本文選題：磨削顫振　切入點(diǎn)：時(shí)變信號(hào)　出處：《浙江理工大學(xué)》2017年碩士論文

【摘要】：磨削加工是現(xiàn)代機(jī)械制造業(yè)中不可或缺的一種用來(lái)獲取高精度、低粗糙度的零件加工表面的工藝方法。對(duì)磨床進(jìn)行狀態(tài)實(shí)時(shí)監(jiān)測(cè)和故障識(shí)別診斷來(lái)確保磨床長(zhǎng)期穩(wěn)定可靠運(yùn)行具有有重大現(xiàn)實(shí)意義和產(chǎn)業(yè)價(jià)值。需要注意的是,在加工過(guò)程中,磨床會(huì)進(jìn)入顫振的狀態(tài),從而引發(fā)一系列負(fù)面影響。因此可靠的顫振監(jiān)測(cè)和識(shí)別技術(shù)是必不可少的,以實(shí)現(xiàn)磨床振動(dòng)狀態(tài)的實(shí)時(shí)監(jiān)測(cè)。以傅里葉變換為理論基礎(chǔ)的傳統(tǒng)時(shí)頻信號(hào)處理方法不適用于非線性、非平穩(wěn)和多維的磨床振動(dòng)輸出信號(hào)。二維經(jīng)驗(yàn)?zāi)B(tài)分解(Bivariate Empirical Mode Decomposition,BEMD)擴(kuò)展了EMD的能力,能將二維復(fù)值信號(hào)分解為一系列零均值的旋轉(zhuǎn)成分。BEMD不僅能描述非線性動(dòng)力學(xué)行為,而且能節(jié)約計(jì)算時(shí)間,并移除計(jì)算中由于假設(shè)和人為原因產(chǎn)生的失真。其在檢測(cè)初始故障方面表現(xiàn)出更強(qiáng)的能力,能有效地分析并提取非平穩(wěn)、非線性磨床顫振信號(hào)特征。本文以KD4020X16數(shù)控龍門導(dǎo)軌磨床為研究對(duì)象,根據(jù)磨床自身的動(dòng)靜態(tài)特性搭建了顫振檢測(cè)試驗(yàn)平臺(tái),進(jìn)行了磨削參數(shù)多水平試驗(yàn)。利用IEPE壓電加速度傳感器和配套的TST5912動(dòng)態(tài)信號(hào)分析儀對(duì)振動(dòng)信號(hào)進(jìn)行采集和保存,得到不同磨削參數(shù)設(shè)定下的80組實(shí)驗(yàn)樣本數(shù)據(jù),其中包括45組平穩(wěn)磨削振動(dòng)信號(hào)和35組顫振磨削信號(hào)。本論文對(duì)實(shí)驗(yàn)過(guò)程中采集到的砂輪主軸X和Z方向的振動(dòng)信號(hào)進(jìn)行信號(hào)重構(gòu),進(jìn)行BEMD處理得到多階BIMF分量;利用基于相關(guān)系數(shù)的真實(shí)固有模態(tài)函數(shù)提取準(zhǔn)則篩選出真實(shí)BIMF;提取出對(duì)顫振信號(hào)敏感的指標(biāo)量—峰峰值、實(shí)時(shí)方差、峭度以及瞬時(shí)能量,分別進(jìn)行求和與歸一化處理形成顫振特征向量;最后以最小二乘支持向量機(jī)作為(Least Square Support Vector Machine,LSSVM)智能化模式分類器對(duì)隨機(jī)選取的55組樣本數(shù)據(jù)的特征量進(jìn)行訓(xùn)練,得到顫振檢測(cè)識(shí)別模型,以剩下的25組樣本數(shù)據(jù)作為檢驗(yàn)樣本,對(duì)識(shí)別模型進(jìn)行檢驗(yàn)和判斷,驗(yàn)證其準(zhǔn)確率及可行性。證明了基于BEMD與LSSVM的方法具有較好的識(shí)別率。通過(guò)上述方法,建立了大型數(shù)控磨床磨削顫振檢測(cè)軟件,驗(yàn)證了其實(shí)時(shí)監(jiān)測(cè)磨床振動(dòng)狀態(tài)的可行性。
[Abstract]:Grinding is an indispensable method in modern mechanical manufacturing industry to obtain high precision. Process method for machining surface of parts with low roughness. It is of great practical significance and industrial value to ensure the long-term stable and reliable operation of grinding machine by real-time monitoring and fault identification diagnosis of grinding machine. Grinding machines enter a flutter state, causing a series of negative effects. Reliable flutter monitoring and identification techniques are therefore essential. In order to realize the real-time monitoring of grinding machine vibration, the traditional time-frequency signal processing method based on Fourier transform theory is not suitable for nonlinear. The two-dimensional empirical mode decomposition extends the ability of EMD to decompose the two-dimensional complex signal into a series of rotating components with zero mean value. BEMD can not only describe the nonlinear dynamic behavior. Moreover, it can save calculation time and remove the distortion caused by assumptions and human causes. It has a stronger ability to detect initial faults and can effectively analyze and extract non-stationary. Based on the dynamic and static characteristics of the KD4020X16 CNC gantry guideway grinder, a flutter detection test platform is built in this paper, which is based on the dynamic and static characteristics of the grinder itself. The vibration signals were collected and saved by IEPE piezoelectric accelerometer and TST5912 dynamic signal analyzer, and 80 sets of experimental data were obtained under different grinding parameters. This paper reconstructs the vibration signals in X and Z directions of the grinding wheel spindle collected during the experiment, and obtains the multi-order BIMF component by BEMD processing, which includes 45 sets of stationary grinding vibration signals and 35 sets of chatter grinding signals. The real BIMF is selected by using the real inherent mode function extraction criterion based on correlation coefficient, and the peak peak value, real time variance, kurtosis and instantaneous energy sensitive to flutter signal are extracted. Finally, the least square support vector machine is used as the intelligent pattern classifier for least Square Support Vector machine to train the characteristic quantity of 55 groups of randomly selected sample data. The flutter detection and identification model is obtained. The remaining 25 sets of sample data are used as test samples to test and judge the identification model. The accuracy and feasibility of the method are verified. The method based on BEMD and LSSVM has a good recognition rate. Through the above method, a large NC grinding machine grinding chatter detection software is established, and the feasibility of real-time monitoring the vibration state of grinding machine is verified.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG580.6

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