Digital Twin Data Modeling with AutomationML and a Communication Methodology for Data Exchange

By Greyce N. Schroeder, Charles Steinmetz, C. Pereira, D. Espíndola. 2016

429 citations recorded by the source corpus.

Abstract: In the context of the Cyber Physical Systems toward the realization of a Digital Twin system for future manufacturing and product service systems we propose the use of AutomationML to model attributes related to the Digital Twin. Also, we propose that this model is very useful for data exchange between different systems that are connected with the Digital Twin. We present a case study where a industrial component was modeled and simulated to prove that our methodology works.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/96023195e889fc258e6ff30aa99d250982dfae01

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In Industrial and Manufacturing Engineering

Citations in the corpus

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues (2020, background): “[73] Industrial valve PLCopen Exchange data between Digital Twin and other systems”
Defining a Digital Twin-based Cyber-Physical Production System for autonomous manufacturing in smart shop floors (2019, methodology): “Data interfaces and protocols, e.g. MTConnect, AutomationML, XML, TCP/IP and HTTP, are used to realise the cyber-physical model mapping and data synchronisation between PSF and CSF.”
Characterising the Digital Twin: A systematic literature review (2020, background): “16 Technical Implementations [16] [98] [87] [51] [12] [57] [77] [100] [112] [105] [11] [109] [22] [17] [16] [16] [94] [112] [111] [107] [112] [111] [107] [106] [112] [110] [5] [57] [75] [104] [71] [2] [27] [6] [55] [16] [87] [46] [80] [8] [100] [88] [84] [112] [46] [80]”
Digital twin paradigm: A systematic literature review (2021, methodology): “( Ríos et al., 2015) and (Schleich et al., 2017) specify the product life cycle from conception and design to usage and servicing while (G. N. Schroeder et al., 2016) suggest the existence of a Twin from the beginning of a product’s life until its disposal.”
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, background): “Models and data can be conside constantly being produced, used, and stored from the beginning until the disposal of a product [35,52].”
Digital Twin in Industry: State-of-the-Art (2019, methodology): “A case study on industrial valves was conducted to validate the method [18].”
Assessing relations between Circular Economy and Industry 4.0: a systematic literature review (2019, methodology): “Among several forms of simulation, Digital Twin (DT) is the most common method for supporting data modelling and exchange (Schroeder et al. 2016a, 2016b) or holons development (Trentesaux and Giret 2015; Turner et al. 2016).”
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, background): “Similarly, other works [109] insist on the importance of creating an API-like middleware that allows the DT to connect to external systems.”
Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions (2020, background): “• The analysis of the debate initiated in 2013 [40] and further explored in [54], whether DT covers the entire product lifecycle [62, 23] or just the product [66, 1].”
A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications (2019, background): “A DT may be part of a Cyber-Physical System [47], [48], which can be described as a set of physical entities (e.g. devices, objects, equipment, humans) that interact with a virtual cyberspace through a communication network.”