The superplasticity and microstructure evolution of TC11 titanium alloy

The superplasticity is the capability of some metallic materials to exhibit very highly tensile elongation before failure. The superplastic tensile tests were carried out at various deformation conditions in this paper to investigate the superplastic behaviors and microstructure evolution of TC11 titanium alloy. The results indicate that the smaller the grain size, the better the superplasticity is, and the wider the superplastic temperature and strain rate is, in which the superplastic temperature is ranging from 1023 to 1223 K and the strain rate is ranging from 4.4 × 10^?5 to 1.1 × 10^?2 s^?1. The maximum tensile elongation is 1260% at the optimum deformation conditions (1173 K and 2.2 × 10^?4 s^?1). For further enhancing the superplasticity of TC11 titanium alloy, the novel tensile method of maximum m superplastic deformation is adopted in the paper. Compared with the conventional tensile methods, the excellent superplasticity of TC11 titanium alloy has been found with its maximum elongation of 2300%.     

基于神经网络遗传算法的GFRP拉挤双目标优化

为研究玻璃钢(GFRP)拉挤工艺参数对复合材料性能的影响,优化最佳拉挤工艺参数,建立了拉挤工艺过程数学模型,结合基于有限元/有限差分的间接解耦法进行求解,模拟得到了拉挤过程中GFRP内部的非稳态温度场和固化度变化情况.分别采用布拉格光栅光纤温度传感器和索氏萃取法检测拉挤GFRP内部的温度与固化度,实测温度和固化度均与模拟温度和固化度吻合,验证了数值模拟程序的正确性.以数值模拟结果为样本,建立反向传播神经网络,得到拉挤工艺参数(固化温度、拉挤速度)与GFRP固化度之间的非线性相关关系,再结合遗传算法解决拉挤过程中固化炉温度和拉挤速度双目标优化问题.优化得到的拉挤工艺参数可在保证复合材料固化度达标的情况下,提高拉挤速度降低固化炉温度,优化效果显著.神经网络遗传算法优化方法能有效解决此类具有复杂非线性关系的多目标优化问题.     

基于遗传算法和神经网络的C/C复合材料等温CVI工艺参数优化模型

建立了基于遗传算法和误差反传（GA-BP）神经网络的化学气相渗透（CVI）工艺参数优化模型。以新型等温CVI工艺制备C/C复合材料时采集的实验数据作为模型评价样本,分析了主要可控影响因素（沉积温度、前驱气体分压与滞留时间等）对C/C复合材料制件密度及其密度均匀性的作用规律。在该模型指导下,样本的期望密度和实测密度最大误差不超过6.2%,密度差最大误差不超过8.2%。实验结果也证明了该模型具有较高的精度和良好的泛化能力,可以用于CVI工艺参数的优化。      

Effect of temperature on deformation behavior and microstructures of TC11 titanium alloy

The isothermal compression deformation behavior of TC11 titanium alloy with beta microstructure was studied between 750 °C and 1100 °C under the strain rate ranging from 0.001 s⁻¹ to 10 s⁻¹ by THERMECMASTOR-Z simulator. In addition, the effect of temperature on microstructure was observed using optical microscope. The results showed that the temperature greatly affected the flow stress and microstructure of TC11 titanium alloy cooled from beta phase region in air. During hot deformation of TC11 titanium alloy, the steady state flow characteristic was observed at higher temperature or lower strain rate. In the α + β phase region, spheroidization fraction of α lamellar decreased with increasing temperature, while in near-β and β phase regions, dynamic recrystallization fraction increased with increasing temperature in all strain rates except at the strain rate of 0.001 s⁻¹.     

Deformation behavior in isothermal compression of the TC11 titanium alloy

Isothermal compression of the TC11 titanium alloy has been conducted on Gleebe-1500 hot-simulator at the deformation temperatures ranging from 1023 K to 1323 K, the strain rates ranging from 0.001 s⁻¹ to 10.0 s⁻¹, and the height reductions ranging from 50% to 70%. The effect of deformation temperature, strain rate and strain on the flow stress and the apparent activation energy for deformation is in depth analyzed. The experimental results show that the apparent activation energy for deformation in isothermal compression of the TC11 titanium alloy decreases with the increasing of strain. Moreover, the apparent activation energy for deformation in α + β two-phase region of the TC11 titanium alloy increases with the increasing of deformation temperature and decreases with the increasing of strain rate. A power dissipation efficiency map in isothermal compression of the TC11 titanium alloy is constructed at a strain of 0.6, in which three domains with higher power dissipation efficiency are observed, and deformation characteristics of the above-mentioned domains are analyzed. Finally, optical micrographs of the TC11 titanium alloy obtained on a Leica DMLP microscope showed the evidence of deformation in three domains.     

基于BP网络和遗传算法的枪架结构优化

为提高机枪的射击精度,需要对机枪枪架的结构参数进行优化设计。基于CAE软件的结构优化有其自身的局限性,所以提出了一种新的优化方法,即联合应用BP网络和遗传算法。本文以此为基础,根据机枪有限元模型,对枪架结构参数进行了优化设计,结果表明：BP网络和遗传算法相结合的方式可适用于多设计变量的枪架参数优化设计,为其它复杂结构的参数优化提供了新的思路。     

基于遗传算法的神经网络被动声呐目标分类研究

被动声呐目标识别系统中目标分类器的设计和训练是一项重要内容，本文设计了目标分类器的神经网络结构，提出了一种用改进的遗传算法训练神经网络分类器的新方法，最后，对海上实录的Ａ，Ｂ，Ｃ三类目标噪声进行了分类识别，实验结果表明基于遗传算法的神经网络分类器比传统的基于ＢＰ算法的神经网络分类源泛化性能有明显提高。     

Microstructural evolution of a TC11 titanium alloy during linear friction welding

Linear friction welded titanium alloy microstructures were investigated to understand the microstructural evolution in the joints. The results show that extrusion of material at the rubbing interface occurred after complete transformation of alpha to beta phase. Finer prior-beta grains were obtained in the flash as compared to the parent material due to dynamic recrystallization. Metallographic examination revealed that two different structures were existed in the joints, i.e., thermomechanically affected zone developing at the edge of joint and weld appearing in the central portion of joint. Although no dynamic recrystallization was observed in the thermomechanically affected zone, the phase transformation would occur concurrently with material deformation during welding. In contrast, dynamic recrystallization had occurred in the weld. Effect of welding parameter on the microstructure was investigated by changing amplitude of oscillation (1.56–2.03 mm). Some defects such as kiss bonding and porosity occurred in the joint at relatively low amplitude of oscillation. Therefore, relatively high amplitude of oscillation is more preferable for obtaining a fully bonded joint.     

基于神经网络趋势分析

文章在分析研究了国内外现状的基础上 ,利用神经网络的非线性处理特性 ,提出了通过神经网络预测常见机械零件剩余寿命的方法 ,用实例验证了其有效性     

基于遗传神经网络的产品配色设计

由于科技的进步和消费者意识的提高,加快了产品的生命周期,如何帮助设计师掌握消费者的感觉,提高产品创新的时效性,是一个不容忽视的问题。从感性工学的理论和知识出发,将目标产品(电话计费器)划分为色块的组合构成,以配色理论制作配色意象问卷,运用倒传递类神经网络学习的特性模拟配色意象评价模式,再通过遗传算法调适与更新的特性搜寻最符合需求意象的配色组合。研究结果显示：通过实验,依据类神经学习与遗传演算推演出的配色组合方式,最终可以架构系统而得以顺利执行,并且提供配色子代的样本作为建议。由本研究的结果可建立一个评估色彩感性的决策支持系统,藉以加快设计流程,辅助设计师以比较有效率且客观的方式进行产品色彩设计。由于科技的进步和消费者意识的提高,加快了产品的生命周期,如何帮助设计师掌握消费者的感觉,提高产品创新的时效性,是一个不容忽视的问题。从感性工学的理论和知识出发,将目标产品(电话计费器)划分为色块的组合构成,以配色理论制作配色意象问卷,运用倒传递类神经网络学习的特性模拟配色意象评价模式,再通过遗传算法调适与更新的特性搜寻最符合需求意象的配色组合。研究结果显示：通过实验,依据类神经学习与遗传演算推演出的配色组合方式,最终可以架构系统而得以顺利执行,并且提供配色子代的样本作为建议。由本研究的结果可建立一个评估色彩感性的决策支持系统,藉以加快设计流程,辅助设计师以比较有效率且客观的方式进行产品色彩设计。     

TC11钛合金几种不同横梁位移速率下拉伸试验的K值关系分析

选取Φ5 mm的TC11钛合金圆棒拉伸试样,在3种不同型号的电子拉伸试验机上按照ISO 6892—1:2016中的方法A2选取不同的横梁位移速率进行室温拉伸试验,在试样上装夹引伸计,测量Fp0.2感兴趣点处和弹性阶段的80％Fp0.2处试样上的真实应变速率.通过比较试样平行长度上的名义应变速率与试样不同感兴趣点处的真实...     

Artificial neural network approach to predict the flow stress in the isothermal compression of as-cast TC21 titanium alloy

Isothermal compression of as-cast TC21 titanium alloy at the deformation temperatures ranging from 1000 to 1150 °C with an interval of 50 °C, the strain rates ranging from 0.01 to 10.0 s^?1 and the height reduction of 60% was conducted on a Gleeble-3500 thermo-mechanical simulator. Based on the experimental results, an artificial neural network (ANN) model with a back-propagation learning algorithm was developed to predict the flow stress in isothermal compression of as-cast TC21 titanium alloy. In the present ANN model, the strain, strain rate and deformation temperature were taken as inputs, and the flow stress as output. According to the predicted and experimental results, the maximum error and average error between the predicted flow stress and the experimental data were 4.60% and 1.58%, respectively. Comparison of the predicted results of flow stress based on the ANN model and those using the regression method, it was found that the relative error based on the ANN model varied from ?1.41% to 4.60% and that was in the range from ?13.38% to 10.33% using the regression method, and the average absolute relative error were 1.58% and 5.14% corresponding to the ANN model and regression method, respectively. These results have sufficiently indicated that the ANN model is more accurate and efficient in terms of predicting the flow stress of as-cast TC21 titanium alloy.     

TC11微弧氧化膜制备及其结构性能研究

在Na2SiO3-Na2WO4-NaOH混合电解液中,利用微孤氧化(MAO)技术在TC11合金表面制备了氧化膜.用扫描电镜(SEM),X射线能谱仪(EDS),X射线衍射仪(XRD)对氧化膜微观结构、化学成分、厚度以及相组成进行了分析,采用HVS-1000维氏显微硬度计、MFT-4000划痕试验机、电化学测量系统完成氧化...     

超音速微粒轰击对TC11钛合金组织和疲劳性能的影响

采用超音速微粒轰击(SFPB)技术对层片组织的TC11钛合金进行表面纳米化处理,对比研究了表面纳米化处理前、后TC11钛合金的室温高周疲劳行为;借助光学显微镜(OM)、扫描电镜(SEM)、透射电镜(TEM)和X射线衍射仪(XRD)对比分析了高周疲劳断口及断口附近的微观组织形貌.结果表明:经SFPB处理后在钛合金表层产生了30～50μm厚的纳米层,纳米晶尺寸在5～15 nm左右;疲劳性能得到明显提高,在相同应力级别下的疲劳寿命提高了约8～10倍,疲劳条带宽度变窄,且随着加载级别的降低,疲劳寿命提高的倍数逐渐增加;SFPB前、后疲劳断口均由疲劳源区、裂纹扩展区、瞬断区三部分组成,但SFPB处理后的疲劳源由处理前的表层移至次表层;SFPB处理态试样疲劳加载后表层组织仍为纳米量级,但次表层组织中出现大量的形变孪晶、位错缠结以及少量的形变诱导马氏体组织.     

Optimization of laser welding process parameters for super austenitic stainless steel using artificial neural networks and genetic algorithm

The laser welding input parameters play a very significant role in determining the quality of a weld joint. The quality of the joint can be defined in terms of properties such as weld bead geometry, mechanical properties and distortion. In particular mechanical properties should be controlled to obtain good welded joints. In this study, the weld bead geometry such as depth of penetration (DP), bead width (BW) and tensile strength (TS) of the laser welded butt joints made of AISI 904L super austenitic stainless steel are investigated. Full factorial design is used to carry out the experimental design. Artificial neural networks (ANNs) program was developed in MatLab software to establish the relationship between the laser welding input parameters like beam power, travel speed and focal position and the three responses DP, BW and TS in three different shielding gases (argon, helium and nitrogen). The established models are used for optimizing the process parameters using genetic algorithm (GA). Optimum solutions for the three different gases and their respective responses are obtained. Confirmation experiment has also been conducted to validate the optimized parameters obtained from GA.     

Modelling beta transus temperature of titanium alloys using artificial neural network

An artificial neural network (ANN) model is developed to simulate the non-linear relationship between the beta transus (β_tr) temperature of titanium alloys and the alloy chemistry. The input parameters to the model consist of the concentration of nine elements, i.e. Al, Cr, Fe, Mo, Sn, Si, V, Zr and O, whereas the model output is the β_tr temperature. Good performance of the ANN model was achieved. The interactions between the alloying elements were estimated based on the obtained ANN model. The results showed good agreement with experimental data. The influence of the database scale on ANN model performance was also discussed. Estimation of β_tr temperature through thermodynamic calculation was carried out as a comparison.     

基于人工神经网络的异步电动机故障检测

为进一步提高异步电动机故障检测的准确性,将人工神经网络应用于异步电动机故障检测.通过提出一种基于BP神经网络的电机故障检测方法,设计了适合该检测系统的网络结构.仿真结果表明:相对于其他算法,该网络结构具有更快的学习速度和更高的学习精度,完全适用于电动机故障检测.     

基于人工神经网络的氮化锆薄膜颜色预测模型

建立了氮化锆薄膜制备工艺参数与薄膜色度参数之间的人工神经网络预测模型，结果表明，预测结果与实测结果吻合，最大色差在5．45以内。利用所建立的模型研究了单个参数对薄膜颜色的影响规律，及多参数间交互作用与薄膜颜色的关系。并且利用神经网络根据加工要求反向预测工艺参数，从而实现了对加工参数的优化选择。     

Bayesian regularisation neural network based on artificial intelligence optimisation

Stock prediction is generally considered to be challenging and known for its high noise and strong nonlinearities in financial time series analysis. However, current forecasting models ignore the importance of model parameter optimisation and the use of recent data. In this article, a novel forecasting approach with a Bayesian-regularised artificial neural networks (BR-ANN) was proposed. The weight of the proposed model (BR-ANN) is determined by the particle swarm optimisation (PSO) algorithm. Daily market prices and financial technical indicators are utilised as inputs to predict the one day future closing price of the Shanghai (in China) composite index. The Bayesian-regularised network uses a probabilistic nature for the network weights and can reduce the potential for over-fitting and over-training. Our empirical study and the results of our K-line theory analysis indicate that PSO is determined to be an effective algorithm to optimise the parameters of the Bayesian neural network compared with other well-known prediction algorithms. In particular, the PSO model is more reliable than the simple Bayesian regularisation neural network near the local maximum value.     

采用RBF网络预测含氢TC4合金的高温流变应力

研究了含氢TCA合金的热变形行为,基于径向基函数(RBF)人工神经网络建立了含氢TCA合金热变形流变应力的预测模型,该模型的样本数据取自热压缩试验数据,模型的输入量为变形温度、应变速率、应变量和氢含量,输出量为流变应力.研究表明:随着变形温度的升高和应变速率的降低,合金的流变应力降低;随着氢含量的增多,流变应力先降低后升高;RBF网络有较好的非线性逼近能力,训练相关性系数为0.999,训练速度较快,网络测试结果的最大相对误差为11.8%.