Abstract
The states of the machine tool, such as the components’ position and the spindle speed, play leading roles in the change of dynamic parameters. However, the traditional modal analysis method that modal parameters manually identified from vibration signal is greatly interfered by harmonics, and the process of eliminating interference is very inefficient and subjective. At present, there is a lack of a standard and efficient method to characterize modal parameter changes in different states of machine tools. This paper proposes a new machine tool modal classification analysis method based on clustering. The characteristics related to the modal parameters are extracted from the response signal in different states, and the clustering results are used to reflect the changes of machine tool modal parameters. After the amplitude of the frequency response function is normalized, the characteristics related to the natural frequency are acquired, and the clustering results further reflect the difference of the natural frequency of the signal. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.
Highlights
The structural vibration characteristics of the machine tool greatly affect the machining accuracy and efficiency.[1,2,3] The scholars Jorgensen and Shin[1] found that the dynamic characteristics of the machine tool under the working condition will change due to the change of machine states, such as the working position of the table and the spindle speed
The current research methods of machine tool dynamics are mainly based on experimental modal analysis methods, which mainly include the hammering method and the OMA (Operational Modal Analysis method) method.[4,5,6]
The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools
Summary
The structural vibration characteristics of the machine tool greatly affect the machining accuracy and efficiency.[1,2,3] The scholars Jorgensen and Shin[1] found that the dynamic characteristics of the machine tool under the working condition will change due to the change of machine states, such as the working position of the table and the spindle speed. This paper, based on clustering, proposes a new modal classification analysis method, which can effectively show the influence that the state factors have on modal parameters of the machine tool. The new method based on clustering can be a standard and efficient method to characterize modal parameter changes in different states of machine tools.
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