本文選題：高壓干法GMAW + 焊縫成形��； 參考：《北京石油化工學(xué)院》2015年碩士論文

【摘要】：海洋石油資源的開發(fā)已經(jīng)成為世界范圍內(nèi)的新的研究熱點(diǎn)之一,海洋中出現(xiàn)越來越多的石油平臺鋼結(jié)構(gòu)、海底管道、水下結(jié)構(gòu)。由于長期浸蝕在海水中,水下焊接修復(fù)十分困難,所以研究適用于深海水下焊接技術(shù)十分關(guān)鍵。高壓干法GMAW是具有現(xiàn)實意義的水下焊接方法,已成為海洋油氣田開采和儲運(yùn)常用的水下焊接技術(shù)之一,得到廣泛應(yīng)用。焊縫成形是反映焊接質(zhì)量的一個重要指標(biāo),通過分析高壓下的焊縫成形有助于研究高壓GMAW的規(guī)律,對焊縫成形進(jìn)行預(yù)測能夠指導(dǎo)高壓環(huán)境下焊接工藝,有效節(jié)約高壓焊接試驗成本。鑒于此原因本文建立了基于BP神經(jīng)網(wǎng)絡(luò)的焊縫成形預(yù)測模型,該模型可以實現(xiàn)焊縫信息預(yù)測,由此優(yōu)化焊接工藝方案,提高焊接質(zhì)量。本文的研究內(nèi)容如下：(1)首先,利用正交設(shè)計法設(shè)計了實驗方案,該方案以焊接工藝參數(shù)為影響因素,每個因素取四個水平,焊縫成形尺寸為輸出變量。在搭建好的高壓焊接試驗系統(tǒng)中進(jìn)行高壓焊接試驗,采集焊縫樣本數(shù)據(jù),作為神經(jīng)網(wǎng)絡(luò)的學(xué)習(xí)訓(xùn)練樣本和預(yù)測樣本。(2)通過分析焊縫成形數(shù)據(jù),得到高壓環(huán)境下GMAW焊縫成形的規(guī)律,如高壓下,焊縫成形不好,飛濺較大;環(huán)境壓力的增加會引起焊縫熔寬減小,熔深增加,余高增高。(3)利用回歸分析的方法建立了焊縫成形尺寸與焊接工藝參數(shù)之間的回歸方程,得到了經(jīng)驗公式。對回歸方程進(jìn)行R2檢驗、F檢驗和置信區(qū)間估計,回歸方程具有很高的顯著性,能夠用于焊縫成形預(yù)測。(4)建立了基于BP神經(jīng)網(wǎng)絡(luò)的高壓GMAW焊縫成形預(yù)測模型,該模型以壓力、電流、CO2比例和干伸長為輸入,以熔寬、熔深和余高為輸出。在VC++環(huán)境下,開發(fā)了可視化的神經(jīng)網(wǎng)絡(luò)預(yù)測系統(tǒng)。將該預(yù)測系統(tǒng)的預(yù)測結(jié)果與實際值進(jìn)行比較,得到該預(yù)測系統(tǒng)相對誤差較小,可靠度較高的結(jié)論。最后利用試驗數(shù)據(jù)對建立的回歸方程和BP神經(jīng)網(wǎng)絡(luò)模型的預(yù)測能力進(jìn)行了對比。結(jié)果表明,BP網(wǎng)絡(luò)的的預(yù)測能力優(yōu)于回歸方程,所以建立的神經(jīng)網(wǎng)絡(luò)預(yù)測模型更適用于高壓GMAW焊縫成形預(yù)測。
[Abstract]:The development of marine petroleum resources has become one of the new research hotspots in the world. There are more and more steel structures, submarine pipelines and underwater structures in the ocean. It is very difficult to repair the underwater welding because of the long-term erosion in the sea water. So it is very important to study the underwater welding technology suitable for the deep sea. The high pressure dry method GMAW is the key. The underwater welding method, which has practical significance, has become one of the underwater welding techniques commonly used in the mining and storage of offshore oil and gas fields, and it has been widely used. The weld forming is an important index to reflect the quality of the welding. The analysis of weld formation under high pressure helps to study the law of high pressure GMAW, and the prediction of weld formation can be guided. Welding technology under high pressure environment can effectively save the cost of high pressure welding test. For this reason, a prediction model of weld formation based on BP neural network is established in this paper. This model can predict weld information, thus optimize the welding process and improve the quality of welding. The contents of this paper are as follows: (1) first, the orthogonal design method is used. Considering the experimental scheme, the scheme takes the welding process parameters as the influencing factor, each factor takes four levels, the weld forming size is the output variable. The high pressure welding test is carried out in the built high pressure welding test system, the sample data of the weld is collected as the learning training sample and the prediction sample of the neural network. (2) through the analysis of the weld formation. The shape data of GMAW weld formation under high pressure, such as under high pressure, the weld formation is not good, the spatter is great, the increase of environmental pressure will cause the weld weld width to decrease, the weld depth increase and the excess height increase. (3) the regression equation is established between the weld forming size and the welding process parameters by the regression analysis method, and the empirical formula is obtained. The regression equation has R2 test, F test and confidence interval estimation. The regression equation has high significance and can be used for weld forming prediction. (4) a prediction model of high pressure GMAW weld forming based on BP neural network is established. The model is input with pressure, current, CO2 ratio and dry elongation as input, and the weld width, depth and residual height are output. In VC++ ring Under the circumstances, a visual neural network prediction system is developed. The results of the prediction system are compared with the actual value, and the relative error of the prediction system is smaller and the reliability is higher. Finally, the prediction ability of the regression equation and the BP neural network model is compared with the experimental data. The results show that the BP network is used. The prediction ability of the neural network prediction model is better than that of the regression equation, so the prediction model of the neural network is more suitable for the prediction of the weld formation of high pressure GMAW.

【學(xué)位授予單位】：北京石油化工學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TG456.5

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