本文選題：電弧聲 + 復(fù)合傳感��； 參考：《南昌航空大學(xué)》2017年碩士論文

【摘要】：在熔化極氣體保護焊中,如何在線監(jiān)測或控制焊接質(zhì)量是一項最為重要的研究課題。在實際生產(chǎn)中,焊接質(zhì)量決定著產(chǎn)品的最終質(zhì)量,而焊絲的熔滴過渡方式不僅決定了弧焊焊絲熔化時的平穩(wěn)性還嚴(yán)重影響著焊縫的成形、熔深以及材料的消耗、冶金等方面,與焊接質(zhì)量密切相關(guān)。本文針對MIG焊薄板平鋪過程中的電弧聲信號,著重研究基于電弧聲信號識別不同熔滴過渡類型。搭建MIG焊不同熔滴過渡模式下電弧聲信號和電信號同步采集與分析系統(tǒng)試驗平臺,包括機器人焊接系統(tǒng)、復(fù)合傳感系統(tǒng)、熔滴過渡模式高速攝影系統(tǒng)及系統(tǒng)軟件。根據(jù)MIG焊方法特點,研制了合適的電信號傳感器系統(tǒng)和電弧聲傳感器系統(tǒng),能夠有效地采集焊接過程中的電弧聲信號、電信號以及焊絲熔滴過渡狀態(tài)的圖像信號。對短路過渡模式下的電弧聲信號與電信號進行自相關(guān)函數(shù)分析,并且對電弧聲信號與電流信號和電弧聲信號與電壓信號進行互相關(guān)函數(shù)分析,結(jié)果顯示:電弧聲與電壓、電流信號具有相似的周期性,電弧能量與電弧聲密切相關(guān)。針對短路過渡、射滴過渡和射流過渡的電弧聲信號進行功率譜分析,由功率譜波形圖可以發(fā)現(xiàn)不同熔滴過渡模式下,電弧聲信號的頻率分布有明顯差異,并且有一定的規(guī)律性,即短路過渡過程低頻成分較多,射滴過渡和射流過渡高頻成分較多。對短路過渡、射滴過渡和射流過渡的電弧聲進行小波包分析。小波包分解時小波基函數(shù)選擇db14,分解層數(shù)設(shè)置為4。提取電弧聲信號小波包4層分解后頻帶能量特征值。電弧聲信號S_(4,0)、S_(4,2)、S_(4,3)頻帶能量分布百分比差異明顯,可作為識別熔滴過渡類型的特征向量。提取不同熔滴過渡狀態(tài)的電弧聲信號峰度系數(shù)值,分析發(fā)現(xiàn),短路過渡、射滴過渡和射流過渡的峰度Ku存在差異性,可以作為熔滴過渡類型的特征向量。鑒于此,識別熔滴過渡模式的四維聯(lián)合特征向量就構(gòu)造完成了�；贛ATLAB軟件平臺設(shè)計了電弧聲信號MIG焊熔滴過渡類型模式識別網(wǎng)絡(luò)模型,網(wǎng)絡(luò)選擇廣義回歸神經(jīng)網(wǎng)絡(luò)和概率神經(jīng)網(wǎng)絡(luò)。結(jié)果顯示:GRNN網(wǎng)絡(luò)熔滴過渡類型識別率為96.7%,PNN網(wǎng)絡(luò)熔滴過渡類型識別率為93.3%。通過構(gòu)造的電弧聲四維聯(lián)合特征向量能夠有效識別熔滴過渡類型,識別精確度較高,達到了預(yù)期實驗?zāi)繕?biāo)。
[Abstract]:How to monitor or control welding quality online is one of the most important research topics in gas shielded electrode welding.In actual production, the welding quality determines the final quality of the product, and the droplet transfer mode of the welding wire not only determines the stability of the arc welding wire melting, but also seriously affects the weld formation, penetration depth, material consumption, metallurgy and so on.Closely related to welding quality.In this paper, the arc sound signal in the process of MIG welding sheet tile is studied, and the recognition of different droplet transfer types based on the arc sound signal is studied.A test platform for synchronous acquisition and analysis of arc acoustic signals and electrical signals in different droplet transfer modes of MIG welding was built, including robot welding system, composite sensing system, high-speed photography system and system software.According to the characteristics of MIG welding method, a suitable electric signal sensor system and an arc sound sensor system are developed, which can effectively collect the arc sound signal, the electric signal and the image signal of the welding wire droplet transfer state.The autocorrelation function analysis of arc sound signal and electric signal in short-circuit transition mode is carried out, and the cross-correlation function analysis between arc sound signal and current signal and arc sound signal and voltage signal is carried out. The results show that: arc sound and voltage,The current signal has similar periodicity and the arc energy is closely related to the arc sound.Based on the power spectrum analysis of arc acoustic signals with short-circuit transfer, droplet transfer and jet transfer, it can be found that the frequency distribution of arc acoustic signals is obviously different under different droplet transfer modes and has certain regularity.That is to say, the low frequency components of short circuit transition are more, and the high frequency components of droplet transfer and jet transfer are more.The arc sound of short circuit transfer, droplet transfer and jet transfer is analyzed by wavelet packet.Wavelet packet decomposition when the wavelet basis function selection db14, decomposition layer set to 4.The band energy eigenvalues of the arc acoustic signal are extracted after the decomposition of the wavelet packet 4 layers.The percentage difference of energy distribution in the frequency band of the arc sound signal S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / S / SThe kurtosis coefficients of arc acoustic signals in different droplet transfer states are extracted. It is found that the kurtosis Ku of short-circuit transfer, droplet transfer and jet transition is different, and can be used as the characteristic vector of droplet transfer type.In view of this, the four dimensional joint Eigenvectors for identifying droplet transfer patterns are constructed.Based on the MATLAB software platform, the network model of droplet transfer pattern recognition for MIG welding with arc sound signal is designed, and the generalized regression neural network and probabilistic neural network are selected.The results show that the recognition rate of droplet transfer type of the WGRNN network is 96.7% and the recognition rate of droplet transfer type of PNN network is 93.3%.The arc acoustic four-dimensional joint eigenvector can effectively identify the droplet transfer type, and the recognition accuracy is high, and the expected experimental goal is achieved.
【學(xué)位授予單位】：南昌航空大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG444.74

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