本文選題：WE43鎂合金 + 激光重熔��； 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：鎂合金具有比強(qiáng)度高、密度低、電磁屏蔽性和減震性好等優(yōu)點(diǎn),其作為一種輕量化材料,已被各行各業(yè)廣泛采用,但鎂合金較差的耐蝕性能嚴(yán)重限制了其應(yīng)用。為了提高鎂合金的耐蝕性能,對其表面進(jìn)行處理是最簡單、有效的方法之一。為此,本文以WE43稀土鎂合金為研究對象,采用激光重熔與微弧氧化復(fù)合工藝制備陶瓷膜層,基于ANSYS平臺建立激光重熔過程溫度場的數(shù)值模型,分析了不同工藝參數(shù)下溫度場的演化規(guī)律,研究了該陶瓷膜層的顯微組織與耐蝕性能,并基于基因遺傳算法優(yōu)化誤差反向傳播神經(jīng)網(wǎng)絡(luò)建立微弧氧化工藝參數(shù)與膜層厚度之間預(yù)測模型,主要工作如下:1.利用ANSYS有限元分析軟件對激光重熔的溫度場進(jìn)行了數(shù)值模擬,利用參數(shù)化設(shè)計(jì)語言仿真了激光重熔的動態(tài)過程,分析了其溫度場隨時(shí)間的分布規(guī)律。2.采用SEM、XRD對比分析了激光重熔-微弧氧化復(fù)合工藝和單一微弧氧化工藝制備的陶瓷膜層的微觀組織,結(jié)果表明:激光重熔-微弧氧化復(fù)合工藝的膜層截面微裂紋和孔洞都要少于單一微弧氧化膜層,同時(shí),激光重熔-微弧氧化復(fù)合工藝膜層表面的孔隙率較單一微弧氧化膜層下降了約1/2,膜層的致密性得到提高。3.采用電化學(xué)工作站和浸泡實(shí)驗(yàn)對比分析了激光重熔-微弧氧化復(fù)合工藝和單一微弧氧化工藝制備的陶瓷膜層的耐蝕性能,結(jié)果表明:激光重熔后的微弧氧化膜層較單一微弧氧化膜層的自腐蝕電流密度降低了一個(gè)數(shù)量級;激光重熔后的微弧氧化膜層電化學(xué)阻抗譜的容抗弧半徑較單一微弧氧化膜層明顯增大;激光重熔后的微弧氧化膜層出現(xiàn)明顯腐蝕的時(shí)間要遠(yuǎn)遠(yuǎn)長于單一微弧氧化膜層,這都表明激光重熔-微弧氧化復(fù)合工藝在提高鎂合金耐蝕性能方面要優(yōu)于單一微弧氧化工藝。4.根據(jù)在不同微弧氧化工藝參數(shù)(電流大小、脈沖寬度、氧化時(shí)間)下獲得的膜層厚度測量數(shù)據(jù),采用基因遺傳算法(GA)優(yōu)化誤差反向傳播(BP)神經(jīng)網(wǎng)絡(luò)建立微弧氧化工藝參數(shù)與膜層厚度之間的GA-BP預(yù)測模型。結(jié)果表明:在相同的訓(xùn)練樣本和檢驗(yàn)樣本條件下,GA-BP模型具有更優(yōu)的函數(shù)逼近能力,更好的泛化能力,其中,BP神經(jīng)網(wǎng)絡(luò)預(yù)測平均誤差為8.62%,而GA-BP神經(jīng)網(wǎng)絡(luò)預(yù)測平均誤差僅為1.65%。
[Abstract]:Magnesium alloy has many advantages such as high specific strength, low density, good electromagnetic shielding and shock absorption. As a lightweight material, magnesium alloy has been widely used in various industries, but its poor corrosion resistance seriously limits its application. In order to improve the corrosion resistance of magnesium alloys, surface treatment is one of the most simple and effective methods. In this paper, WE43 rare earth magnesium alloy was used as the research object, the ceramic film was prepared by laser remelting and micro-arc oxidation, and the numerical model of temperature field in laser remelting process was established based on ANSYS platform. The evolution law of temperature field under different technological parameters was analyzed, and the microstructure and corrosion resistance of the ceramic film were studied. The prediction model between the process parameters of micro-arc oxidation and the thickness of the film is established based on genetic algorithm optimization error back-propagation neural network. The main work is as follows: 1. The temperature field of laser remelting is numerically simulated by using ANSYS finite element analysis software. The dynamic process of laser remelting is simulated by parameterized design language, and the distribution of temperature field with time is analyzed. The microstructure of ceramic film prepared by laser remelting and micro-arc oxidation process and single micro-arc oxidation process was analyzed by means of SEM XRD. The results show that the microcracks and holes in the film section of the laser remelting / microarc oxidation composite process are less than that of the single micro-arc oxide film, and at the same time, The porosity of the film surface of laser remelting / micro-arc oxidation composite process is about 1 / 2 lower than that of the single micro-arc oxide film, and the density of the film is improved by .3. The corrosion resistance of ceramic film prepared by laser remelting and micro-arc oxidation and single micro-arc oxidation was analyzed by electrochemical workstation and immersion experiment. The results show that the self-corrosion current density of the micro-arc oxide film after laser remelting is one order of magnitude lower than that of the single micro-arc oxide film. The electrochemical impedance spectrum of the micro-arc oxide film after laser remelting is much larger than that of the single micro-arc oxide film, and the corrosion time of the micro-arc oxide film after laser remelting is much longer than that of the single micro-arc oxide film. All these indicate that the composite process of laser remelting and micro-arc oxidation is superior to single micro-arc oxidation process in improving corrosion resistance of magnesium alloy. The thickness of the film was measured under different parameters (current, pulse width, oxidation time). The genetic algorithm (GA) optimization error back-propagation (BP) neural network was used to establish the GA-BP prediction model between the process parameters of micro-arc oxidation and the thickness of the film. The results show that the GA-BP model has better function approximation ability and better generalization ability under the same training samples and test samples. The average prediction error of BP neural network is 8.62 and that of GA-BP neural network is only 1.65.
【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG178

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