【摘要】：機床的加工性能與其動態(tài)特性緊密相關(guān),隨著機床裝備朝高速高精高可靠性方向發(fā)展,有關(guān)機床動態(tài)特性的研究也越來越重要。實驗模態(tài)分析方法是研究靜態(tài)下結(jié)構(gòu)動態(tài)特性的主要手段。然而機床在加工狀態(tài)與靜止狀態(tài)下的動態(tài)特性有很大區(qū)別,傳統(tǒng)的實驗模態(tài)分析識別的結(jié)果難以準確地表征機床工況時的動態(tài)特性。工作模態(tài)分析方法OMA (Operational or Output-only Modal Analysis)是一種僅需要測量響應來識別結(jié)構(gòu)在工況下動態(tài)特性的新方法,因其簡單實用,前景廣闊而備受關(guān)注。然而,工作模態(tài)分析理論本身并不完善,例如由于不測輸入無法估計頻響函數(shù),模態(tài)參數(shù)的識別依賴人工經(jīng)驗；同時,當OMA用于機床等機械結(jié)構(gòu)時會帶來一些新問題：首先,工作模態(tài)分析要求激勵信號為白噪聲信號,在頻域上均勻分布,這對于機床等機械結(jié)構(gòu)難以實現(xiàn)；其次,對于機床等旋轉(zhuǎn)機械,其響應通常包含周期信號,導致模態(tài)參數(shù)識別受到諧頻的嚴重干擾從而難以區(qū)分固有頻率和諧頻頻率。本文旨在針對上述問題,在激勵方法,信號處理,工作模態(tài)分析理論及模態(tài)參數(shù)自動識別算法方面做一些研究和探索,力圖提出一套面向機床等機械結(jié)構(gòu)的僅基于響應的模態(tài)分析解決方案。 首先,結(jié)合隨機信號和脈沖信號的特點,采用隨機脈沖激勵信號,提出了多種切削激勵方法,并實現(xiàn)機床結(jié)構(gòu)動態(tài)特性與切削過程的解耦。建立了隨機脈沖激勵信號的數(shù)學模型,并通過該數(shù)學模型和仿真分析闡明了激勵頻帶和能量與脈沖參數(shù)、切削參數(shù)的關(guān)系,總結(jié)了該信號的激勵規(guī)律和特性,為實現(xiàn)激勵的可控可調(diào)提供理論基礎。分別從運動方式和切削工件兩方面入手,提出主軸隨機轉(zhuǎn)動切削、隨機多凸臺、隨機(曲線和折線)單凸臺、漸變寬度凸臺和凸點切削激勵方法,從激勵方法上對機床結(jié)構(gòu)動態(tài)特性與切削過程解耦。 其次,提出了利用切削參數(shù)、刀具幾何參數(shù)、工件材料特性及切削力系數(shù)等已知信息,預測切削激勵力從而代替測量,并結(jié)合工作模態(tài)分析實測的振動響應信號,估計頻響函數(shù)的新方法,彌補了OMA不測輸入帶來的缺憾。其中,為了考量轉(zhuǎn)速對切削力的影響,通過對瞬時切削過程的功能分析,提出轉(zhuǎn)速影響因子以改進預測切削力的瞬時剛性力模型。 第三,提出了識別工作模態(tài)分析中可能產(chǎn)生的偽模態(tài)(Spurious mode,即虛假模態(tài))的方法。首先,根據(jù)產(chǎn)生的原因和表現(xiàn)出的特性,對可能的偽模態(tài)進行分類。然后,根據(jù)各類偽模態(tài)的特性和差異,提出了對應的識別方法。對于最難處理的諧頻模態(tài)(Harmonic mode),本文提出了頻率柵欄縫和譜突變度兩種新工具,實現(xiàn)即使在不知諧頻成分的復雜工況下也能識別諧頻模態(tài)。 最后,對模態(tài)參數(shù)自動識別的兩個關(guān)鍵問題,極點自動歸組和從各組極點中自動識別各階模態(tài)參數(shù),進行了探討。根據(jù)物理極點比較集中,而計算極點較為分散的特性,提出極點分布準則以用于清除計算模態(tài)。提出了基于最小方差的模態(tài)參數(shù)自動識別算法,并與偽模態(tài)識別算法、極點分布準則以及極點容差率準則整合,在Matlab上實現(xiàn)了適用于復雜工況的只基于響應的模態(tài)參數(shù)自動識別算法Auto-LSCE。 總體而言,本文提出了一種利用切削力激勵機床而僅利用響應信號進行參數(shù)識別的模態(tài)分析方法,解決了OMA分析理論中頻響函數(shù)無法估計、模態(tài)參數(shù)自動辨識及OMA用于機床等結(jié)構(gòu)時的激勵、偽模態(tài)識別等科學及技術(shù)難題。本文所取得的研究成果對推動OMA分析方法在機械工程領域的研究與推廣具有積極意義。
[Abstract]:The machining performance of the machine tool is closely related to its dynamic characteristics. With the development of machine tool equipment toward high speed and high precision, the research on the dynamic characteristics of the machine tool is becoming more and more important. The experimental modal analysis method is the main means to study the dynamic characteristics of static structure. However, the dynamic characteristics of the machine tool in the machining state and the static state are very different, and the traditional experimental modal analysis and recognition results are difficult to accurately characterize the dynamic characteristics of the machine tool working condition. Working mode analysis method OMA (Operational or Function-only Modal Analysis) is a new method which only needs measurement response to identify dynamic characteristics of structure under working condition. However, the theory of modal analysis is not perfect, for example, because the input cannot estimate the frequency response function, the identification of modal parameters depends on the artificial experience; meanwhile, when OMA is used for mechanical structures such as machine tools, some new problems are brought forward: firstly, the working mode analysis requires that the excitation signal be a white noise signal, which is uniformly distributed in the frequency domain, which is difficult to achieve for mechanical structures such as machine tools; secondly, for rotating machines such as machine tools, the response typically comprises a periodic signal, It is difficult to distinguish the harmonic frequency of the natural frequency by causing the modal parameter to recognize the severe interference of harmonic frequency. In this paper, aiming at the above problems, some research and exploration are made on the excitation method, signal processing, working mode analysis theory and modal parameter automatic recognition algorithm. First of all, combining the characteristics of the random signal and the pulse signal, the random pulse excitation signal is adopted, a plurality of cutting excitation methods are proposed, and the dynamic characteristics and the cutting process of the machine tool structure are realized. The mathematical model of the random pulse excitation signal is established, and the relationship between excitation frequency band and energy and pulse parameters and cutting parameters is clarified by the mathematical model and simulation analysis. The excitation law and characteristic of the signal are summarized. On the basis of the two aspects of moving mode and cutting workpiece, this paper proposes random rotation cutting of main shaft, random multi-convex table, random (curve and broken line) single convex table, gradual width convex table and convex point cutting excitation method, and the dynamic characteristic and cutting process of machine tool structure are analyzed from the excitation method. Secondly, using the known information such as cutting parameters, tool geometry parameters, workpiece material properties and cutting force coefficients, the cutting excitation force is predicted to replace the measurement, and the measured vibration response signal is analyzed in combination with the working mode, and the frequency response function is estimated. The new method of number has made up for OMA's unmeasured input. In order to consider the effect of rotating speed on cutting force, the influence of rotating speed on cutting force is analyzed. By analyzing the function of instantaneous cutting process, the influence factor of rotating speed is put forward to improve the instantaneous cutting force. Thirdly, the pseudo-mode which may be generated in the modal analysis of the identification is presented. A method of making spurious modes. First, according to the causes and characteristics of the generation, it is possible to The pseudo-mode is classified. Then, based on the characteristics and differences of various pseudo-modes, it is proposed that In this paper, two new tools for frequency fence joint and spectral mutation are proposed for the most difficult harmonic mode (harmonic mode). It is also possible to identify the harmonic frequency mode. Finally, two key problems of automatic identification of modal parameters, the pole auto-return group and the automatic identification of each group from the poles of each group are identified. In this paper, the modal parameters of the order are discussed. According to the physical pole comparison set, the characteristic of the extreme dispersion of the poles is calculated and the pole distribution is put forward. In this paper, a modal parameter automatic recognition algorithm based on minimum variance is proposed, which is combined with pseudo-modal identification algorithm, pole distribution criterion and pole tolerance criterion. The algorithm Auto-LSCE. In general, this paper presents a modal analysis method which uses cutting force to excite the machine tool and uses the response signal only to identify the parameters, which solves the problem that the frequency response function in OMA analysis theory can't be estimated, the modal parameter can be recognized automatically. The excitation of OMA for machine tools and other structures The result of this paper is to push the OMA analysis method in mechanical engineering.
【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TG506
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本文編號：2291786