TWINKLE: A digital-twin-building kernel for real-time computer-aided engineering

Zambrano, V.; Rodríguez-Barrachina, R.; Calvo, S.; Izquierdo, Salvador

doi:10.1016/j.softx.2020.100419

Cited by 13 publications

(12 citation statements)

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“…A novel numerical methodology for the assessment of the micro-textured wall effects on rubber injection moulding is presented in this paper. The methodology is fully based on two open-sources tools: (a) OpenFOAM, a standard open-source CFD platform [ 10 ]; and (b) Twinkle a reduced order model builder library [ 28 , 29 ]. As a result, a new and advanced OpenFOAM solver, was developed to calculate the rubber flow during rubber injection on textured moulds, where the effective viscosity at the wall corresponding to the textured surface is introduced by means of a reduced order model.…”

Section: Discussionmentioning

confidence: 99%

“…An in-house developed ROM-generation algorithm based on Canonical Polyadic Decomposition (CPD) of tensors [ 28 , 29 ] is used in this work. It is able to transform a function of N not necessarily independent variables into the product of N one-dimensional functions.…”

Section: Methodsmentioning

confidence: 99%

“…This text file contains the values of the functions and weighting factors of Equation ( 32 ). The new OpenFOAM solver ( ) reads this file and calls a function ( ), available at the ROM builder library [ 28 , 29 ], which interprets the data and evaluates Equation ( 32 ) to predict the viscous shear tensor at the textured wall.

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confidence: 99%

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Numerical Approach for the Assessment of Micro-Textured Walls Effects on Rubber Injection Moulding

et al. 2021

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Micro-surface texturing of elastomeric seals is a validated method to improve the friction and wear characteristics of the seals. In this study, the injection process of high-viscosity elastomeric materials in moulds with wall microprotusions is evaluated. To this end, a novel CFD methodology is developed and implemented in OpenFOAM to address rubber flow behaviour at both microscale and macroscale. The first approach allows analyzing the flow perturbation induced by a particular surface texture and generate results to calculate an equivalent wall shear stress that is introduced into the macroscale case through reduced order modelling. The methodology is applied to simulate rubber injection in textured moulds in an academic case (straight pipe) and a real case (D-ring seal mould). In both cases, it is shown that textured walls do not increase the injection pressure and therefore the manufacturing process is not adversely affected.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Numerical Approach for the Assessment of Micro-Textured Walls Effects on Rubber Injection Moulding

et al. 2021

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“…ML models, such as ANN [ 155 ], and probabilistic modeling methods, such as the Gaussian process [ 156 , 157 , 159 ], could likewise be adopted to develop a surrogate model and implemented in a control context [ 157 , 158 , 160 ]. Alternatively, model order reduction techniques can transfer highly detailed and complex simulation models to other domain and life cycle phase, e.g., building efficient finite element model for dynamic structural analysis through reducing the degree of freedom, while maintaining required accuracies and predictability [ 161 , 162 , 163 ].…”

Section: Sustainable Resilient Manufacturingmentioning

confidence: 99%

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Huang

Shen²,

et al. 2021

Sensors

194

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Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

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“…Since the gradient is not known in advance, the samples from which the surrogate model is constructed are iteratively removed to estimate the area where adding a new sample would have a greater impact. Canonical polyadic decomposition is performed to built the surrogate models, using TWINKLE library [4]. To avoid local oversampling, gradient-based function is compensated with a distance function, whose score is maximum in between sample points.…”

mentioning

confidence: 99%

Tensor-Decomposition-Based Sequential Design of Experiments for Computer Simulations

Alfaro-Isac¹,

Juan-Alejandre²,

Izquierdo³

2021

10th International Conference on Adaptative Modeling and Simulation

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High-fidelity simulations aim at numerically replicating complex physical systems, usually involving several phenomena. Despite recent advances on digital computing, these simulations might take hours, even days to complete. To avoid performing a computationally expensive simulation for each case, surrogate models are used instead. A surrogate model links the inputs and outcomes of a set of computer experiments to approximate the response surface of a high fidelity model [1].

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TWINKLE: A digital-twin-building kernel for real-time computer-aided engineering

Cited by 13 publications

References 13 publications

Numerical Approach for the Assessment of Micro-Textured Walls Effects on Rubber Injection Moulding

Numerical Approach for the Assessment of Micro-Textured Walls Effects on Rubber Injection Moulding

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Tensor-Decomposition-Based Sequential Design of Experiments for Computer Simulations

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