Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Barton, David K.; Fleischer, Jürgen

doi:10.1007/978-3-662-62138-7_55

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…It can be shown that modal parameters are dependent on the axis positions of machine tools [13]. However, this is often not considered or not investigated in proposed methods [7,8,10,14,18].…”

Section: Review Of the State-of-the-art Methodsmentioning

confidence: 99%

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Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Oexle,

Heimberger,

Puchta

et al. 2024

Machines

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The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.

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Section: Review Of the State-of-the-art Methodsmentioning

confidence: 99%

“…Approaches that use machine learning technologies show good results in modeling a system's dynamic behavior. However, they require extensive training data and are fixed to use cases they are trained for [18,19].…”

Section: Review Of the State-of-the-art Methodsmentioning

confidence: 99%

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Oexle,

Heimberger,

Puchta

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…Recent advances in data‐driven approaches have demonstrated the advantages of resorting to comprehensive modeling, analysis, and prediction of dynamical systems solely based on available data 22 . Barton and Fleischer 23 presented the concept of predicting the vibrations in machine tools using machine learning (ML). Their idea was based on dividing the machining operation into two submodels, that is, process and machine models.…”

Section: Introductionmentioning

confidence: 99%

Tool‐tip vibration prediction based on a novel convolutional enhanced transformer

Shehzad,

Rui,

Ding

et al. 2024

Int Journal of Mech Sys Dyn

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Superior surface finish remains a fundamental criterion in precision machining operations, and tool‐tip vibration is an important factor that significantly influences the quality of the machined surface. Physics‐based models heavily rely on assumptions for model simplification when applied to complex high‐end systems. However, these assumptions may come at the cost of compromising the model's accuracy. In contrast, data‐driven techniques have emerged as an attractive alternative for tasks such as prediction and complex system analysis. To exploit the advantages of data‐driven models, this study introduces a novel convolutional enhanced transformer model for tool‐tip vibration prediction, referred to as CeT‐TV. The effectiveness of this model is demonstrated through its successful application in ultra‐precision fly‐cutting (UPFC) operations. Two distinct variants of the model, namely, guided and nonguided CeT‐TV, were developed and rigorously tested on a data set custom‐tailored for UPFC applications. The results reveal that the guided CeT‐TV model exhibits outstanding performance, characterized by the lowest mean absolute error and root mean square error values. Additionally, the model demonstrates excellent agreement between the predicted values and the actual measurements, thus underlining its efficiency and potential for predicting the tool‐tip vibration in the context of UPFC.

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Auf dem Weg zur autonomen Maschine

Oexle

Fleischer

2023

Zeitschrift Für Wirtschaftlichen Fabrikbetrieb

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Im Kontext von Industrie 4.0 werden die Funktionen von Produktionsmaschinen durch digitale Services immer umfangreicher. Unter anderem steht dabei die Vorhersage des Maschinenverhaltens im Fokus. Zur Abbildung des maschinendynamischen Verhaltens kommen aktuell beispielsweise die Finite-Elemente-Methode oder Mehrkörpersimulationen zum Einsatz. Jedoch gibt es bisher keine Lösung, die eine Maschine lebensbegleitend und individuell digital abbildet. Folgend wird hierfür ein neuer Ansatz vorgestellt.

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Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Cited by 4 publications

References 23 publications

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

Tool‐tip vibration prediction based on a novel convolutional enhanced transformer

Auf dem Weg zur autonomen Maschine

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