Investigation of the Time-invariance and Causality of a Machine Tool for Performing Operational Modal Analysis

Pütz, Matthias; Wittstock, Volker; Kolouch, Martin; Berthold, Jan

doi:10.1016/j.procir.2016.04.052

Cited by 7 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[7] EMA is a costly process requiring specialised equipment and machine downtime, therefore it is not usually performed on every machine. Recent research has focused on identifying machine properties during operation [8][9][10]. These approaches aim to use cutting forces to replace the external excitation, however they still require targeted experimentation and thus a loss of production time.…”

Section: Experimental Identification and Data-driven Approachesmentioning

confidence: 99%

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Barton

Fleischer²

2020

Lecture Notes in Production Engineering

View full text Add to dashboard Cite

Vibrations have a significant influence on quality and costs in metal cutting processes. Existing methods for predicting vibrations in machine tools enable an informed choice of process settings, however they rely on costly equipment and specialised staff. Therefore, this contribution proposes to reduce the modelling effort required by using machine learning based on data gathered during production. The approach relies on two sub-models, representing the machine structure and machining process respectively. A method is proposed for initialising and updating the models in production.

show abstract

Section: Experimental Identification and Data-driven Approachesmentioning

confidence: 99%

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Barton

Fleischer²

2020

Lecture Notes in Production Engineering

View full text Add to dashboard Cite

show abstract

Partition of the workspace for machine tool based on position-dependent modal energy distribution and clustering algorithm

Peng

Mao

et al. 2020

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Identification of natural frequencies of machine tools during milling: comparison of the experimental modal analysis and the operational modal analysis

Berthold,

Kolouch,

Regel

et al. 2024

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

Modal parameters (natural frequencies, mode shapes and modal damping) help to understand the dynamic behaviour of complex systems like machine tools. There are several approaches for finding the modal parameters. The Experimental Modal Analysis (EMA) has proven to be effective at standstill of a machine tool. The excitation, realized with impulse hammer or shaker, and excited responses at several locations are measured. Alternatively, the Operational Modal Analysis (OMA) can be deployed for finding the modal parameters during operation. Here, responses to excitation resulting from operation are only measured. The modal parameters are mathematically identified from the measured signals in both cases but with different methods. This paper discusses, to what extent both approaches (EMA and OMA) can lead to plausible identification of natural frequencies of a machine tool during milling. Concerning the EMA, attention is paid to capturing the excitation. Process forces can be assumed to be the most significant excitation. However, there are other excitation sources beside the process forces (e.g. drives, hydraulic and pneumatic aggregates), which are considered by this assumption to be a part of disturbances with consequence for the identification of the modal parameters. Regarding the OMA, attention is paid to the fact that the excitation is assumed to be broadband like the white noise. Unfortunately, this assumption does not match the characteristics of a real excitation. This paper contains the identification of natural frequencies of a machine tool during milling within both approaches. The achieved results are compared and discussed.

show abstract

Investigation of the Time-invariance and Causality of a Machine Tool for Performing Operational Modal Analysis

Cited by 7 publications

References 5 publications

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Partition of the workspace for machine tool based on position-dependent modal energy distribution and clustering algorithm

Identification of natural frequencies of machine tools during milling: comparison of the experimental modal analysis and the operational modal analysis

Contact Info

Product

Resources

About