本文選題：刀具磨損 + 聲發(fā)射信號(hào)��； 參考：《東北電力大學(xué)》2017年碩士論文

【摘要】：隨著裝備制造業(yè)的發(fā)展,刀具磨損狀態(tài)監(jiān)測(cè)技術(shù)已成為制約現(xiàn)代自動(dòng)化機(jī)床的一項(xiàng)關(guān)鍵技術(shù),該技術(shù)目前尚未得到有效解決。實(shí)時(shí)地監(jiān)測(cè)刀具狀態(tài),可提高零件加工質(zhì)量和機(jī)床的加工效率,減少機(jī)床事故的發(fā)生,最大限度地減少人對(duì)機(jī)床的操作,實(shí)現(xiàn)機(jī)床的智能化和無人化,保證系統(tǒng)在最優(yōu)參數(shù)下運(yùn)行。因此,刀具磨損狀態(tài)監(jiān)測(cè)技術(shù)的研究是非常迫切且重要的。本文針對(duì)不同切削條件下刀具磨損狀態(tài)監(jiān)測(cè)和磨損量預(yù)測(cè)研究課題,通過正交試驗(yàn)法安排切削試驗(yàn),在采集的聲發(fā)射信號(hào)的基礎(chǔ)上,應(yīng)用現(xiàn)代信號(hào)處理方法小波包分析和最優(yōu)熵理論相結(jié)合實(shí)現(xiàn)信號(hào)的濾波處理,提出了基于云模型理論和最小二乘支持向量機(jī)的刀具磨損狀態(tài)識(shí)別方法,最后應(yīng)用不確定性云推理方法實(shí)現(xiàn)磨損量的不確定性預(yù)測(cè)。主要研究?jī)?nèi)容由以下幾個(gè)部分構(gòu)成:以往的刀具磨損監(jiān)測(cè)信號(hào)濾波采用時(shí)域分析(經(jīng)驗(yàn)?zāi)B(tài)分解)、頻域分析(功率譜分析)等傳統(tǒng)的信號(hào)預(yù)處理方法。由于所采用的聲發(fā)射信號(hào)的非平穩(wěn)和非線性特點(diǎn),本文將適合處理非平穩(wěn)信號(hào)處理的小波包分析方法引入到信號(hào)預(yù)處理中,實(shí)現(xiàn)信號(hào)的濾波。首先通過頻譜分析得到不同磨損階段聲發(fā)射信號(hào)的頻帶分布范圍,作為小波包分解層次的定性參考;其次應(yīng)用信息熵理論中的Shannon熵表征噪聲的大小,確定小波包分解最佳樹;最后通過最佳樹統(tǒng)計(jì)分析確定小波包分解的最優(yōu)分枝,并通過閾值處理后進(jìn)行信號(hào)重構(gòu),信噪比可達(dá)35dB以上。提出了基于云理論的不確定性聲發(fā)射信號(hào)特征提取方法。首先通過改進(jìn)的逆向云算法提取不同磨損量聲發(fā)射信號(hào)的特征參數(shù),期望、熵和超熵;其次,定量分析刀具在不同切削條件下三種云特征參數(shù)隨磨損量增大所呈現(xiàn)的變化趨勢(shì)和規(guī)律;最后,通過散點(diǎn)圖驗(yàn)證三種特征參數(shù)表征刀具磨損聲發(fā)射信號(hào)的有效性。提出了將云特征參數(shù)與最小二乘支持向量機(jī)相結(jié)合的刀具磨損狀態(tài)識(shí)別方法。針對(duì)神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)算法收斂速度慢、易陷入局部極值以及對(duì)特征要求較高等問題,提出基于云理論與最小二乘支持向量機(jī)結(jié)合刀具磨損狀態(tài)識(shí)別方法。實(shí)例分析表明,在優(yōu)化選取支持向量機(jī)參數(shù)的條件下,云-支持向量機(jī)結(jié)合的方法比傳統(tǒng)神經(jīng)網(wǎng)絡(luò)識(shí)別方法的識(shí)別率更高。將不確定性云推理模型應(yīng)用到刀具磨損量預(yù)測(cè)領(lǐng)域。首先,通過條件云發(fā)生器挖掘不同磨損階段磨損趨勢(shì)與該階段云特征參數(shù)數(shù)據(jù)之間的關(guān)系;其次,在此基礎(chǔ)上構(gòu)建云預(yù)測(cè)規(guī)則;最后,建立了多條件單規(guī)則不確定性磨損量預(yù)測(cè)方法。實(shí)例分析結(jié)果顯示,云推理磨損量預(yù)測(cè)結(jié)果符合刀具磨損規(guī)律;對(duì)非確定模型進(jìn)行預(yù)測(cè),云推理比模糊推理更接近實(shí)際情況。此外,該方法能可推廣到不同工況條件下的磨損量預(yù)測(cè),具有較強(qiáng)的實(shí)用性。
[Abstract]:With the development of equipment manufacturing industry, tool wear monitoring technology has become a key technology restricting modern automatic machine tools, which has not been effectively solved. Monitoring the cutting tool condition in real time can improve the machining quality and efficiency of the machine tool, reduce the accident of the machine tool, reduce the operation of the machine tool to the maximum extent, and realize the intellectualization and inhumanity of the machine tool. Make sure the system runs under the optimal parameters. Therefore, the research of tool wear monitoring technology is very urgent and important. In this paper, aiming at the research of tool wear condition monitoring and wear quantity prediction under different cutting conditions, cutting test is arranged by orthogonal test method, and on the basis of the collected acoustic emission signal, A new method of tool wear recognition based on cloud model theory and least square support vector machine (LS-SVM) is proposed, which combines wavelet packet analysis and optimal entropy theory. Finally, uncertainty cloud reasoning method is used to predict the uncertainty of wear quantity. The main research contents are as follows: the traditional signal preprocessing methods such as empirical mode decomposition (EMD) and frequency domain analysis (power spectrum analysis) are used in the previous tool wear monitoring signal filtering. Because of the nonstationary and nonlinear characteristics of the acoustic emission signal, the wavelet packet analysis method, which is suitable for the processing of the non-stationary signal, is introduced to the signal preprocessing to realize the signal filtering. First, the frequency band distribution range of acoustic emission signals at different wear stages is obtained by spectrum analysis, which can be used as the qualitative reference of wavelet packet decomposition level. Secondly, the Shannon entropy of information entropy theory is used to characterize the size of noise, and the best tree of wavelet packet decomposition is determined. Finally, the optimal branch of wavelet packet decomposition is determined by the statistical analysis of the best tree, and the signal to noise ratio (SNR) is higher than 35dB after threshold processing. A method for feature extraction of uncertain acoustic emission signals based on cloud theory is proposed. Firstly, the characteristic parameters, expectation, entropy and superentropy of acoustic emission signals with different wear quantities are extracted by the improved reverse cloud algorithm. Quantitative analysis of the change trend and law of the three cloud characteristic parameters with the increase of wear quantity under different cutting conditions. Finally, the validity of the three characteristic parameters to characterize the acoustic emission signal of tool wear is verified by scatter plot. A tool wear recognition method combining cloud feature parameters with least squares support vector machine (LS-SVM) is proposed. Aiming at the problems of slow convergence, easy to fall into local extremum and high demand for features of neural network learning algorithm, a tool wear recognition method based on cloud theory and least squares support vector machine (LS-SVM) is proposed. The analysis of examples shows that the cloud-support vector machine method has a higher recognition rate than the traditional neural network method under the condition of optimizing the selection of support vector machine parameters. The uncertain cloud reasoning model is applied to the field of tool wear prediction. Firstly, the relationship between wear trend of different wear stages and cloud characteristic parameter data is mined by conditional cloud generator. Secondly, cloud prediction rules are constructed on this basis. Finally, A prediction method for uncertain wear volume with multiple conditions and single rules is established. The result of case analysis shows that the prediction results of cloud reasoning wear quantity accord with the law of tool wear, and the cloud reasoning is closer to the actual situation than fuzzy reasoning to predict the uncertain model. In addition, the method can be extended to predict the wear quantity under different working conditions, and it has strong practicability.
【學(xué)位授予單位】：東北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TG71

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 閆曉玲;董世運(yùn);徐濱士;;基于最優(yōu)小波包Shannon熵的再制造電機(jī)轉(zhuǎn)子缺陷診斷技術(shù)[J];機(jī)械工程學(xué)報(bào);2016年04期

2 馮明;李興源;李妮;王超;洪潮;;基于Simplex算法的高壓直流輸電分段變速率VDCOL研究[J];四川大學(xué)學(xué)報(bào)(工程科學(xué)版);2015年04期

3 關(guān)山;彭昶;;刀具磨損聲發(fā)射信號(hào)的混沌特性分析[J];農(nóng)業(yè)工程學(xué)報(bào);2015年11期

4 陳紹南;陳碧云;韋化;任龍;韋杏秋;;不規(guī)則風(fēng)速概率分布的混合半云建模方法[J];中國(guó)電機(jī)工程學(xué)報(bào);2015年06期

5 楊軍;劉培;胡文平;巫偉南;于騰凱;;基于云理論的輸變電設(shè)備故障率預(yù)測(cè)方法[J];高電壓技術(shù);2014年08期

6 張鍇鋒;袁惠群;聶鵬;;基于廣義分形維數(shù)的刀具磨損狀態(tài)監(jiān)測(cè)[J];振動(dòng)與沖擊;2014年01期

7 劉禹;李德毅;;正態(tài)云模型霧化性質(zhì)統(tǒng)計(jì)分析[J];北京航空航天大學(xué)學(xué)報(bào);2010年11期

8 李鋒;湯寶平;章國(guó)穩(wěn);;基于舍一交叉驗(yàn)證優(yōu)化最小二乘支持向量機(jī)的故障診斷模型[J];振動(dòng)與沖擊;2010年09期

9 鄧揚(yáng);丁幼亮;李愛群;;基于小波包分析的拉索損傷聲發(fā)射信號(hào)特征提取[J];振動(dòng)與沖擊;2010年06期

10 張鍇鋒;聶鵬;王東磊;林士龍;諶鑫;;小波分析在數(shù)控刀具磨損狀態(tài)監(jiān)測(cè)中的應(yīng)用[J];測(cè)控技術(shù);2009年12期

相關(guān)博士學(xué)位論文 前8條

1 李威霖;車銑刀具磨損狀態(tài)監(jiān)測(cè)及預(yù)測(cè)關(guān)鍵技術(shù)研究[D];西南交通大學(xué);2013年

2 陳侃;基于多模型決策融合的刀具磨損狀態(tài)監(jiān)測(cè)系統(tǒng)關(guān)鍵技術(shù)研究[D];西南交通大學(xué);2012年

3 關(guān)山;基于聲發(fā)射信號(hào)多特征分析與融合的刀具磨損分類與預(yù)測(cè)技術(shù)[D];吉林大學(xué);2011年

4 李紹東;中國(guó)裝備制造業(yè)先進(jìn)水平實(shí)證研究[D];遼寧大學(xué);2011年

5 皇攀凌;面向鈦合金銑削的變齒距立銑刀研究[D];山東大學(xué);2011年

6 戴紅軍;我國(guó)機(jī)械制造業(yè)的發(fā)展研究[D];河北工業(yè)大學(xué);2010年

7 高宏力;切削加工過程中刀具磨損的智能監(jiān)測(cè)技術(shù)研究[D];西南交通大學(xué);2005年

8 熊四昌;基于計(jì)算機(jī)視覺的刀具磨損狀態(tài)監(jiān)測(cè)技術(shù)的研究[D];浙江大學(xué);2003年

相關(guān)碩士學(xué)位論文 前10條

1 羅維朗;鉆孔動(dòng)力頭鉆削刀具磨損監(jiān)測(cè)方法研究[D];浙江大學(xué);2016年

2 彭昶;基于混沌理論的刀具磨損識(shí)別研究[D];東北電力大學(xué);2016年

3 唐亮;基于人工蜂群優(yōu)化神經(jīng)網(wǎng)絡(luò)的刀具磨損狀態(tài)監(jiān)測(cè)系統(tǒng)研究[D];西南交通大學(xué);2014年

4 李敏;基于粒子群優(yōu)化神經(jīng)網(wǎng)絡(luò)的刀具磨損狀態(tài)監(jiān)測(cè)技術(shù)研究[D];西南交通大學(xué);2012年

5 任振華;基于振動(dòng)信號(hào)的PCB微鉆刀具磨損狀態(tài)監(jiān)測(cè)研究[D];上海交通大學(xué);2012年

6 常艷麗;鎳基高溫合金GH4169的切削力與刀具磨損試驗(yàn)研究[D];哈爾濱工業(yè)大學(xué);2011年

7 賀靈敏;EMD和支持向量機(jī)在刀具故障診斷中的應(yīng)用[D];西華大學(xué);2010年

8 黃遂;切削加工過程中刀具磨損智能監(jiān)測(cè)與切削力預(yù)測(cè)技術(shù)研究[D];西南交通大學(xué);2010年

9 朱麗琴;基于分形理論的刀具磨損狀態(tài)識(shí)別研究[D];西南交通大學(xué);2009年

10 高琛;刀具磨損狀態(tài)監(jiān)測(cè)系統(tǒng)研究[D];山東大學(xué);2008年

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本文編號(hào)：1931073