Digital twin-driven product design, manufacturing and service with big data

Tao, Fei; Cheng, Jiangfeng; Qi, Qinglin; Zhang, Meng; Zhang, He; Sui, Fangyuan

doi:10.1007/s00170-017-0233-1

Cited by 2,000 publications

(987 citation statements)

References 23 publications

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“…In [323], the authors analyze the relationship between the data processing and the energy consumption through investigating the content correlation of the captured data. Traditional centralized approaches are presented in [322], where the authors develop a big data toolbox for manufacturing prediction tasks to bridge the gap between machine learning research and concrete industrial requirements, and in [324], where the authors use big data services in order to design a new method for product design, manufacturing, and service driven by digital twin. Table VI displays the extent of centrality that the various recent approaches have adopted, in terms of computation for big data analytics.…”

Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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Section: Computation and Data Analytics Articlesmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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“…B. , ). Dabei weiß der Digitale Zwilling des Werkstücks zu jeder Zeit, wo in der Fabrik und in der Produktionskette sein reales Pendant sich gerade befindet, welche Produktionsschritte als nächstes folgen müssen und welche Zertifikate schon erworben wurden.…”

Section: Merkmale Des Digitalen Zwillingsunclassified

Was den Digitalen Zwilling zum genialen Kompagnon macht

Bamberg

Urbas

Bröcker

et al. 2019

Chemie Ingenieur Technik

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Im internationalen Wettbewerb kann der Digitale Zwilling für die deutsche Industrie ein entscheidender Wettbewerbsvorteil sein. Dieser Essay adressiert Merkmale eines Digitalen Zwillings aus Anwendersicht und soll Lösungsanbietern helfen, der Produktentwicklung eine kundenorientierte Richtung zu verleihen. Dazu werden die unterschiedlichen Datenmodelle beschrieben, die in den Lebenszyklen von chemischen Prozessen, Anlagen und Produkten eine Rolle spielen. Insbesondere für bestehende (Brownfield‐)Anlagen werden abschließend wesentliche Aspekte des Digitalen Zwillings ausformuliert. Weitere Ausprägungen und Auswirkungen, z. B. auf die Aus‐ und Fortbildung, werden daraus abgeleitet.

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“…Operators o 9 and o 10 possess specialist knowledge, while s 3 consists of group-wise knowledge because it is the most related to the operators. The workloads (number of activities, built-in components, and total activity times) can be easily calculated based on the biadjacency matrices of the proposed model, which supports the balancing of the conveyor belt.…”

Section: Analysis Of the Flexibility Of Operator Assignmentmentioning

confidence: 99%

“…Horizontal and intercompany integration should connect the elements of the supply chain [6], while vertical integration should connect information related to the entire product life cycle [7]. According to this new concept, the improvement and optimization of production technologies based on cyber-physical systems (CPS) are realized by the simultaneous utilization of information related to production systems [8], products, models [9], simulators, and process data [10,11].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Network‐Based Production Flow Analysis

2018

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A multilayer network model for the exploratory analysis of production technologies is proposed. To represent the relationship between products, parts, machines, resources, operators, and skills, standardized production and product-relevant data are transformed into a set of bi-and multipartite networks. This representation is beneficial in production flow analysis (PFA) that is used to identify improvement opportunities by grouping similar groups of products, components, and machines. It is demonstrated that the goal-oriented mapping and modularity-based clustering of multilayer networks can serve as a readily applicable and interpretable decision support tool for PFA, and the analysis of the degrees and correlations of a node can identify critically important skills and resources. The applicability of the proposed methodology is demonstrated by a well-documented benchmark problem of a wire-harness production process. The results confirm that the proposed multilayer network can support the standardized integration of production-relevant data and exploratory analysis of strongly interconnected production systems.

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Digital twin-driven product design, manufacturing and service with big data

Cited by 2,000 publications

References 23 publications

Data Management in Industry 4.0: State of the Art and Open Challenges

Data Management in Industry 4.0: State of the Art and Open Challenges

Was den Digitalen Zwilling zum genialen Kompagnon macht

Multilayer Network‐Based Production Flow Analysis

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