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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Modal identification of a machine tool structure during machining operations
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Modal identification of a machine tool structure during machining operations

机译:机加工操作期间机床结构的模态识别

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摘要

The identification of modal parameters of a machine tool structure, in service, is important to ensure stability and productivity during machining operations. The characterization can be carried out through an Operational Modal Analysis (OMA). However, in the presence of strong harmonic excitation, the application of OMA is not straightforward. To overcome this difficulty, the Transmissibility Function-Based (TFB) method is proposed. The major advantage of this approach is its independence from the excitation nature and its ability to separate structural poles from spurious ones. The main novelty of this paper lies in the investigation of the TFB approach to identify the modal properties of a machine tool, during machining operations. Identified modal model through an Experimental Modal Analysis (EMA) of the considered machine tool, at rest, presents our reference modal base to validate results obtained through the TFB approach. For a comparison purpose, the modified Enhanced Frequency Domain Decomposition (EFDD) method is also investigated. Both methods enable the identification of the modal properties under operational conditions, with a clear advantage to the TFB approach due to its ability to eliminate all of the preponderant harmonic components from the measured data without the need of any additional selection criteria. The TFB method is thus a reliable technique for the identification of modal parameters of a machine tool in operational conditions.
机译:在服务中识别机床结构的模态参数对于确保加工操作期间的稳定性和生产率是重要的。表征可以通过操作模态分析(OMA)进行。然而,在存在强烈的谐波激发的情况下,OMA的应用并不简单。为了克服这种困难,提出了一种基于透射功能的(TFB)方法。这种方法的主要优势是它独立于激发性质及其将结构极与杂散的能力不同。本文的主要新颖性在于在加工操作期间调查TFB方法来确定机床的模态性能。通过考虑机床的实验模态分析(EMA)静止,静置的模型模型呈现了我们参考模态基础,以验证通过TFB方法获得的结果。为了进行比较目的,还研究了改进的增强频域分解(EFDD)方法。两种方法都能够在运行条件下识别模态性质,并且由于其能够消除来自测量数据的所有优势谐波分量而不需要任何额外的选择标准,因此可以清楚地利用TFB方法。因此,TFB方法是一种可靠的技术,用于识别操作条件中的机床的模态参数。

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