本文選題：刀具磨損 + 聲發(fā)射信號��； 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)測技術(shù)已成為制約現(xiàn)代自動化機(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)時地監(jiān)測刀具狀態(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)測技術(shù)的研究是非常迫切且重要的。本文針對不同切削條件下刀具磨損狀態(tài)監(jiān)測和磨損量預(yù)測研究課題,通過正交試驗(yàn)法安排切削試驗(yàn),在采集的聲發(fā)射信號的基礎(chǔ)上,應(yīng)用現(xiàn)代信號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。主要研究內(nèi)容由以下幾個部分構(gòu)成:以往的刀具磨損監(jiān)測信號濾波采用時域分析(經(jīng)驗(yàn)?zāi)B(tài)分解)、頻域分析(功率譜分析)等傳統(tǒng)的信號預(yù)處理方法。由于所采用的聲發(fā)射信號的非平穩(wěn)和非線性特點(diǎn),本文將適合處理非平穩(wěn)信號處理的小波包分析方法引入到信號預(yù)處理中,實(shí)現(xiàn)信號的濾波。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)識別方法。針對神經(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)識別方法的識別率更高。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)測方法。實(shí)例分析結(jié)果顯示,云推理磨損量預(yù)測結(jié)果符合刀具磨損規(guī)律;對非確定模型進(jìn)行預(yù)測,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,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)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG71

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本文編號：1931073