from-relational-databases-to-mongoDB, 27/4.
12.
GÖTZ C., 2020, MQTT: Protokoll für das Internet der Dinge, https://www.heise.de/developer..., 08.05.2020.
16.
RED PULSE, All Blockchain are DLTs but not all DLTs are Blockchain, https://www.redpulse.com/insig... 20190601/all-blockchain-are-DLTs-but-not-all-DLTs-are-blockchain--e14df7e07f, (09.05.2020).
17.
ZHENG Z., XIE S., DAI H., CHEN X., WANG H., 2017, An Overview of Blockchain Technology: Architecture, Consensus and Future Trends, IEEE 6th International Congress on Big Data.
18.
LEMME G., LEMME, D., NÖLSCHER K.A., IHLENFELDT S., 2020, Towards Safe Service Ecosystems for Production for Value Networks and Manufacturing Monitoring, Journal of Machine Engineering, 20/1, 117–126.
19.
SZABO N., 1997, The Idea of Smart Contracts, Satoshi Nakamoto Institute, https://nakamotoinstitute.org/....
20.
BOGENSPERGER A., ZEISELMAIR A., HINTERSTOCKER M., 2018, The Blockchain Technology – a Chance to Transform the Energy Supply?, Report section technology description, 46f.
21.
LEMME G., NÖLSCHER K.A., 2019, Wireless Sensor Network for Retrofitting Production Systems, 18 Fachge spräch Sensornetze der GI/ITG Fachgruppe, Kommunikation und Verteilte Systeme, Magdeburg.
22.
ECLIPSE, 2020, Production Performance Management Protocol Specification, https://www.eclipse.org/unide /specification/v3/machine-message#messageDetail, (14.05.2020).
23.
PRITZKER P., GALLAGHER P.D., 2015, SHA-3 Standard: Permutation-Based Hash and Extendable-Output Functions, Laboratory National Institute of Standards and Technology.
| eISSN: | 2391-8071 |
| ISSN: | 1895-7595 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
