Modelling for Digital Twins—Potential Role of Surrogate Models

Bárkányi, Ágnes; Chován, Tibor; Németh, Sándor; Abonyi, János

doi:10.3390/pr9030476

Cited by 62 publications

(28 citation statements)

References 104 publications

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“…The first approach is a bottom-up approach. The corner-stones of this approach are simplified models of atomic components, frequently called surrogate models [58] for individual components. An example of this bottom-up approach is the method AML2SIM, which is intended for assembling simulation models from atomic components [59] in the object-oriented manner within the signal-oriented environment of MATLAB-Simulink.…”

Section: Assignment Of Durations To Production Operations To Get More...mentioning

confidence: 99%

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Novák

Vyskočil

2022

Processes

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Smart production systems conforming the Industry 4.0 vision are based on subsystems that are integrated in a way that supports high flexibility and re-configurability. Specific components and devices, such as industrial and mobile robots or transport systems, now pose full-blown systems, and the entire Industry 4.0 production system constitutes a system-of-systems. Testing, fine-tuning, and production planning are important tasks in the entire engineering production system life-cycle. All these steps can be significantly supported and improved by digital twins, which are digitalized replicas of physical systems that are synchronized with the real systems at runtime. However, the design and implementation of digital twins for such integrated, yet partly stand-alone, industrial sub-systems can represent challenging and significantly time-consuming engineering tasks. In this article, the problem of the digital twin design for discrete-event production systems is addressed. The article also proposes to utilize a formal description of production resources and related production operations that the resources can perform. An executable version of such formalization can be automatically derived into a form of a digital twin. Such a derived digital twin can be enhanced with operation duration times that are obtained with process mining methods, leading to more realistic simulations for the entire production system. The proposed solution was successfully tested and validated in the Industry 4.0 Testbed, equipped with four robots and a transport system, which is utilized as a use-case in this article.

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Section: Assignment Of Durations To Production Operations To Get More...mentioning

confidence: 99%

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Novák

Vyskočil

2022

Processes

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“…In the article "Modelling for Digital Twins -Potential Role of Surrogate Models", Bárkányi et al [300] coins the term Physics-driven surrogate models (PDSM) for surrogate generated from a large amount of data got from a high-fidelity physical model and enumerates several advantages and applications of DT with surrogate models. First, after training, PDSM is much faster than traditional first-principle simulations.…”

Section: Vibroacoustic Product Design By Physics-driven Surrogatesmentioning

confidence: 99%

“…Unlike a datadriven model, PDSM is less susceptible to different bias sources, and its uncertainties can be related to the physical model, so they can be estimated and bounded. As for drawbacks of the PDSM, Bárkányi et al [300] mentions the lack of interpretability, inability to extrapolate the prediction to unseen scenarios, and the difficulty of assimilating long-term historical data. However, some built-in or implemented methods can increase the interpretability of ML, and Adaptive Sampling can deal with extrapolation when the prediction uncertainty is high.…”

Section: Sampling With Highmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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“…Digital twins can play a key role in combining the related physical and virtual entities into an efficient cyber-physical production system [27]. The novel methodological approach pursues to serve as a basis framework to develop digital twin models for improving the overall performance in manufacturing systems together with simulation techniques, data analysis, and artificial intelligence, as well as machine learning and blockchain technology.…”

Section: Introductionmentioning

confidence: 99%

“…The novel methodological approach pursues to serve as a basis framework to develop digital twin models for improving the overall performance in manufacturing systems together with simulation techniques, data analysis, and artificial intelligence, as well as machine learning and blockchain technology. The digital twin concept applied to manufacturing systems can enable real-time monitoring and control, higher efficiency and safety, the effective use of predictive maintenance, and the simulation of scenarios and risk assessment for better decision making [27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Methodology for Assessing and Modeling Manufacturing Processes

Reschke

Gallego-García

2021

Applied Sciences

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Historically, researchers and practitioners have often failed to consider all the areas, factors, and implications of a process within an integrated manufacturing model. Thus, the aim of this research was to propose a holistic approach to manufacturing processes in order to assess their status and performance to improve target indicators such as product quality. For this purpose, a conceptual model is designed by identifying areas, flows, and indicators that are relevant to the assessment of a manufacturing system. Moreover, using the conceptual model, manufacturing systems can be modeled considering all related flows and decision-making options in the respective areas of production, maintenance, and quality. As a result, this model serves as the basis for the integral management and control of manufacturing systems in digital twin models for the regulation of process stability and quality with maintenance strategies. Thus, an assessment based on the conceptual model improves the knowledge level of all elements involved in the manufacturing of a product according to the desired quality specifications. The continuous monitoring of all areas and flows together with the optimal strategies in the quality and maintenance areas can enable companies to increase their profitability and customer service level. In this context, the discussion section lists key decision aspects for the assessment and improvement of manufacturing systems, while also providing a methodological sequence to evaluate and improve manufacturing systems. In conclusion, the conceptual approach allows better decision making, ensuring continuous optimization along the manufacturing asset lifecycle and providing a unique selling proposition for equipment producers and service engineering suppliers, as well as for production and assembly companies.

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Modelling for Digital Twins—Potential Role of Surrogate Models

Cited by 62 publications

References 104 publications

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Novel Methodology for Assessing and Modeling Manufacturing Processes

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