人工神经网络用于炭／炭复合材料CVD SiC涂层的研究

炭／炭复合材料抗氧化涂层是该材料可否用于高温结构件的关键。将人工神经网络用于CVDSiC抗氧化涂层的建模,试图解决该过程影响因素复杂、数学模型难于建立、无法对整个过程进行有效预测和控制的问题。初步探讨表明,采用人工神经网络建模,可以比较确切与全面地反映CVDSiC涂层过程的影响因素和内在规律,预测的工艺参数与沉积速率的关系同实验结果吻合较好,由此证实了将人工神经网络应用于抗氧化涂层制备系统是可行的。     

SiC-CVD过程的人工神经网络建模

ＳｉＣ－ＣＶＤ过 本质复杂性制约 了碳／碳复合材料表面有效地制备高性能抗氧化涂层。本研究在对ＣＶＤ工艺过程及机理试验研究的基础上,采用人工神经网络技术对ＳｉＣ－ＣＶＤ过程进行了辨识与模拟研究,建立了ＳｉＣ－ＣＶＤ过程的神经网络的模型。     

涂层工艺对C/C复合材料结构和弯曲性能的影响

采用热处理和包埋工艺制备了Ｃ／Ｃ复合材料的ＭｏＳｉ２／ＳｉＣ抗氧化涂层,对组织结构、界面、弯曲断口进行了显微观察,分析了氧化保护涂层及其工艺对其机械性能的影响,结果表明,该工艺在Ｃ／Ｃ复合材料表面生成涂层的同时,使基材内部的界面也被硅化;并且发现,热解炭基体比炭纤维更易与Ｓｉ反应生成ＳｉＣ。Ｃ／Ｃ复合材料经涂层工艺处理后,弯曲强度降低;热处理过程中发生的材料氧化是弯曲强度下降的主要原因     

含炭耐火材料中Si与SiC抑制碳氧化的机理

本文报导了含炭耐火材料氧化过程中Ｓｉ、ＳｉＣ所发生的氧化还原反应。脱碳层中Ｓｉ和ＳｉＣ所发生的氧化还原反应对含炭耐火材料的抗氧化性起着重要作用。文中解释了在反应ＳｉＣ＋２ＣＯ→ＳｉＯ２十３Ｃ不能进行热力学温度内，ＳｉＣ仍能对含炭耐火材料中碳的氧化有抑制作用的原因．从而进一步阐明了Ｓｉ和ＳｉＣ通过ＳｉＣ气相扩散到脱碳层，再氧化生成ＳｉＯ２，沉积在气孔壁上，堵塞气孔的抗氧化机理．     

含炭耐火材料添加Al4SiC4的作用

Ａｌ４ＳｉＣ４是一种极好的抗水化化合物，它作为含炭耐火材料的抗氧化剂，对其性能用作用进行了研究，并探讨了相应机理。Ａｌ４ＳｉＣ４添加到含炭耐火材料中，起初与ＣＯ反应，生成Ａｌ２Ｏ３、ＳｉＣ和Ｃｏ反应后，如果温度在－１５６０℃以下，生成的ＳｉＣ和Ａｌ２Ｏｅ将进一步与ＣＯ应生成莫来石（Ａｌ６ＳｉＣ２Ｏ１３）和Ｃｏ。在耐火材料表面进行的上述反应形成了保护层，这就阻止了耐火材料的氧化。为此，Ａｌ４ＳｉＣ４     

炭/炭复合材料抗氧化研究现状

总结了炭/炭复合材料抗氧化研究现状,重点介绍了抗氧化涂层的制备方法,包括包埋浸渍法、化学气相沉积(CVD)、溶胶-凝胶法(Sol-Gel)和水热电沉积法以及近几年针对抗氧化涂层开裂问题各国学者的最新研究成果。提出了炭/炭复合材料抗氧化研究今后努力的方向。     

炭材料的高温抗氧化性研究进展

介绍了炭材料的氧化过程,综述了国内外目前提高炭材料抗氧化性能的两个主要途径;基体改性技术和涂层技术。重点阐述了抗氧化涂层的基本要求,涂层的制备方法以及各涂层体系,提出了对于炭材料高温抗氧化性研究方面的一些认识,阐明了其发展前景。     

炭／炭复合材料高温抗氧化研究的现状

对炭／炭复合材料的高温抗氧化研究现状进行了全面介绍,重点侧重于抗氧化涂层制备方法及研究的最新进展。在此基础上,提出了1650℃以上抗氧化涂层研究的新思路。     

炭/炭复合刹车材料的防氧化技术研究进展

从炭／炭复合材料氧化特点、抗氧化要求出发，综述了基体改性技术和涂层技术，对炭／炭复合材料防氧化研究进展进行了详细的介绍，提出了对其高温抗氧化研究方向的一些观点。     

C-B4C-SiC与Ti组分的原位反应及热压烧结

以炭黑、炭纤维、Ｂ４Ｃ、ＳｉＣ、Ｓｉ、ＴｉＯ２和ＴｉＣ为原料制备了不同ＴｉＯ２和ＴｉＣ含量的炭／陶复合材料,采用原位合成及热压技术研究了不同ＴｉＯ２和ＴｉＣ含量对多组分炭／陶复合材料的组成、结构和性能的影响。在烧结过程中ＴｉＯ２和ＴｉＣ与Ｂ４Ｃ反应原位生成ＴｉＢ２,Ｓｉ和ＴｉＯ２分别与Ｃ反应生成ＳｉＣ和ＴｉＣ,这些陶瓷相的生成对提高炭／陶复合材料的力学性能有显著作用。加入ＴｉＯ２能使炭／陶复合材料在较低的温度下实现致密化烧结,获得了抗弯强度达４３０ＭＰａ的炭／陶复合材料     

Rolling-Contact Fatigue and Wear of CVD-SiC with Residual Surface Compression

The rolling-contact fatigue life and wear of CVD-SiC coatings with surface compression were studied using a three- ball-on-rod rolling-contact fatigue (RCF) tester. Two levels of surface compression in the coatings, approximately 190 μm thick, were introduced by using substrates of a liquid-phase sintered SiC and a SiC-30 vol% TiC to obtain desired thermal-expansion mismatches with the CVD-SiC. Residual surface compressions of 250 and 680 MPa were measured in the CVD-SiC coatings on SiC and SiC-30 vol% TiC substrates, respectively, by a strain-gage technique. In the RCF test, the fatigue life of the CVD-SiC coating with the moderate surface compression was limited by severe wear by a brittle fracture mechanism at a Hertzian contact stress of 5.5 GPa. The CVD-SiC coating on the SiC-30 vol% TiC composite, on the other hand, did not show measurable wear. A majority of the tests (11 out of 16) were suspended at 100 h or stopped due to failure of the steel balls. Five tests stopped due to spalling of the CVD-SiC coating at weak interfaces 10 to 15 μm below the contact surface. Examination of the microstructure of the CVD-SiC coating in cross sections revealed that the weak interfaces that led to the spalling were related to discontinuous growth of the CVD-SiC in the form of nodules or growth regions. Elimination of these defective structures is likely to enhance the tribological performance of surface-toughened CVD-SiC.     

Oxidation resistance of highly porous CVD-SiC coated Tyranno fiber composites

Oxidation tests have been carried out on highly porous ceramic matrix fiber composites consisting of Tyranno Lox M fibers coated with Pyro C and CVD-SiC thin layers. TGA experiments carried out at 900 °C confirm that mass loss rates are higher for materials with thicker Pyro C layers. At higher temperatures (i.e. 1250 °C) such differences are not significant likely due to the interaction between SiC oxidation and Pyro C burnout. These tests clearly show that the oxidation kinetics of Tyranno fibers are much faster than those of CVD-SiC coatings. Therefore, the CVD-SiC coatings protect the Tyranno fibers against oxidation, although this is less effective as the thickness of the Pyro C layer increases. Finally, it has been found that the oxidation kinetics of the CVD-SiC layers are faster as the coating thickness increases and are different for the inner and the outer coating surfaces.     

Application of artificial neural networks to predict corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition

A feed-forward, multilayer perceptron artificial neural network (ANN) model with eight hidden layers and 12 neurons was used to predict the corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition. The effect of process parameters, namely, ultrasonic power, SiC particle concentration, and current density, on the weight losses of Ni–SiC composite coatings was investigated. The grain sizes of Ni and SiC were determined by using X-ray diffraction (XRD) and scanning probe microscopy (SPM). Results indicate that ultrasonic power, SiC particle concentration, and current density have significant effects on the weight losses of Ni–SiC composite coatings. The ANN model, which has a mean square error of approximately 3.35%, can effectively predict the corrosion behavior of Ni–SiC composite coatings. The following optimum conditions for depositing Ni–SiC composite coatings were determined on the basis of the lowest weight loss of Ni–SiC deposits: ultrasonic power of 250 W, SiC particle concentration of 8 g/l, and current density of 4 A/dm². XRD and SPM results demonstrate that the average grain sizes of Ni and SiC in the Ni–SiC composite coating are 90 and 70 nm, respectively.     

RP-HPLC optimization of econea by using artificial neural networks and its antifouling performance on the Turkish coastline

Coverage of artificial surfaces within seawater by fouling organisms is defined as biofouling. Although biofouling is a natural process, it has some disadvantages for shipping industry such as increased fuel consumption, and CO₂ emission. Therefore, the ships' hull must be covered by antifouling (AF) or fouling release type coatings to overcome biofouling. In general, the so-called self-polishing AF paints contain biocides for preventing fouling organisms. Their concentrations and release rates from AF coatings are of great importance and they definitely affect both quality and cost of the coating. In the present study, we aimed at applying a new robust method. In this method, we used a model biocide, i.e., econea, to obtain its RP-HPLC optimization through artificial neural networks (ANN) and to see its antifouling performance. Column temperature, mobile phase ratio, flow rate, concentration and wavelength as input parameters and retention time as an output parameter were used in the ANN modeling. In conclusion, the R&D groups in AF paint industry may use RP-HPLC method supported with ANN modeling in further studies.     

Application of an Artificial Neural Network for Simulating Robust Plasma-Sprayed Zirconia Coatings

This article presents the application of the artificial neural network (ANN) of a statistically designed experiment for developing a robust wear-resistant zirconia coating. In this research, experimental design with orthogonal arrays efficiently provides enough information with the least number of experiments, reducing the cost and time. A radial basis function (RBF) network for the wear behavior is adopted. The friction and tribological properties of zirconia coatings were investigated. The microstructural feature of the coatings is also addressed in this study. It is found that the worn volumes of plasma-sprayed zirconia coatings after wear tests are greatly improved by the optimal parameters. The relationships between the microstructure of the worn surface and their properties are examined, and the results reveal a higher wear resistance and a lower worn surface roughness with a large amount of plastic deformations. These wear resistant structures formed as a result of a dense lamellar formation during sprayed zirconia coatings. The RBF network can be established efficiently. A comparison of the predicted results with that of the RBF network and the Taguchi method predictor shows average errors of 2.735% and 9.191% for the RBF network and the Taguchi method, respectively. It is experimentally confirmed that the RBF network predictions are in agreement with the experiments, and it can be reliably used for the prediction of wear for plasma sprayings. The experimental results demonstrate that the RBF network used for a statistically designed experiment is an effective, efficient, and intelligent approach for developing a robust, high efficiency, and high-quality zirconia coating process.     

Improved thermal shock resistance of SiCnw/PyC core-shell structure-toughened CVD-SiC coating

In-situ SiC nanowire (SiCnw)/pyrolytic carbon (PyC) core-shell structures were introduced to mainly improve the thermal shock performance of chemical vapor deposition (CVD)-SiC coating on carbon/carbon (C/C) composites. The microstructure, phase composition, and mechanical properties of the CVD-SiC coating toughened by SiCnw/PyC core-shell structures were studied as well. The results show that the introduction of SiCnw/PyC core-shell structures can effectively alleviate the mismatch of coefficient of thermal expansion (CTE) between SiC coating and C/C substrate, thus enhancing the thermal shock resistance of the coating. Furthermore, the increased numbers of interfaces in the SiC coating owing to the addition of core-shell structures are beneficial to the mechanical properties of the coating after thermal shock test.     

基于自适应神经网络的芳烃异构化过程建模

针对芳烃异构化过程(AHIP)中影响对二甲苯(PX)产率的因素众多且复杂等特点,提出一种自适应神经网络(Adaptive ANN)以建立AHIP的各因素与PX产率的关联模型.Adaptive ANN将样本分成训练样本和校验样本,并设计过拟合判据参数.通过训练样本对网络进行训练,训练过程中以模型对校验样本的预测性能为指标,通过过拟合判据参数的计算自适应地在获得具有最佳预测性能模型时终止网络训练,克服了传统的神经网络以模型的拟合精度为指标,造成训练时间过长和过拟合等缺点.     

C/C复合材料抗氧化涂层研究进展

结合国内外近几年的研究报道，介绍了C/C复合材料抗氧化涂层的基本要求及类别、各涂层体系及制备方法，并简要阐述了抗氧化涂层的研究方向。     

水性环氧涂料用固化剂的探索

水性环氧涂料是水性涂料发展的重要品种之一,水性环氧涂料用固化剂是影响其涂料性能的主要因素之一,因此开发新型水性环氧涂料用固化剂则别具现实意义.文中对水性环氧涂料用固化剂的发展历程作了探讨,并对各类水性环氧涂料用固化剂的特点进行扼要的解说,从中探索出水性胺加成物固化剂是水性环氧涂料用固化剂中最具发展前景的品种.     

纳米针状羟基磷灰石涂层的制备及其性能的研究

用电化学沉积法在钛合金上形成涂层,随后在碱性过热蒸气中进行水热处理,最后进行高温真空烧结。用扫描电镜,Ｘ射线衍射和红外光谱对涂层的组织结构和化学成分进行了分析,测量了涂层的粘接拉伸强度,将涂层浸泡于Ｔｙｒｏｄｅ生理盐液中考虑其抗溶解特性。