A plasticity-corrected stress intensity factor for fatigue crack growth in ductile materials

In this paper, a plasticity-corrected stress intensity factor range ΔK_pc is developed on the basis of plastic zone toughening theory. Using this new mechanical driving force parameter for fatigue crack growth (FCG), a theoretical correlation of Paris’s law with the crack tip plastic zone is established. Thus, some of the important phenomena associated with the plastic zone around the fatigue crack tip, such as the effects of load ratio R, overload and T stress on the FCG behavior, can be incorporated into the classical Paris’s law. Comparisons with the experimental data demonstrate that ΔK_pc as a single and effective mechanical parameter is capable of describing the effects of the load ratio, T stress and overload on the FCG rate. The FCG rate described as a function of ΔK_pc tested under a simple loading condition can also be used for other complex loading conditions of the same material.     

Neural network detection of grinding burn from acoustic emission

An artificial neural network (ANN) approach is proposed for the detection of workpiece “burn”, the undesirable change in metallurgical properties of the material produced by overly aggressive or otherwise inappropriate grinding. The grinding acoustic emission (AE) signals for 52100 bearing steel were collected and digested to extract feature vectors that appear to be suitable for ANN processing. Two feature vectors are represented: one concerning band power, kurtosis and skew; and the other autoregressive (AR) coefficients. The result (burn or no-burn) of the signals was identified on the basis of hardness and profile tests after grinding. The trained neural network works remarkably well for burn detection. Other signal-processing approaches are also discussed, and among them the constant false-alarm rate (CFAR) power law and the mean-value deviance (MVD) prove useful.     

Estimation of acoustic emission source waveform of fracture using a neural network

The applicability of a neural network to acoustic emission (AE) is presented. It is shown that the shape of the simulated source waveform using piezoelectric ceramics is steplike, similar to that of mode I crack extension, and its rise-time can be varied by the resonance frequency in the thickness direction. The results imply that the simulated source can provide learning waveforms for the network. Actual AE waveforms were also acquired by conducting a tensile test of a chevron-notched graphite specimen. It was demonstrated that the appropriate source waveform associated with mode I crack extension was successfully determined by the network taught with simulated waveforms.     

裂纹尖端塑性区的声发射特性研究

对裂纹尖端形成塑性区时产生声发射信号的累计数与应力强度因子的关系进行研究。运用线弹性断裂理论和弹塑性断裂理论进行了分析,得出累计数在小范围屈服时与应力强度因子的4m次方成正比,在大范围屈服时与应力强度因子的2m次方成正比。对碳钢和不锈钢的单边切口试样进行了拉伸声发射监测实验,实验测得大范围屈服时,比例系数分别为0.333和0.183,m值为0.5。     

人工神经网络在拉深润滑油选择中的应用

首次利用人工神经网络技术对影响拉深过程中法兰下摩擦系数的工艺参数及润滑油参数进行了分析 ,提出了润滑油选择方案并描述了神经网络建模过程。神经网络预测计算结果与实际符合较好。对自行设计的试验装置进行了简要描述并提出了试验数据误差修正公式 ,实践证明 ,该公式有效的减少了试验误差。     

Trouble diagnosis of the grinding process by using acoustic emission signals

The focus of this study is the development of a credible diagnosis system for the grinding process. The acoustic emission signals generated during machining were analyzed to determine the relationship between grinding-related troubles and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient (m), a learning rate (a), and a structure of the hidden layer in the iterative learning process. The success rates of trouble recognition were verified.     

Developing a machine vision system for spangle classification using image processing and artificial neural network

These studies are carried out to classify the three different spangle patterns found on the galvanized steel sheets by image processing and artificial neural network. Images of 200 × 200 pixel sizes from three different spangle samples were captured using optical filter and digital camera. These images were preprocessed and Haralicks (energy, entropy, contrast and homogeneity) and Laws (LE/EL, LS/SL, LR/RL, ES/SE and SR/RS) textural parameters were calculated. Principle component analysis was carried out on the generated textural database and this database was used to train and test the artificial neural network. The artificial neural network could be able to classify the spangle pattern up to a reliable extent and the overall accuracy was 80.09% for investigated samples. The proposed methodology can be used for quantification of spangle patterns and to develop an online system for spangle classification. Matlab® 7 was used for image processing and artificial neural network studies.     

基于人工神经网络的机械加工工艺方案的综合评估决策模型的研究

采用了人工神经网络(Artifical Neural Network ANN)技术。提出了一种新型的机械加工工艺方案的综合评估模型,并经实际应用,结果表明该工作是可行的、简易的,可以作为评估的一种有效方法,为机械加工工艺方案的决策打下基础。     

Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks

A novel system which allows arc-welding defect detection and classification is presented in this paper. The spectroscopic analysis of the plasma spectra produced during the welding process is a well-known technique to monitor the quality of the resulting weld seams. The analysis of specific emission lines and the subsequent estimation of the electronic temperature T_e profile offers a direct correlation between this parameter and the corresponding weld seams. However, the automatic identification and classification of weld defects has proven to be difficult, and it is usually performed by means of statistical studies of the electronic temperature profile. In this paper, a new approach that allows automatic weld defect detection and classification based in the combined use of principal component analysis (PCA) and an artificial neural network (ANN) is proposed. The plasma spectra captured from the welding process is processed with PCA, which reduces the processing complexity, by performing a data compression in the spectral dimension. The designed ANN, after the selection of a proper data training set, allows automatic detection of weld defects. The proposed technique has been successfully checked. Arc-weld tests on stainless steel are reported, showing a good correlation between the ANN outputs and the classical interpretation of the electronic temperature profile.     

Prediction of flow stress of Ti-15-3 alloy with artificial neural network

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电铸金刚石-镍复合膜品质的神经网络分析

本文综合考虑电铸金刚石-镍复合膜沉积工艺参数与复合膜品质的关系，建立了一种复合膜品质预测的人工神经网络模型。该模型能够精确刻画电镀工艺参数与沉积结果之间的非线性关系，并且具有泛化能力。用所得到的神经网络模型对复合膜沉积结果进行分析预测，其预测值与实际样品测量值吻合，表明神经网络分析方法是有效的。     

Closing law and stress intensity factor of elliptical crack under compressive loading

The solution of surface displacement of an elliptical crack under compressive-shear loading was obtained by using the comples function method.The closing mode was established by analyzing the geometrical condition of closing crack,and the corresponding critical stress was solved.The result corrects the traditional viewpoint.in which there exist only open or close states for an elliptical crack,and points out that the local closing is also one of crack states.Based on them,the effect of the closed crack on stress intensity factor was discussed in detail,and its rational formulae are put forward.     

基于人工神经网络冷轧带肋钢筋轧制工艺的优化设计

用人工神经网络的计算机辅助技术模拟冷轧带肋钢筋生产工艺的产品性能与工艺参数之间的非线性非显式多目标多变量的映射关系,从而找出其最佳的工艺方案和产品性能,获得满意的产品质量。     

基于数值模拟和人工神经网络技术的汽车覆盖件坯料设计优化

阐述了运用数值模拟和人工神经网络技术相结合的方法进行汽车覆盖件坯料设计的思路,试验研究的结果表明：采用设计优化切实可行,具有良好的实际应用效果。     

A hybrid multi-fidelity approach to the optimal design of warm forming processes using a knowledge-based artificial neural network

In this paper, a hybrid multi-fidelity optimization approach based on a knowledge-based artificial neural network (KBNN) was used to determine the optimal heating strategy in warm forming processes. First, a less costly, but less accurate isothermal finite element analysis (FEA), which neglected the complex heat transfer between the part and tooling elements, was performed to obtain overall knowledge about the effect of temperature on forming performance. Then, a small number of more accurate and expensive (i.e., longer computational time) non-isothermal FEA results were utilized in an artificial neural network (ANN), along with the prior knowledge from the isothermal FEA, to improve the accuracy in defining the non-linear relationship between the design variables (i.e., regional temperatures on the tooling) and the response (i.e., part depth value before failure). The accuracy of the non-isothermal FEA was validated by comparing its prediction results to the experimental findings. This approach was demonstrated for forming a rectangular cup, where it offered a rapid and accurate recommendation of the optimal temperature distribution on the tooling elements for improved formability. The individual and interaction effects of the regional temperatures on formability were also evaluated in detail by constructing the response surfaces near the optimal design point using the multi-fidelity system developed. Finally, a comparison of the temperature and thickness strain distributions on the formed parts was made under various operating conditions, to acquire detailed information on the deformation characteristics of the material.     

Modeling of tool wear in drilling by statistical analysis and artificial neural network

The useful life of a cutting tool and its operating conditions largely control the economics of the machining operations. Hence, it is imperative that the condition of the cutting tool, particularly some indication as to when it requires changing, to be monitored. The drilling operation is frequently used as a preliminary step for many operations like boring, reaming and tapping, however, the operation itself is complex and demanding.

Back propagation neural networks were used for detection of drill wear. The neural network consisted of three layers input, hidden and output. Drill size, feed, spindle speed, torque, machining time and thrust force are given as inputs to the ANN and the flank wear was estimated. Drilling experiments with 8 mm drill size were performed by changing the cutting speed and feed at two different levels. The number of neurons in the hidden layer were selected from 1, 2, 3, …, 20. The learning rate was selected as 0.01 and no smoothing factor was used. The estimated values of tool wear were obtained by statistical analysis and by various neural network structures. Comparative analysis has been done between statistical analysis, neural network structures and the actual values of tool wear obtained by experimentation.     

飞机疲劳开裂声发射波形信号的人工神经网络模式识别方法

利用SOM神经网络,对分类挑选的飞机疲劳过程采集的声发射波形信号进行模式识别分析,得到一组(300个)疑似裂纹的波形信号。其特点有:频谱图上同时出现三个明显的峰值,其能量相对较大,且频率基本固定。其中,第三峰值频率(168.5kHz)与先前的试验数据(175.8kHz)相接近,已具备了较明显的裂纹特征。     

基于平均等效应力强度因子的焊缝疲劳寿命预测

目前普遍应用的焊缝疲劳仿真分析方法主要有名义应力法和结构应力法,针对两种方法的应用局限性及未考虑剪切结构应力导致的预测精度不高等问题,提出了一种新的高精度焊缝疲劳寿命预测方法。该方法是以断裂力学为理论基础,通过有限元软件提取焊缝周围节点的节点力及力矩,并通过一系列计算得到整个裂纹扩展路径上的平均等效应力强度因子ΔKeq,并将其作为本文中焊缝疲劳寿命预测方法的评价参数。通过对DP800GI和HSLA350GI两种高强钢材料的搭接接头进行疲劳试验,并将ΔKeq与试验所得的疲劳寿命数据进行双对数回归分析,得到的一条主ΔKeq-N曲线作为焊缝的疲劳寿命预测曲线,并与目前的名义应力法和结构应力法进行预测精度对比,得出本文中的焊缝疲劳寿命预测方法的预测精度高于目前的名义应力法和结构应力法。     

声发射法在复合材料飞轮试件损伤检测中的应用

采用声发射法对复合材料飞轮试件高速旋转时的损伤信号进行采集.借助体视显微镜、光学显微镜和着色法等辅助检测方法,使损伤类型与损伤信号一一对应.用经过训练的人工神经网络对飞轮试件的损伤模式进行了有效识别.试验还研究了飞轮试件的最终失效原因是由于环向裂纹的贯穿造成的.     

Simulation of aging process of lead frame copper alloy by an artificial neural network

1　INTRODUCTIONThefunctionsofleadframeinelectronicpackingareprovidingchannelsforelectronicsignalsbetweendevicesandcircuits ,andfixingdevicesoncircuitboards.Leadframealloysarerequiredtohavehighstrengthandgoodformabilityaswellashighelectri calandthermalconductivity .Cu basealloysarethemostpopularleadframealloysandareusedinplasticpackagingapplicationduetotheirhighthermalandelectricalconductivityaswellashighstrength[13] .Theaginghardening processinfabricationofleadframecopperalloymakesitpossi…