The effect of dispersion on long-range inspection using ultrasonic guided waves

The dispersion of ultrasonic guided waves causes wave-packets to spread out in space and time as they propagate through a structure. This limits the resolution that can be obtained in a long-range guided wave inspection system. A technique is presented for quickly predicting the rate of spreading of a dispersive wave-packet as it propagates. It is shown that the duration of a wave-packet increases linearly with propagation distance. It is also shown that the duration of a wave-packet after a given propagation distance can be minimised by optimising the input signal. A dimensionless parameter called minimum resolvable distance (MRD) is defined that enables a direct comparison to be made between the resolution attainable at different operating points. Some conclusions are made concerning the resolution of various operating points for the case of Lamb waves in an aluminium plate.     

Mechanical properties estimation of functionally graded materials using surface waves recorded with a laser interferometer

An approach is presented here to invert surface wave dispersion and attenuation relative to the depth-dependence of the visco-elastic parameters of functionally graded materials (FGMs). The particularity of this method lies in allowing visco-elastic parameters to vary continuously with depth and in properly incorporating the continuous nature of these variations into both the forward problem (the calculation of dispersion and attenuation) and the inverse problem (evaluation of visco-elastic parameters). The forward problem solves the equation of elastic motion using a Runge–Kutta integration scheme, while the inverse problem is solved with the nonlinear solution for continuous inverse problems developed by Tarantola and Valette (1982) [32]. Viscoelasticity is treated as a first-order perturbation to the elastic structure. Testing on a synthetic example shows that the procedure is able to closely reproduce the S-wave velocity and attenuation profiles. As expected, the variations in P-wave velocity are not resolved, yet they do not introduce any significant bias into the S-wave velocity profile. The Rayleigh wave phase velocity and attenuation, measured by laser ultrasonic experiments, are used to infer the depth-dependence of S-wave velocity and of attenuation on mortar samples. This depth-dependence compares well with the depth-dependence derived from the sample density inferred from gamma-densitometry.     

基于人工神经网络的A357合金力学性能预测(英文)

A357铝合金零件一般都需要经过热处理(T6状态)以获得优异的力学性能。这类零件的性能取决于固溶温度、固溶时间、人工时效温度及人工时效时间。在本研究中,建立了基于反向传播(BP)算法的人工神经网络(ANN)模型,对A357合金的力学性能进行预测,研究了热处理工艺对该合金性能的影响。结果表明,所建立的BP模型能够对A357合金的力学性能进行有效且精度高的预测。良好的神经网络预测能力能够直观地反映A357合金的热处理工艺参数对其力学性能的影响。绘制抗拉强度和伸长率的等值线图形有助于清晰地找到抗拉强度和伸长率之间的关系,可为实际生产中热处理工艺参数的选择提供技术支持。     

Calculation of propagation properties of Lamb waves in a functionally graded material (FGM) plate by power series technique

To investigate the propagation behavior of Lamb waves in a thermal stress relaxation type functionally graded material (FGM) plate with material parameters that vary continuously along the thickness, the power series technique, which has been proved to have good convergence and high precision, is employed for theoretical derivations. The influence of the gradient coefficients of FGM on the dispersion curves is illustrated. The numerical results also reveal differences between the properties of Lamb wave propagation in the FGM plate and the corresponding properties in a homogenous plate. In terms of results, we find that both the normal and anomalous dispersions exist in the first and the second modes of the Lamb wave that propagates in the FGM plate, while only the anomalous dispersion is in the first mode and only the normal dispersion is in the second mode for the homogenous plate. The wave structure is asymmetric due to the asymmetric properties of the material. The dominance of in-plane and out-plane displacements is different between the metal-rich and ceramic-rich surfaces. All these results give theoretical guidance not only for experimental measurement of material properties but also for nondestructive evaluation using an ultrasonic wave generation device.     

A study of creep-fatigue life prediction using an artificial neural network

In this study, using AISI 316 stainless steel, creep-fatigue tests were carried out under various test conditions (different total strain ranges and hold times) to verify the applicability of the artificial neural network method to creep-fatigue life prediction. Life prediction was also made by the modified Coffin-Manson method and the modified Ostegren method using 21 data points out of a total 27 experimental data points. The six verification data points were carefully chosen for the purpose of evaluating the predictability of each method. The predicted lives were compared with the experimental results and the following conclusions were obtained within the scope of this study. While the creep-fatigue life prediction by the modified Coffin-Manson method and the modified Ostegren method had average errors of 35.8% and 47.7% respectively, the artificial neural network method had only 15.6%. As a result, the artificial neural network method with the adaptive learning rate was found to be far more accurate and effective than any of the others. The validity of the artificial neural network method for life prediction checked with the six verification data points also proved to be very satisfactory.     

梯度功能材料等离子熔积成形过程的Level-Set方法模拟

利用Level-Set方法与有限体积法(finite volume method,FVM)建立梯度功能材料(functionally graded material,FGM)等离子熔积制造(plasma deposition manufacturing,PDM)过程多相混态场统一模型;研究了在不同热输入功率下熔池形貌、温度场与流场以及溶质分布的变化规律.结果表明,热功率是影响梯度功能材料成分分布和性能的重要工艺参数.并用等离子熔积制造方法制备Al2O3-AISI316梯度功能材料,验证了模拟计算结果的正确性和可靠性.     

Evaluation of dispersion characteristics of multimodal guided waves using slant stack transform

In this paper slant stack (SL) transform is presented and its application for processing of multi-modal dispersive Lamb waves snapshots is proposed. The SL transform can facilitate the evaluation of dispersion curves based on a set of signals captured at the structure׳s surface. The presented technique leads explicitly to the frequency-phase-velocity representation of the processed signals. Theory behind the technique is presented and the SL results are compared to those obtained using the 2D discrete Fourier transform. The SL is used to process data acquired from an aluminum plate and to investigate anisotropic properties of a composite plate.     

Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements

An artificial neural (ANN) network was trained to recognize the stress intensity factor in the interval from microcrack to fracture from acoustic emission (AE) measurements on compact tension specimens. The specimens were made from structural steel SWS490B whilst the ANN had a 5-14-1 structure. The number of neurons in the input layers was five inputs of the AE parameters such as ring-down counts, rise time, energy, event duration and peak amplitude. The performance of the ANN was tested using a specific set of the AE data. The ANN is a promising tool for predicting the stress intensity factor of material using AE data.     

一种Ti-6Al-4V功能梯度网状结构的设计与压缩性能

通过电子束选区熔化制造具有高孔隙率的多孔Ti-6Al-4V结构,旨在用于替代人类松质骨.开放的网状结构能够提供骨组织向内生长的空间,因此能更好的起到固定的作用.利用计算机辅助设计（CAD）,制备一种低密度（0.78 g/cm3）,高孔隙率（82%）,弹性模量为2.7 GPa的功能梯度网状结构.结果表明,制备的功能梯度网状结构与致密件相比,具有和松质骨接近的的弹性模量,能够有效的避免应力屏蔽效应.此外,通过增加层与层之间的厚度,可以有效的防止裂纹在网状结构中快速扩展,提高安全性.此结构的屈服强度为62 MPa,试样的组织中的细小的α’相有利于提高植入物的寿命.     

一种管道中的导波频散计算方法

为了求得管道中导波的频散特性,提出了一种基于有限元的模式分析法来求解频散关系。以导波理论为基础,构建了Navier-stokes方程,采用分离变量法得到Helmholtz方程及泛函形式,并利用COMSOL软件对Helmholtz方程进行特征值的求解,计算结果与半解析有限元法所求得的结果基本吻合,并且能够求解出环状模态,证明了该方法的有效性及求解的全面性。同时,运用导波理论及铁木辛柯梁理论对低频的频散关系进行理论求解,通过对比,验证了模式分析法的精度良好。最后通过位移分量分析了模态的特征,为管道导波无损检测提供了依据。     

Drilling wear detection and classification using vibration signals and artificial neural network

In automated flexible manufacturing systems the detection of tool wear during the cutting process is one of the most important considerations. This study presents a comparison between several architectures of the multi-layer feed-forward neural network with a back propagation training algorithm for tool condition monitoring (TCM) of twist drill wear. The algorithm utilizes vibration signature analysis as the main and only source of information from the machining process. The objective of the proposed study is to produce a TCM system that will lead to a more efficient and economical drilling tool usage. Five different drill wear conditions were artificially introduced to the neural network for prediction and classification. The experimental procedure for acquiring vibration data and extracting features in both the time and frequency domains to train and test the neural network models is detailed. It was found that the frequency domain features, such as the averaged harmonic wavelet coefficients and the maximum entropy spectrum peaks, are more efficient in training the neural network than the time domain statistical moments. The results demonstrate the effectiveness and robustness of using the vibration signals in a supervised neural network for drill wear detection and classification.     

基于人工神经网络的P20钢热处理工艺

用BP人工神经网络及材料微观分析方法研究了热处理工艺对P20钢硬度的影响。结果表明,BP网络能根据淬火及回火温度精确预测P20钢热处理后的硬度;BP网络预测结果表明,P20钢经800～920℃淬火及530～650℃回火,在给定的淬火温度下,随回火温度的增加硬度急剧降低;在给定的回火温度下,随淬火温度的增加硬度略有增加。材料微观分析表明:这主要归因于回火温度升高造成的碳化物长大和α相的回复程度的加剧及淬火温度升高造成的碳及合金元素固溶量的增加。     

应用人工神经网络构造Ti40合金加工图

以Gleeble-1500热模拟试验机获得的Ti40钛合金压缩试验数据为基础,应用人工神经网络对数据进行训练和预测,建立该合金的高温流动应力与应变、应变速率和温度对应关系的预测模型,其中,应变、应变速率（对数形式）和变形温度作为模型的输入参数,流动应力作为模型的输出参数。结果发现,运用BP反向传播算法进行训练的神经网络模型具有良好的预测功能,其预测值与实验测量值基本吻合。同时,采用神经网络模型预测的数据构造Ti40合金的加工图,其安全区和失稳区的范围与实测数据获得的加工图基本相符,并对各自区域的相应组织状态进行金相观察。     

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.     

Prediction and control of surface roughness in CNC lathe using artificial neural network

In this study, a neural network approach is presented for the prediction and control of surface roughness in a computer numerically controlled (CNC) lathe. Experiments have been performed on the CNC lathe to obtain the data used for the training and testing of a neural network. The parameters used in the experiment were reduced to three cutting parameters which consisted of depth of cutting, cutting speed, and feed rate. Each of the other parameters such as tool nose radius, tool overhang, approach angle, workpiece length, workpiece diameter and workpiece material was taken as constant. A feed forward multi-layered neural network was developed and the network model was trained using the scaled conjugate gradient algorithm (SCGA), which is a type of back-propagation. The adaptive learning rate was used. Therefore, the learning rate was not selected before training and it was adjusted during training to minimize training time. The number of iterations was 8000 and no smoothing factor was used. R_a, R_z and R_max were modeled and were evaluated individually. One hidden layer was used for all models while the numbers of neurons in the hidden layer of the R_a model were five and the numbers of neurons in the hidden layers of the R_z and R_max models were ten. The results of the neural network approach were compared with actual values. In addition, inasmuch as the control of surface roughness is proposed, a control algorithm was developed in the present investigation. The desired surface roughness was entered into the control system as a reference value and the controller determined the cutting parameters for these surface roughness values. A new surface roughness value was determined by sending the cutting parameters to the observer (ANN block). The obtained surface roughness was fed back to the comparison unit and was compared with the reference value and the difference surface roughness was then sent to the controller. The iteration was continued until the difference was reduced to a certain value of surface roughness which could be permitted for machining accuracy. When the surface roughness reached the permitted value, these cutting parameters were sent to the CNC turning system as input values. In conclusion, both the surface roughness values corresponding to the cutting parameters and suitable cutting parameters for a certain surface roughness can be determined prior to a machining operation using the ANN and control algorithm.     

Prediction of mechanical property of E4303 electrode using artificial neural network

Based on the method of artificial neural network, a new approach has been devised to predict the mechanical property of E4303 electrode. The outlined predication model for determining the mechanical propert) of electrode was built upon the production data. The research leverages a back propagation algorithm as the neural network‘ s learning rule. The result indicates that there are positive correlations between the predicted results and the practical production dota. Hence, using the neural network, predication of electrode property can be realized. For the first time, this research prorides a more scientific method for designing electrode.     

TA15钛合金热变形工艺-组织的人工神经元预报

TA15钛合金经过不同条件的热约束变形之后进行金相观察,获得了工艺(温度、应变、应变速率、冷却方式)和组织(初生α相含量、初生α相尺寸、初生α相长径比)参数数据,分别以这些数据作为输入和输出,建立了结构为4×6×8×3的BP人工神经网络.研究结果表明:所建立的网络可以很好地反映出材料的工艺-组织之间的关系并且具有一定的精度,网络模型可以用来预测不同变形条件下TA15钛合金的组织,且对于TA15钛合金的实际生产具有有效的指导作用.     

基于BP神经网络的铸态高熵合金成分-强度预报

对文献报道的铸态高熵合金的成分和压缩断裂强度进行统计,获得了铸态高熵合金成分(元素种类、含量)、强度(压缩断裂强度)的参数,分别以这些数据作为输入和输出,利用BP人工神经网络建立起其间的关系网络模型.研究表明:所建立的网络很好地反映出铸态高熵合金的成分-强度之间的关系并且具有较好的精度,网络模型可用来预测不同成分铸态高熵合金的压缩断裂强度.该网络对铸态高熵合金的体系设计具有有效的指导作用.     

The reflection of guided waves from notches in pipes: a guide for interpreting corrosion measurements

Ultrasonic guided waves are used for the rapid screening of pipelines in service and simple, standard testing procedures are already defined. The implementation of the method enables the localization of the defects along the length of the pipe and offers a rough estimate of defect size. In this article we present a systematic analysis of the effect of pipe size, defect size, guided wave mode and frequency on the reflection from notches. The maximum and minimum value of the reflection coefficient at varying axial extent are identified and used for the purpose of defect sizing. Maps of reflection coefficient as a function of the circumferential extent and depth of the defect are presented for a 3 in. schedule 40 steel pipe. An approximate formula, which allows these results to be extrapolated to other pipe sizes, is proposed and evaluated.     

基于人工神经网络的焊接接头力学性能预测系统

对神经元网络在焊接接头力学性能预测上的应用做了探索,训练了焊接方法包括焊条电弧焊、气体保护焊、埋弧焊和TIG焊的抗拉强度、屈服强度、断后伸长率和断面收缩率模型.并在此基础上设计完成了基于人工神经元网络的焊接接头力学性能预测系统.利用可视化界面编程技术和数据库技术制作了友好的人机用户界面.焊接接头力学性能预测系统包括添加...