本文關(guān)鍵詞：電弧焊熔透狀態(tài)視覺檢測模型研究　出處：《廣東工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 焊接質(zhì)量 熔透狀態(tài) 模式識別 ICA算法 BP神經(jīng)網(wǎng)絡(luò)

【摘要】：焊接是制造業(yè)的基礎(chǔ)。隨著工業(yè)制造要求的提高,焊接質(zhì)量的要求也變得越來越重要。電弧焊由于自身技術(shù)發(fā)展十分成熟,且適用的范圍也十分廣泛,目前應(yīng)用在諸多領(lǐng)域。因此,人們對電弧焊的焊接質(zhì)量提出了更高的要求。在越來越注重效率的今天,電弧焊焊接實時監(jiān)測顯得愈發(fā)重要,為此論文研究基于機器視覺傳感的熔透狀態(tài)監(jiān)測模型。在實時監(jiān)測焊接質(zhì)量的過程中,必須通過當前的焊接狀態(tài)與焊接質(zhì)量之間的關(guān)系建立數(shù)學(xué)模型以獲取熔透實時監(jiān)測狀態(tài)。許多研究表明,焊接過程中形成的熔池與焊縫背部的熔透有著密切的聯(lián)系,而熔透與最終的焊接質(zhì)量聯(lián)系緊密,因此通過提取的熔池特征能與當前的焊接質(zhì)量建立對應(yīng)關(guān)系。通過視覺傳感器拍攝焊接過程的熔池圖像,利用相關(guān)的圖像處理技術(shù)從中提取出最能代表熔池信息的特征參數(shù),再與此時的焊接質(zhì)量對應(yīng)起來,建立熔池特征與熔透狀態(tài)之間的數(shù)學(xué)模型。通過多組焊接試驗條件驗證模型,證明建立的數(shù)學(xué)模型能夠有效地預(yù)測出當前的熔透狀態(tài),最終得到當前的焊接質(zhì)量。對熔透當前的狀態(tài)進行實時監(jiān)測可以看作是模式識別的一類,而神經(jīng)網(wǎng)絡(luò)則在模式識別這一方面有著較高的預(yù)測精度,因此論文根據(jù)神經(jīng)網(wǎng)絡(luò)的特點設(shè)計了兩個不同訓(xùn)練算法的神經(jīng)網(wǎng)絡(luò)：BP算法(Back Propagation Algorithm)和ICA算法(Imperialist Competitive Algorithm),利用神經(jīng)網(wǎng)絡(luò)模型對熔透狀態(tài)進行準確預(yù)測。其中,ICA算法是通過模擬帝國主義競爭的過程來得到優(yōu)化問題最優(yōu)解的一種全局優(yōu)化搜索算法。首先,從熔池圖像中提取出3個特征參數(shù)：熔池面積、熔池熔寬、熔池半長。其次,采用試湊法確定神經(jīng)網(wǎng)絡(luò)隱含層神經(jīng)元個數(shù),建立一個預(yù)測精度較高的神經(jīng)網(wǎng)絡(luò)模型,最后用兩個算法訓(xùn)練神經(jīng)網(wǎng)絡(luò),獲得兩個模型對熔透各自的識別率。試驗結(jié)果表明選用的BP神經(jīng)網(wǎng)絡(luò)最優(yōu)識別模型比ICA神經(jīng)網(wǎng)絡(luò)的最優(yōu)模型對不同焊接條件的熔透狀態(tài)有著很高的預(yù)測精度。針對BP神經(jīng)網(wǎng)絡(luò)的識別精度高但過于依賴初始權(quán)值閾值的特點和ICA算法在全局搜索的優(yōu)點,論文將ICA算法與BP神經(jīng)網(wǎng)絡(luò)結(jié)合,利用ICA算法選取BP神經(jīng)網(wǎng)絡(luò)初始權(quán)值及閾值,提出一種ICA-BP神經(jīng)網(wǎng)絡(luò)的熔透識別模型。首先將模型的權(quán)值閾值作為優(yōu)化問題要求的解,然后根據(jù)熔透狀態(tài)輸出的實際誤差與期望誤差的差的大小及時調(diào)整權(quán)值及閾值。其次利用ICA得到神經(jīng)網(wǎng)絡(luò)的初始權(quán)值、閾值,通過BP算法對神經(jīng)網(wǎng)絡(luò)進行訓(xùn)練,得到使得模型識別精度最高的一組權(quán)值和閾值。最終利用不同試驗條件尋找對熔透的識別效果最好的神經(jīng)網(wǎng)絡(luò)模型。試驗結(jié)果證明ICA-BP神經(jīng)網(wǎng)絡(luò)對熔透狀態(tài)的預(yù)測精度優(yōu)于BP神經(jīng)網(wǎng)絡(luò)的預(yù)測精度。
[Abstract]:Welding is the foundation of the manufacturing industry. With the improvement of industrial manufacturing requirements, the requirement of welding quality is becoming more and more important. Arc welding is widely used in many fields because of its mature technology and wide range of application. Therefore, people have put forward higher requirements for the welding quality of arc welding. Nowadays, more and more attention is paid to efficiency. The real-time monitoring of arc welding is becoming more and more important. Therefore, a penetration monitoring model based on machine vision sensing is studied in this paper. In real-time monitoring of welding quality, we must establish a mathematical model through the relationship between the current welding state and the welding quality, so as to get the real-time monitoring state of penetration. Many studies have shown that the weld pool formed during the welding process is closely related to the penetration of the back of the weld, and the penetration is closely related to the final welding quality. Therefore, the characteristics of the molten pool can be used to establish the corresponding relationship with the current welding quality. The image of molten pool is captured by visual sensor, and the characteristic parameters that represent the information of molten pool are extracted from related image processing technology. Then the mathematical model between molten pool characteristics and penetration state is established based on the corresponding welding quality. Through multi group welding test condition verification model, it is proved that the established mathematical model can effectively predict the current penetration state, and finally get the current welding quality. The current penetration state monitoring can be regarded as a kind of pattern recognition, pattern recognition and neural network in this area has a higher prediction accuracy, so this paper according to the characteristics of the neural network to design a neural network with two different training algorithms: BP algorithm (Back Propagation Algorithm) and ICA algorithm (Imperialist Competitive, Algorithm) by using the neural network model to forecast the penetration state. Among them, ICA algorithm is a global optimization search algorithm to optimize the optimal solution by simulating the process of imperialist competition. First, extract 3 feature parameters from the image of weld pool in molten pool area, pool and pool half length Rongkuan. Secondly, we use the trial and error method to determine the number of neurons in the hidden layer of the neural network, and establish a neural network model with high prediction accuracy. Finally, we use two algorithms to train the neural network, and get the recognition rates of two models for penetration. The experimental results show that the optimal identification model of BP neural network is higher than that of ICA neural network, and has high prediction accuracy for penetration condition of different welding conditions. According to the characteristics and advantages of the ICA search algorithm of BP neural network with high accuracy but is too dependent on the initial weights in the global threshold, the ICA algorithm combined with BP neural network, using ICA algorithm to select BP neural network initial weights and threshold, proposes a ICA-BP neural network fusion recognition model. First, the weight threshold of the model is used as the solution of the optimization problem, and then the weights and thresholds are adjusted according to the difference between the actual error and the expected error of the penetration state output. Secondly, we use ICA to get the initial weights and thresholds of neural network, and train the neural network through BP algorithm, and get a set of weights and thresholds that make the model identification accuracy the highest. Finally, different experimental conditions are used to find the neural network model which has the best recognition effect on penetration. The experimental results show that the prediction accuracy of ICA-BP neural network is better than that of BP neural network.
【學(xué)位授予單位】：廣東工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TG441.7;TP391.41

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