Browsing by Author "Kneifl, Jonas"

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Open Access
An improved development process of production plants using digital twins with extended dynamic behaviour in virtual commissioning and control : Simulation@Operations
(2023) Pfeifer, Denis; Scheid, Jonas; Kneifl, Jonas; Fehr, Jörg
The challenges in automation system development are driven by short development cycles and individualization along with resource‐constraints. State of the art solutions do not provide the necessary digital tools to apply model‐based methods in automation engineering to achieve higher performing systems. To overcome these issues this paper presents a novel approach to address some of the current challenges in automation systems development using digital twins with extended dynamic behaviour. The study underscores how dynamic models can be imported through standardised interfaces into virtual commissioning (VC) tools, improving the development process by effectively utilising domain‐specific expertise. The paper highlights how these digital twins enhance not only the VC process but can also be applied to model‐based control methods. Initial experiments showcase the utility of digital twins in calculating dynamic acceleration limits during trajectory planning of CNC control and enhancing feed‐forward control. Further, the importance of parameter identification in achieving accurate system models is stressed. The initial results are promising, and future work aims to combine these methods in an industrial application involving a newly developed, individual lightweight robot, demonstrating the potential for enhanced design, accelerated development, and resource efficiency in automation systems.
Open Access
Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction
(2023) Kneifl, Jonas; Rosin, David; Avci, Okan; Röhrle, Oliver; Fehr, Jörg
Over the last decades, computer modeling has evolved from a supporting tool for engineering prototype design to an ubiquitous instrument in non-traditional fields such as medical rehabilitation. This area comes with unique challenges, e.g. the complex modeling of soft tissue or the analysis of musculoskeletal systems. Conventional modeling approaches like the finite element (FE) method are computationally costly when dealing with such models, limiting their usability for real-time simulation or deployment on low-end hardware, if the model at hand cannot be simplified without losing its expressiveness. Non-traditional approaches such as surrogate modeling using data-driven model order reduction are used to make complex high-fidelity models more widely available regardless. They often involve a dimensionality reduction step, in which the high-dimensional system state is transformed onto a low-dimensional subspace or manifold, and a regression approach to capture the reduced system behavior. While most publications focus on one dimensionality reduction, such as principal component analysis (PCA) (linear) or autoencoder (nonlinear), we consider and compare PCA, kernel PCA, autoencoders, as well as variational autoencoders for the approximation of a continuum-mechanical system. In detail, we demonstrate the benefits of the surrogate modeling approach on a complex musculoskeletal system of a human upper-arm with severe nonlinearities and physiological geometry. We consider both, the model’s deformation and the internal stress as the two main quantities of interest in a FE context. By doing so we are able to create computationally low-cost surrogate models which capture the system behavior with high approximation quality and fast evaluations.
Open Access
A non-intrusive nonlinear model reduction method for structural dynamical problems based on machine learning
(2020) Kneifl, Jonas; Grunert, Dennis; Fehr, Jörg
The paper uses a nonlinear non-intrusive model reduction approach, to derive efficient and accurate surrogate models for structural dynamical problems. Therefore, a combination of proper orthogonal decomposition along with regression algorithms from the field of machine learning is utilized to capture the dynamics in a reduced representation. This allows highly performant approximations of the original system. In this context, we provide a comparison of several regression algorithms based on crash simulations of a structural dynamic frame.