Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis

机床 模态分析 机械加工 情态动词 工作模态分析 白噪声 工程类 数控 计算机科学 频域 噪音(视频) 算法 有限元法 机械工程 结构工程 人工智能 电信 化学 高分子化学 图像(数学) 计算机视觉
作者
Bin Li,Hui Cai,Xinyong Mao,Junbin Huang,Bo Luo
出处
期刊:International Journal of Machine Tools & Manufacture [Elsevier BV]
卷期号:71: 26-40 被引量:93
标识
DOI:10.1016/j.ijmachtools.2013.04.001
摘要

Abstract Dynamic properties of the whole machine tool structure including tool, spindle, and machine tool frame contribute greatly to the reliability of the machine tool in service and machining quality. However, they will change during operation compared with the results from static frequency response function measurements of classic experimental modal analysis. Therefore, an accurate estimation of the dynamic modal parameters of the whole structure is of great value in real time monitoring, active maintenance, and precise prediction of a stability lobes diagram. Operational modal analysis (OMA) developed from civil engineering works quite efficiently in modal parameters estimation of structure in operation under an intrinsic assumption of white noise excitation. This paper proposes a new methodology for applying this technique in the case of computer numerically controlled (CNC) machine tools during machining operations. A novel random excitation technique based on cutting is presented to meet the white noise excitation requirement. This technique is realized by interrupted cutting of a narrow workpiece step while spindle rotating randomly. The spindle rotation speed is automatically controlled by G-code part program, which contains a series of random speed values produced by MATLAB software following uniform distribution. The resulting cutting produces random pulses and excites the structure in all three directions. The effect of cutting parameters on the excitation frequency and energy was analyzed and simulated. The proposed technique was experimentally validated with two different OMA methods: the Stochastic Subspace Identification (SSI) method and the poly-reference least square complex frequency domain (pLSCF or PolyMAX) method, both of which came up with similar results. It was shown that the proposed excitation technique combined successfully with OMA methods to extract dynamic modal parameters of the machine tool structure.
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