Evolving the Digital Industrial Infrastructure for Production: Steps Taken and the Road Ahead

Author:

Pennekamp Jan,Belova Anastasiia,Bergs Thomas,Bodenbenner Matthias,Bührig-Polaczek Andreas,Dahlmanns Markus,Kunze Ike,Kröger Moritz,Geisler Sandra,Henze Martin,Lütticke Daniel,Montavon Benjamin,Niemietz Philipp,Ortjohann Lucia,Rudack Maximilian,Schmitt Robert H.,Vroomen Uwe,Wehrle Klaus,Zeng Michael

Abstract

AbstractThe Internet of Production (IoP) leverages concepts such as digital shadows, data lakes, and a World Wide Lab (WWL) to advance today’s production. Consequently, it requires a technical infrastructure that can support the agile deployment of these concepts and corresponding high-level applications, which, e.g., demand the processing of massive data in motion and at rest. As such, key research aspects are the support for low-latency control loops, concepts on scalable data stream processing, deployable information security, and semantically rich and efficient long-term storage. In particular, such an infrastructure cannot continue to be limited to machines and sensors, but additionally needs to encompass networked environments: production cells, edge computing, and location-independent cloud infrastructures. Finally, in light of the envisioned WWL, i.e., the interconnection of production sites, the technical infrastructure must be advanced to support secure and privacy-preserving industrial collaboration. To evolve today’s production sites and lay the infrastructural foundation for the IoP, we identify five broad streams of research: (1) adapting data and stream processing to heterogeneous data from distributed sources, (2) ensuring data interoperability between systems and production sites, (3) exchanging and sharing data with different stakeholders, (4) network security approaches addressing the risks of increasing interconnectivity, and (5) security architectures to enable secure and privacy-preserving industrial collaboration. With our research, we evolve the underlying infrastructure from isolated, sparsely networked production sites toward an architecture that supports high-level applications and sophisticated digital shadows while facilitating the transition toward a WWL.

Publisher

Springer International Publishing

Reference65 articles.

1. Bader L, Pennekamp J et al (2021) Blockchain-based privacy preservation for supply chains supporting lightweight multi-hop information accountability. Inf Process Manag 58(3):102529

2. Bergs T, Niemietz P et al (2020) Punch-to-punch variations in stamping processes. In: Proceedings of 2020 IEEE 18th world symposium on applied machine intelligence and informatics (SAMI’20). IEEE

3. Beyer B, Jones C et al (2016) Site reliability engineering: how Google runs production systems. O’Reilly

4. Bodenbenner M, Sanders MP et al (2020) Domain-specific language for sensors in the internet of production. In: Proceedings of 10th congress of the German academic association for production technology (WGP’20), vol 20. p100206, Springer

5. Bodenbenner M, Montavon B et al (2021) FAIR sensor services – towards sustainable sensor data management. Measur Sens 18

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Recurrent neural networks as virtual cavity pressure and temperature sensors in high-pressure die casting;The International Journal of Advanced Manufacturing Technology;2024-08-29

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3