WC体积分数对WC_p/Fe复合材料组织及压缩性能的影响

为研究WC体积分数对WC_p/Fe复合材料组织、界面及压缩性能的影响,采用粉末烧结法制备了不同WC体积分数的WC_p/Fe复合材料。结果表明:在不同WC体积分数的WC_p/Fe复合材料中,WC颗粒发生了不同程度的溶解,其与基体间均呈冶金结合。随着WC体积分数的增加,WC_p/Fe复合材料的界面等效宽度呈现递减趋势,当WC体积分数为50%时,界面等效宽度最小,为39.8μm;复合材料的压缩强度呈先增大后减小趋势,当WC体积分数达到45%时,压缩强度达到最大值;复合材料的断裂方式由准解理断裂逐渐转向纯解理断裂,当WC体积分数达到50%时,WC_p/Fe复合材料的断裂方式为纯解理断裂。     

WC颗粒增强金属基复合耐磨材料制备工艺与性能研究

与传统单一的材料相比,增强金属基复合材料(MMCs)的力学、物理和机械加工性能具有许多优点和更加优异的性能,在各种工程领域中应用广泛。首先从制备工艺开始,介绍了目前发展较为迅速的冷喷涂技术、激光熔覆、等离子堆焊及电弧堆焊等工艺发展。在此基础上着重论述了微米WC颗粒添加及纳米WC颗粒添加金属基耐磨材料性能的研究,论述了提高微米WC颗粒增强金属基复合耐磨材料耐磨性的途径,通过增加基体组织韧性,增加WC颗粒包裹、支撑,减少裂纹产生。进而介绍了纳米WC颗粒改变凝固形式,细化复合材料晶粒从而提高性能,并指出了纳米WC颗粒烧损是制约其发展的重要原因。最后,对该方向研究进展进行了总结,并对其发展前景和主要发展方向进行了展望。     

(AIBO)w/Al复合材料制备工艺研究

为减少传统挤压铸造工艺制备金属基复合材料步骤,降低成本,用湿法制备了硼酸铝晶须预制件,并用液-固挤压法制备晶须含量为20%(体积分数)的硼酸铝晶须增强铝基复合材料((AlBO)w/Al).通过正交实验法对预制件及复合材料的制备工艺参数进行设计及筛选,并采用光学显微镜、扫描电镜等对制备的(AIBO)w/Al微观组织进行观察分析.得到预制件及复合材料制备的最佳工艺参数,发现硼酸铝晶须在基体铝合金中的分布较为均匀,与挤压铸造法相比,该方法可减少工序,降低成本.     

WC/铁基表面复合材料的热疲劳裂纹形成过程

为了对高热疲劳性能的表面复合材料的设计提供理论依据 , 采用热震试验方法对通过真空实型铸渗(V2 EPC) 方法制备的 WC/铁基表面复合材料的热疲劳性能进行了研究 , 重点讨论了热疲劳裂纹的形成机制。研究结果表明 , 随着 WC颗粒体积分数的增加 , 表面复合材料的热疲劳性能有所改善 , 当 WC体积分数达到 52 %时 , 复合层表面在经过 10次热循环后能保持完好。热疲劳裂纹扩展机制研究表明 , 陶瓷 WC颗粒增强铁基表面复合材料的热疲劳裂纹的产生和扩展是由 WC和基体本身的热应力和二者界面交变循环应力共同交互作用的结果 , 可以通过选用高热导率的陶瓷颗粒作为增强体、 改善陶瓷颗粒本身的微观质量和采用与陶瓷颗粒热膨胀系数相接近的基体等方法提高复合材料的热疲劳性能。      

选区激光熔化制备颗粒增强金属基复合材料的研究进展

选区激光熔化是一种使用聚焦高能激光束熔化粉末,逐层叠加成形零件的增材制造方法.选区激光熔化可以直接制备复杂结构零件和实现近净成形,能够方便地通过粉末预混添加或原位反应实现颗粒增强金属基复合材料的控形控性,具有独特的技术优势,受到广泛关注.本文综述了选区激光熔化制备颗粒增强金属基复合材料的研究进展,总结了主要研究结果及存在的共性问题,并展望了选区激光熔化制备颗粒增强金属基复合材料的研究方向和发展趋势.通过总结分析,指出选区激光熔化制备颗粒增强金属基复合材料时,聚焦激光作用下形成的高温微小熔池凝固时间短,远远偏离平衡状态,凝固过程复杂,增强颗粒与基体间冶金反应剧烈,容易熔化、分解和溶解并对基体特性产生影响,进而影响成形后的复合材料的宏观形貌和组织、性能.除增强体成分、颗粒形貌与尺寸、体积分数外,复合材料的性能还受激光功率、扫描速度、扫描间距、粉层厚度、成形气氛等工艺参数的影响,粉末特性与工艺参数之间的交互作用复杂.因此,考察工艺参数与粉末特性之间的交互作用关系,系统研究增强体颗粒特性与成形工艺参数对复合材料宏观形貌、致密度、缺陷、组织和性能的影响规律,是实现复合材料组织结构设计和性能调控的基础.     

亚麻落麻纤维增强可降解复合材料的拉伸强度预测

采用非织造结合热压成型工艺制备了亚麻落麻纤维增强聚乳酸(PLA)基可降解复合材料(亚麻落麻/PLA),研究了纤维体积分数对材料拉伸强度的影响,并利用 Kelly-Tyson拉伸强度预测模型及相关修正理论,提出了非连续植物纤维增强可降解复合材料(D-NFRBC)强度预测模型,该模型考虑了纤维长度、取向角、直径、强度概率分布及材料界面剪切强度与材料中纤维临界长度、纤维极限拉伸强度三者间制约关系对复合材料强度的影响。结果表明;亚麻落麻/PLA拉伸强度在纤维体积分数为39.6%时达到最大,应用本文建立的强度预测模型所得亚麻落麻/PLA拉伸强度预测值与实验值吻合良好。     

颗粒种类及制备工艺对铜基材料性能影响

以纯铜为基体,以WC、AlN、TiN、MgB2等具有不同导电性能与密度的陶瓷颗粒为增强相,采用粉末冶金工艺制备了WCp/Cu、AlNp/Cu、TiNp/Cu和MgB2p/Cu系列复合材料.研究了不同增强颗粒、制备工艺的不同环节对铜基复合材料导电性能的影响.结果表明:相同制备工艺及体积分数条件下,以具有不同导电性能与密度的陶瓷颗粒作为增强相的铜基复合材料的导电性能相近,混粉、压制、烧结、复压及复烧等工艺环节对铜基复合材料导电性能有不同程度的影响,提高铜基复合材料的致密度为提高其导电性能的关键.     

（AlBO）w／Al复合材料制备工艺研究

为减少传统挤压铸造工艺制备金属基复合材料步骤，降低成本，用湿法制备了硼酸铝晶须预制件，并用液一固挤压法制备晶须含量为20％（体积分数）的硼酸铝晶须增强铝基复合材料（AlBO）w／Al）．通过正交实验法对预制件及复合材料的制备工艺参数进行设计及筛选，并采用光学显微镜、扫描电镜等对制备的（AlBO）w／Al微观组织进行观察分析．得到预制件及复合材料制备的最佳工艺参数，发现硼酸铝晶须在基体铝合金中的分布较为均匀，与挤压铸造法相比，该方法可减少工序，降低成本．     

二氧化锡颗粒增强银基复合材料的电阻率

根据复合材料的传导理论ｇ－ＭｅＯ系金属基复合材料电阻率的计算模型，并与Ａｇ－Ｓ的实验结果进行了对比。结果分析表明，颗粒增强金属基复合材料的电阻率不仅与基体和颗粒材料的电阻率及颗粒的体积分数有关，还与颗粒粒度及在基体中的分布状态有关。     

不同陶瓷颗粒增强Cu基复合材料的制备及导电性能

以纯铜为基体,以WC、AlN、TiN、MgB2等具有不同导电性能与密度的陶瓷颗粒为增强相,采用球磨-冷压-烧结工艺制备了WCp/Cu、AlNp/Cu、TiNp/Cu和MgB2p/Cu系列复合材料.研究了制备工艺的不同环节对铜基复合材料导电性能的影响,讨论了不同陶瓷颗粒增强铜基复合材料的导电性能.结果表明相同制备工艺及体积分数条件下,以具有不同导电性能与密度的陶瓷颗粒作为增强相的铜基复合材料的导电性能相近,球磨、冷压、烧结、复压及复烧等工艺环节对铜基复合材料导电性能有不同程度的影响,提高铜基复合材料的致密度为提高其导电性能的关键.     

Influence of reinforcing particles on microstructure and mechanical properties of friction stir processed aluminium alloy AA7075-T651

This paper emphasis the improvement of mechanical properties of AA7075-T651 using friction stir processing through localized surface modification by adding nano boron carbide particles. The reinforcement techniques such as the groove and blind hole methods were used by changing reinforcements of nano boron carbide and a matrix of AA7075-T651 surface composites volume percentages (2 %, 4 %, and 6 %) along with tool rotational speed and processing speeds. Optical microscopy, scanning electron microscope and x-ray diffraction analysis were used to examine the particle dispersion for the surface composites and to correlate with the enhanced mechanical properties. Results revealed that high input parameters have given grain coarsening and precipitate agglomeration and low input parameters provide poor nugget metal consolidation and no vertical material flow. The L9 orthogonal Array designed and optimized the process parameters for enhancing the surface properties of processed samples. Mechanical properties like ultimate tensile strength, yield strength, hardness, percentage of elongation and impact strength were evaluated for the groove friction stir processing method and blind-hole friction stir processing methods. From the results, it has been observed that the blind-hole technique resulted in higher hardness and the homogenous dispersion of nano boron carbide particles in the stir zone than the groove method. Consequently, for blind-hole friction stir processing, grey relational analysis (GRA) and particle swarm optimization (PSO) approaches were proposed to optimise process parameters. From the compared optimization results between grey relational analysis and particle swarm optimization, particle swarm optimization approach was shown the best optimization results. Successively, the optimum condition in the respective experimentation is accomplished. Based on these observation and results, final validation tests were carried by changing the volume percentages of reinforcement keeping tool rotation speed and tool processing speed as constant. It is apparent that dynamic recrystallization in aluminium alloy at the processed zone due to presence of heterogeneous nucleation sites with nano boron carbide particles.     

神经网络模型在SiC涂层制备中的应用

材料表面抗氧化涂层的质量是限制碳／碳复合材料作为高温结构材料使用的关键．本文运用人工神经网络技术建立了CVD－SiC涂层制备工艺的过程模型,以解决该过程影响因素众多、相互作用关系复杂、难以对制备过程进行有效的预测和控制的问题．研究结果表明：所建立的神经网络模型,可以比较准确和全面地反映各工艺因素对SiC-CVD过程的影响大小及内在规律;模型对工艺参数与沉积速率之间关系的预测与实验结果相吻合;证实了将人工神经网络模型应用于抗氧化涂层的制备过程的控制和工艺优化是有效和可行的．     

Optimization of Machining Characteristics for Al／SiCp Composites using ANN／GA

The present work is focused on optimization of machining characteristics of Al/SiCp composites.The machining characteristics such as specific energy,tool wear and surface roughness were studied.The parameters such as volume fraction of SiC,cutting speed and feed rate were considered.Artificial neural networks(NN) was used to train and simulate the experimental data.Genetic algorithms(GA) was interfaced with ANN to optimize the machining conditions for the desired machining characteristics .Validation of optimized results was also performed by confirmation experiments.     

基于相空间重构和遗传优化SVR的机械设备状态趋势预测

针对机械设备振动信号序列的非线性、非平稳性特点,提出了一种基于相空间重构与遗传优化支持向量回归机的设备状态趋势预测方法。首先,采用相空间重构技术将一维振动信号时间序列转化成矩阵形式,自适应地选取特征,以相点作为输入特征训练SVR预测器;然后应用自适应遗传算法对惩罚因子、不敏感系数以及高斯核宽度进行同步优化,自动获取最佳的建模参数;最后构建SVR预测模型,并将其应用于某机组振动信号预测。实验结果表明,无论是单步还是24步预测,本文所提遗传优化SVR模型的预测精度都要比标准SVR模型的预测精度高,说明该方法对机械设备的运行状态趋势具有较好的预测能力。     

SiCp/Al复合材料制孔崩边缺陷及其评价方法

对中高体积分数碳化硅颗粒增强铝基（SiCp/Al）复合材料制孔崩边缺陷进行了三维试验观测和统计分析,建立了制孔崩边缺陷模型,提出了一种基于崩边体积的SiCp/Al复合材料崩边缺陷程度评价方法。研究结果表明：崩边缺陷是SiCp/Al复合材料制孔的主要缺陷,且崩边缺陷主要产生在制孔出口处。采用该方法能够对不同参数下的制孔崩边缺陷程度进行客观真实的评价,对优化制孔工艺参数具有一定的指导意义。     

Predictive modelling of turning operations using response surface methodology,artificial neural networks and support vector regression

This paper focuses on developing empirical models for predicting surface roughness, tool wear and power required in turning operations. These response parameters are mainly dependent upon cutting velocity, feed and cutting time. Three competing data mining techniques, response surface methodology (RSM), artificial neural networks (ANN) and support vector regression (SVR), are applied in developing the empirical models. The data of 27 experiments have been used to generate, compare and evaluate the proposed models of tool wear, power required and surface roughness for the selected tool/material combination. Testing results demonstrate that the models developed in this research are suitable for predicting the response parameters with a satisfactory goodness of fit. It has been found that ANN and SVR models are much better than regression and RSM models for predicting the three response parameters. Finally, some future research directions are outlined.     

Formulation optimization technique based on artificial neural network in salbutamol sulfate osmotic pump tablets

The aim of this study was to develop a formulation optimization technique in which an artificial neural network (ANN) was incorporated; 30 kinds of salbutamol sulfate osmotic pump tablets were prepared, and their dissolution tests were performed. The amounts of hydroxypropyl methylcellulose (HPMC), polyethylene glycol 1500 (PEG1500) in the coating solution, and the coat weight were selected as the causal factors. Both the average drug release rate v for the first 8 hr and the correlation coefficient r of the accumulative amount of drug released andtime were obtained as release parameters to characterize the release profiles. A set of release parameters and causal factors was used as training data for the ANN, and another set of data was used as test data. Both sets of data were fed into a computer to train the ANN. The training process of theANN was completed until a satisfactory value of error function E for the test data was obtained. The optimal formulation produced by the technique gave the satisfactory release profile since the observed results coincided well with the predicted results. These findings demonstrate that an ANN is quite useful in the optimization of pharmaceutical formulations.     

Formulation Optimization Technique Based on Artificial Neural Network in Salbutamol Sulfate Osmotic Pump Tablets

The aim of this study was to develop a formulation optimization technique in which an artificial neural network (ANN) was incorporated; 30 kinds of salbutamol sulfate osmotic pump tablets were prepared, and their dissolution tests were performed. The amounts of hydroxypropyl methylcellulose (HPMC), polyethylene glycol 1500 (PEG1500) in the coating solution, and the coat weight were selected as the causal factors. Both the average drug release rate v for the first 8 hr and the correlation coefficient r of the accumulative amount of drug released andtime were obtained as release parameters to characterize the release profiles. A set of release parameters and causal factors was used as training data for the ANN, and another set of data was used as test data. Both sets of data were fed into a computer to train the ANN. The training process of theANN was completed until a satisfactory value of error function E for the test data was obtained. The optimal formulation produced by the technique gave the satisfactory release profile since the observed results coincided well with the predicted results. These findings demonstrate that an ANN is quite useful in the optimization of pharmaceutical formulations.     

Microstructural evolution and mechanical properties of SiC/Al2O3 particulate-reinforced spray-deposited metal-matrix composites

In this study, aluminium-copper-based metal-matrix composites were synthesized utilizing the spray atomization and co-deposition technique. Microstructural characterization studies were carried out with an emphasis on understanding the effects associated with the co-injection of silicon carbide and aluminium oxide particulates. The results demonstrate the ageing kinetics of the spray-deposited and hot-extruded metal-matrix composites to be the same as those of the monolithic aluminium-copper material. Results of ambient temperature mechanical tests demonstrate that the presence of particulate reinforcement in the metal matrix does little to improve strength, and degrades the ductility of the matrix material. A model is formulated to compute the critical volume fraction of reinforcement. The results obtained using this model suggest that an optimum volume fraction of silicon carbide is essential in order to realize a strength improvement in the metal-matrix composite, relative to their monolithic counterpart.     

Studies on machinability of Al/Si<Subscript>p</Subscript> + SiC<Subscript>p</Subscript> composite materials

This paper presents the experimental results on the machinability of silicon and silicon carbide particles (SiC_p) reinforced aluminium matrix composites (Al/Si_p + SiC_p) during milling process using a carbide tool. The total volume fraction of the reinforcements is 65 vol%. The milling forces, flank wear of the tool and the machined surface quality of composites with different volume fraction of SiC_p were measured during experiments. The machined surfaces of composites were examined through SEM. The results showed that the flexural strength and Vickers hardness are improved when certain volume fraction of silicon particles are replaced by silicon carbide particles with the same volume fraction and particle size and the effect of SiC_p on machinability is optimal when 9 vol% silicon particles in Al/Si_p was replaced by silicon carbide particles with the same volume fraction and the same particle size. Cracks and pits were found on the machined surfaces of composites due to the intrinsic brittleness of silicon particles.