本文選題：刀具磨損狀態(tài) + 互補(bǔ)總體平均經(jīng)驗(yàn)?zāi)B(tài)分解　； 參考：《南京信息工程大學(xué)》2017年碩士論文

【摘要】：隨著機(jī)床在自動化、集成化和無人化方向發(fā)展的越來越快,如何保證加工產(chǎn)品的質(zhì)量和生產(chǎn)效率就顯得尤為重要。而刀具作為加工過程的直接執(zhí)行者,不可避免地存在著磨損現(xiàn)象。因此為了保證產(chǎn)品質(zhì)量的同時(shí)又實(shí)現(xiàn)對刀具的高效利用,就有必要對刀具狀態(tài)監(jiān)測技術(shù)展開研究。針對刀具加工特點(diǎn),本文選擇對刀具聲發(fā)射信號進(jìn)行監(jiān)測,聲發(fā)射監(jiān)測技術(shù)作為一種有效的無損檢測技術(shù)因其靈敏度高、抗干擾能力強(qiáng)、無需停機(jī)等優(yōu)點(diǎn)已經(jīng)得到廣泛的應(yīng)用,但由于采集得到的聲發(fā)射信號頻率高,數(shù)據(jù)量大且頻率成分復(fù)雜,無法直接進(jìn)行刀具狀態(tài)識別,故本文為了準(zhǔn)確定性的掌握切削過程中刀具的磨損狀態(tài),提出了一種基于互補(bǔ)總體平均經(jīng)驗(yàn)?zāi)B(tài)分解(Complementary Ensemble Empirical Mode Decomposition, CEEMD)和小波包變換(Wavelet Package Transform, WPT)的刀具狀態(tài)監(jiān)測方法。首先利用CEEMD將聲發(fā)射信號自適應(yīng)地分解成多個(gè)固有模態(tài)函數(shù)(Intrinsic Mode Function, IMF),每個(gè)IMF內(nèi)都包含有原信號的不同時(shí)間尺度特征,針對依然存在的模態(tài)混疊問題的IMF,利用WPT良好的局部處理能力予以修正,從而實(shí)現(xiàn)對特征分量的精確提取,然后選取修正后能量值較大的前幾個(gè)IMF分量,求其占總能量的比重組成特征向量,最后輸入到支持向量機(jī)(Support Vector Machine, SVM)中進(jìn)行訓(xùn)練與測試,從而建立起由6個(gè)SVM二值分類器組成的4類刀具狀態(tài)識別系統(tǒng)。文章通過與CEEMD特征提取方法進(jìn)行比較,說明CEEMD-WPT提取的特征更加精確,更具有表征性,將兩種時(shí)頻分析方法結(jié)合起來,既有效的解決了CEEMD分解后依然存在的模態(tài)混疊問題,又消除了單獨(dú)進(jìn)行WPT處理后產(chǎn)生虛假頻率分量、頻率混淆現(xiàn)象的影響,為后期精確地識別出刀具磨損狀態(tài)奠定了好的基礎(chǔ)。
[Abstract]:With the development of automatic, integrated and unmanned machine tools, it is very important to ensure the quality and production efficiency of processed products. As the direct executor of machining process, tool wear is inevitable. Therefore, it is necessary to study the tool condition monitoring technology in order to ensure the product quality and realize the efficient use of cutting tools. According to the characteristics of tool machining, this paper chooses to monitor the tool acoustic emission signal. As an effective nondestructive testing technology, acoustic emission monitoring technology has been widely used because of its high sensitivity, strong anti-interference ability and no need to stop. However, due to the high frequency of acoustic emission signal collected, the large amount of data and the complexity of frequency components, it is impossible to recognize the cutting tool state directly, so in order to accurately and qualitatively grasp the tool wear state in the cutting process, A tool condition monitoring method based on complementary Ensemble empirical Mode decomposition (CEEMDM) and Wavelet package transform (WPT) is proposed. At first, acoustic emission signals are decomposed adaptively into Intrinsic Mode functions (IMFMs) by CEEMD. Each IMF contains different time scale characteristics of the original signals. Aiming at the IMF of the existing modal aliasing problem, this paper uses the good local processing ability of WPT to correct the feature components, and then selects the first few IMF components with large corrected energy. Finally, it is input into support vector machine support Vector Machine (SVM) for training and testing, and four types of tool state recognition system composed of six SVM binary classifiers are established. Compared with CEEMD feature extraction method, this paper shows that the feature extraction of CEEMD-WPT is more accurate and more characterizing. The combination of two time-frequency analysis methods can effectively solve the modal aliasing problem that still exists after CEEMD decomposition. The effect of false frequency component and frequency confusion after WPT processing alone is eliminated, which lays a good foundation for accurate identification of tool wear state in later stage.
【學(xué)位授予單位】：南京信息工程大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG71

【參考文獻(xiàn)】

中國期刊全文數(shù)據(jù)庫 前10條

1 王麗華;陶潤U，

本文編號：1988201