A framework for data-driven digital twins of smart manufacturing systems

Friederich, Jonas; Francis, Deena P.; Lazarova-Molnar, Sanja; Mohamed, Nader

doi:10.1016/j.compind.2021.103586

Cited by 92 publications

(28 citation statements)

References 55 publications

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“…In addition, data-driven digital twins will enable integrated ongoing model validation that can be started as soon as parts of the model are being extracted. The methodology presented in the current paper is compatible with the results and conclusions of the work [21]. • Most commercial simulators are not programming languages but rather graphical modelling systems.…”

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confidence: 61%

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A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

2022

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A generic well-defined methodology for the construction and operation of dynamic process models of discrete industrial systems following a number of well-defined steps is introduced. The sequence of steps for the application of the method as well as the necessary inputs, conditions, constraints and the results obtained are defined. The proposed methodology covers the classical offline modelling and simulation applications as well as their online counterpart, which use the physical system in the context of digital twins, with extensive data exchange and interaction with sensors, actuators and tools from other scientific fields as analytics and optimisation. The implemented process models can be used for what-if analysis, comparative evaluation of alternative scenarios and for the calculation of key performance indicators describing the behaviour of the physical systems under given conditions as well as for online monitoring, management and adjustment of the physical industrial system operations with respect to given rules and targets. An application of the proposed methodology in a discrete industrial system is presented, and interesting conclusions arise and are discussed. Finally, the open issues, limitations and future extensions of the research are considered.

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supporting

confidence: 61%

“…In [21], the authors claim and discuss the fact that manual simulation modelling is not an option with the modern manufacturing systems that undergo numerous and rapid reconfigurations during their lifetimes. For this reason, they propose a datadriven development of simulation models for smart manufacturing systems, as a basis for their digital twins.…”

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confidence: 99%

A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

2022

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“…Simulation of physical process on ad hoc or continuous basis process simulation [63], automated simulation model generation, [64] Digital model richness Robustness, resilience, self-adaption, fidelity of virtual model Robustness, resilience, self-adaption, fidelity [44], DT fidelity [53], fidelity [61], DT behaviour model [37], high-fidelity of DTs [64] Human interaction Bridging human and machine Human-machine collaboration [5], bridges a human user and robot [25] Product life-cycle Product design, manufacturing and service Service stage: service, data analytics [38], Full product life-cycle management [37,63,65], Manufacturing stage: fault prediction [3], predicting energy efficiency [37], predictive maintenance, feature extraction [30] Figure 16 shows contribution of ML-based DT in manufacturing PLM. ML-based DT is marginally used in full product life-cycle management [37,63,65].…”

Section: Simulation Capabilitiesmentioning

confidence: 99%

“…According to Figure 14, geometric, definition, and behaviour models were implemented following this theme in the period 2018-2019. Additionally, model improvement [43] and expert knowledge incorporation [64] were implemented, which are not stated as future research alternatives.…”

Section: Model-based Taskmentioning

confidence: 99%

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

2022

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The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.

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“…One example of a mathematically sound simplification method that can be used is model order reduction (MOR), see for instance [HHW18] for a corresponding discussion in the context of digital twins and [BGTQ + 21a, BGTQ + 21b, BGTQ + 21c] for a general overview. One may also rely on data-driven surrogate models obtained from measurement data [FFLMM22].…”

Section: Introduction: Numerical Simulation and Requirements For A Di...mentioning

confidence: 99%

Simulations in a Digital Twin of an Electrical Machine

Cherifi¹,

Mehrmann²,

Schulze³

2022

Preprint

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Digital twins have become popular for their ability to monitor and optimize a process or a machine during its lifetime using simulations and sensor data. In this paper, we focus on the challenge of the implementation of accurate and real time simulations for digital twins in the context of electrical machines.In general, this involves not only computational models for the electromagnetic aspects, but also mechanical and thermal effects need to be taken into account. We address mathematical tools that can be employed to carry out the required simulations based on physical laws as well as surrogate or data-driven models. One of those tools is a model hierarchy of very fine to very course models as well model reduction which is required for obtaining real-time simulations.We discuss in detail the coupling of electromagnetic, mechanical, and thermal models of an electrical machine to obtain a simulation model which is able to describe the interaction of those different physical components. In this context, a very promising setting is provided by energy-based formulations within the port-Hamiltonian framework, which has received much attention in the past years, especially in the context of multiphysics modeling. We present such port-Hamiltonian formulations for the considered electromagnetic, mechanical, and thermal models as well as for the coupled overall system.

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A framework for data-driven digital twins of smart manufacturing systems

Cited by 92 publications

References 55 publications

A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

A Discrete Process Modelling and Simulation Methodology for Industrial Systems within the Concept of Digital Twins

Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview

Simulations in a Digital Twin of an Electrical Machine

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