【摘要】：拉深裂紋作為金屬板材成形件的主要失效形式,常在成形件危險區(qū)域出現(xiàn),尤其是其中的早期裂紋,很難憑借傳統(tǒng)的檢測方法進行識別。針對上述所提到的問題,本研究采用聲發(fā)射(AE)信號檢測技術對金屬成形件的拉深AE信號進行檢測,再應用基于小波閥值-EMD綜合法對裂紋AE信號進行分解降噪和重組,最后利用基于模糊等價關系的模糊聚類方法對各類裂紋進行模糊識別。主要的研究內(nèi)容見下文:1)以盒形拉深件為本研究的理論模型,對金屬板材成形件的拉深應力、應變狀態(tài)和拉深過程出現(xiàn)裂紋的成因進行分析,然后結合金屬板材拉深件畚斗的仿真結果得到此類拉深件容易出現(xiàn)拉深裂紋的危險區(qū)域;對無損檢測方法中的聲發(fā)射檢測系統(tǒng)和工作原理進行介紹,在結合了金屬板材拉深過程中裂紋的擴展伴隨著聲發(fā)射信號和聲發(fā)射信號的特點,確定對金屬板材拉深件進行深拉來獲得拉深裂紋,并通過聲發(fā)射檢測系統(tǒng)對整個拉深過程進行監(jiān)測以獲得包含裂紋信號的聲發(fā)射信號。2)結合上述理論分析,以金屬板材拉深件畚斗為研究對象進行了金屬板材拉深的AE信號采集實驗,大量的采集和保存無裂紋、早起裂紋、擴展裂紋三種裂紋狀態(tài)的AE信號,并對采集的信號和金屬制件畚斗進行對應標注,以便于后期的數(shù)據(jù)處理。3)對聲發(fā)射信號的降噪方法進行了研究,將其中對聲發(fā)射降噪效果比較好的小波閾值濾波降噪、EMD降噪兩種降噪方法進行分析和比較,并結合它們的優(yōu)缺點提出了小波閾值-EMD綜合降噪法,采用這種綜合降噪法對金屬板材拉深件的聲發(fā)射信號進行降噪處理,在降噪前首先要采用消失矩為5的Daubechies小波基對AE信號進行三層小波分解操作,然后根據(jù)信號的頻段進行頻帶選取,對選取的頻帶信號采用EMD降噪方法進行降噪,而沒有選取的頻帶信號則采用小波閾值降噪,然后將兩部分降噪過后的信號進行信號重構得到純凈的聲發(fā)射信號。4)分析了金屬板材成形裂紋產(chǎn)生的非平穩(wěn)信號的特征,從降噪過后的純凈信號中提取出對應的信號參數(shù);對模糊聚類算法進行介紹,選擇基于模糊等價關系的模糊聚類方法對四類信號進行模糊識別,選取相互獨立的幅度、有效值電壓(RMS)、平均信號電平值(ASL)、能量四個參數(shù)作為識別參數(shù),建立數(shù)據(jù)矩陣,運用MATLAB軟件對金屬板材拉深件畚斗的提取參數(shù)進行數(shù)值模擬分析,實現(xiàn)對無裂紋、早起裂紋和擴展裂紋三種狀態(tài)裂紋的多參數(shù)模糊識別。研究結果表明:利用聲發(fā)射采集系統(tǒng)采集金屬制件畚斗的聲發(fā)射信號,采用小波閾值-EMD綜合降噪法對拉深信號進行降噪處理和重構,然后從中提取特征信號作為模糊聚類的參數(shù),實現(xiàn)了對金屬板材拉深件畚斗裂紋狀態(tài)(尤其是早期裂紋)的模糊識別,且識別準確率較高。
[Abstract]:Drawing cracks, as the main failure forms of sheet metal forming parts, often appear in the dangerous areas of forming parts, especially the early cracks, so it is difficult to identify them by traditional detection methods. In order to solve the problems mentioned above, the acoustic emission (AE) signal detection technique is used to detect the deep drawing AE signal of metal forming parts, and then the cracked AE signal is decomposed and recombined based on wavelet threshold EMD synthesis method. Finally, the fuzzy clustering method based on fuzzy equivalence relation is used to identify all kinds of cracks. The main research contents are as follows: 1) taking the box drawing parts as the theoretical model of this study, the paper analyzes the drawing stress, strain state and the causes of cracks in the drawing process of sheet metal forming parts. Then combining with the simulation results of the metal sheet drawing bucket, the dangerous area where the drawing crack is easy to appear is obtained. This paper introduces the acoustic emission testing system and working principle in the nondestructive testing method. It combines the characteristics of acoustic emission signal and acoustic emission signal in the process of metal sheet drawing. It is determined that deep drawing is carried out to obtain deep drawing crack of metal sheet drawing, and the whole drawing process is monitored by acoustic emission detection system to obtain acoustic emission signal containing crack signal. 2) combined with the above theoretical analysis, Taking the metal plate deep drawing bucket as the research object, the AE signal acquisition experiment of metal plate drawing is carried out. A large number of AE signals are collected and preserved in three kinds of crack states, that is, no crack, early rise crack and propagating crack. At the same time, the signal and metal dustbin are labeled accordingly, so as to facilitate the later data processing. 3) the noise reduction method of acoustic emission signal is studied, and the wavelet threshold filter, which has better effect on acoustic emission noise reduction, is used to reduce the noise. Two methods of EMD noise reduction are analyzed and compared. Combined with their advantages and disadvantages, the wavelet threshold-EMD comprehensive noise reduction method is put forward, which is used to reduce the noise of the acoustic emission signal of the metal sheet drawing parts. Before noise reduction, Daubechies wavelet basis with vanishing moment of 5 is used to decompose AE signal with three-layer wavelet transform, then the frequency band is selected according to the frequency band of the signal, and the selected frequency band signal is de-noised by EMD denoising method. The non-selected band signal is de-noised by wavelet threshold, and then the two parts of the de-noised signal are reconstructed to get the pure acoustic emission signal. 4) the characteristics of the non-stationary signal produced by the metal sheet forming crack are analyzed. The corresponding signal parameters are extracted from the pure signal after noise reduction. This paper introduces the fuzzy clustering algorithm, selects the fuzzy clustering method based on the fuzzy equivalence relation to carry on the fuzzy recognition to the four kinds of signals, selects the mutually independent amplitude, the effective value voltage (RMS), average signal level value (ASL), Four parameters of energy are used as identification parameters, data matrix is established, and the extraction parameters of metal plate deep drawing bucket are numerically simulated and analyzed by using MATLAB software, and the crack free is realized. Multi-parameter fuzzy identification of early crack and propagating crack. The results show that the acoustic emission signal of metal dustbin is collected by acoustic emission acquisition system, and the deep drawing signal is de-noised and reconstructed by wavelet threshold-EMD comprehensive de-noising method. Then the feature signal is extracted as the parameter of fuzzy clustering to realize fuzzy identification of bucket crack state (especially early crack) of metal sheet drawing parts and the accuracy of identification is high.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TG386.32

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