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

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

Artificial neural networks applied to spot welding process modeling

Artificial neural networks have been studied for applicability for modeling of spot welding process. Some basic concepts relating to neural networks are explained as well as how they can be used to model welding quality parameters in terms of the welding process parameter. The performance of the neural networks for modeling is presented and evaluated using actual welding data. It is concluded that neural network modeling is a good means of estimating spot welding quality on-line.     

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.     

Application of BP neural network to semi-solid apparent viscosity simulation

1　INTRODUCTIONSincethethixotropic propertiesofvigorouslystirredtin leadslurrieswerediscoveredbySpenceretal[1] atMIT ,investigationandapplicationofthese mi solidtechnologyhavebeendevelopedquickly .Thestudyontheapparentviscosityisnotonlyoftheoreti calbutalsoofutilizationsignificance .Manyinvesti gatorshavecarriedoutagreatdealofexperimentalresearchandputforwardmanymathematicalmodelsofthesemi solidapparentviscosity .Someofthesemodels ,whicharerelatedto popularexponentialfunctionandaclassica…     

An approach to measure the pose of RHJD4-1 arc welding robot for calibration

A measurement setup used for robot calibration was designed to meet the requirement of off-line programming technique. The robot end-effector pose (position and orientation) can be calculated indirecdy by using this setup. The setup has been applied to RHJD4-1 arc welding robot. The experimental results show the method of pose measuring using the measurement setup is simple and reliable to finish pose measuring.for robot calibration. In addition, the setup can measure the position repeatability of robot.     

Optimisation of friction stir welding process parameters to weld cast aluminium alloy A413 – an experimental approach

Abstract

A413 is a high strength eutectic aluminium silicon cast alloy used in the food, chemical, marine, electrical and automotive industries. Fusion welding of these cast alloys can lead to problems such as porosity, microfissuring and hot cracking, etc. However, friction stir welding can be used to weld these cast alloys effectively, without defects. In this investigation, an attempt was made to optimise the friction stir welding process parameters for joining the cast aluminium alloy A413. Joints were made using four levels each of tool rotation speed, welding speed and axial force. The quality of the weld zone was analysed using macrostructure and microstructure analysis. Tensile strength of the joints were evaluated and correlated with the weld zone hardness and microstructure. The joint fabricated using a tool rotation speed of 900 rev min^?1, a welding speed of 75 mm min^?1 and an axial force of 3 kN showed the best tensile strength.     

On-line quality inspection in laser blank welding using ART2 neural network

Laser blank welding is becoming more and more important in the automotive industry and the quality of the weld is critical for a successful application.A fully automated solution is required to inspect the quality of the blanks.This paper presents a vision inspection system with a CMOS camera which uses ART2 network to inspect the defects on-line to obtain the geometry and the quality of the weld seam.The neural network ART2 has the capability of self-learning from the environment. It can distinguish the defects that have been learned before and give new outputs for new defects.So ART2 network is suitable for weld quality inspection in laser blank welding.Additionally,a CO_2 laser is used for the blank butt-welding.     

Friction stir welding of aluminum to copper—An overview

Components made by joining different materials are required in various engineering applications. Fabrication of such components is a challenging task due to the vast difference in mechanical, thermal and electrical properties of the materials being used. Friction stir welding (FSW) is capable of joining dissimilar materials such as aluminum (Al) and copper (Cu) and therefore researchers have used this novel process for dissimilar joining. Consequently, several works pertaining to dissimilar joining, specifically Al-Cu, are available in the literature but they are scattered in different sources, which makes the task of gathering information about dissimilar FSW of Al-Cu cumbersome. This work has been written with an aim to provide all pertinent information related to dissimilar FSW of Al-Cu at one place to ease the problems of researchers. It comprehensively covers and summarizes the topics such as the effect of tool design and geometry, FSW process parameters, FSW strategies on mechanical properties, microstructure and formation of defects during dissimilar FSW of Al-Cu. In addition, it also presents and discusses several variants of dissimilar FSW of Al-Cu. Finally, this work not only puts forth major findings of the previous researchers but also suggests future recommendations for dissimilar FSW of Al-Cu.     

Artificial neural network approach for prediction of stress–strain curve of near β titanium alloy

In the present study, artificial neural network（ANN） approach was used to predict the stress-strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network（NN） to predict the stress-strain curve. In order to achieve this, three different NN architectures（feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network）, three different transfer functions（purelin, Log-Sigmoid, and Tan-Sigmoid）, number of hidden layers（1 and 2）, number of neurons in the hidden layer（s）,and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set（a,16 % a, 40 % a, and b stress-strain curves）. After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress-strain curve of near b titanium alloy.     

Artificial neural network approach for prediction of stress–strain curve of near b titanium alloy

In the present study, artificial neural network(ANN) approach was used to predict the stress–strain curve of near beta titanium alloy as a function of volume fractions of a and b. This approach is to develop the best possible combination or neural network(NN) to predict the stress–strain curve. In order to achieve this, three different NN architectures(feed-forward back-propagation network,cascade-forward back-propagation network, and layer recurrent network), three different transfer functions(purelin, Log-Sigmoid, and Tan-Sigmoid), number of hidden layers(1 and 2), number of neurons in the hidden layer(s),and different training algorithms were employed. ANN training modules, the load in terms of strain, and volume fraction of a are the inputs and the stress as an output.ANN system was trained using the prepared training set(a,16 % a, 40 % a, and b stress–strain curves). After training process, test data were used to check system accuracy. It is observed that feed-forward back-propagation network is the fastest, and Log-Sigmoid transfer function is giving the best results. Finally, layer recurrent NN with a single hidden layer consists of 11 neurons, and Log-Sigmoid transfer function using trainlm as training algorithm is giving good result, and average relative error is1.27 ± 1.45 %. In two hidden layers, layer recurrent NN consists of 7 neurons in each hidden layer with trainrp as the training algorithm having the transfer function of LogSigmoid which gives better results. As a result, the NN is founded successful for the prediction of stress–strain curve of near b titanium alloy.     

基于多层ANN的机器人等离子熔射智能化模型

分析了机器人等离子熔射过程的神经网络模型的实现方法,基于多层人工神经网络(antificial neural network,ANN)建立了等离子熔射过程的智能化模型.基于该模型,系统研究了等离子弧电流、熔射距离、机器人扫描间距和速度对主要涂层性能参数-残余应力和孔隙率的影响规律,并通过试验数据库的学习对涂层性能参数进行预测.结果表明,模型预测结果与试验结果有着很好的吻合,解决了工艺试验结果中仅有离散数据且难以全面反映等离子熔射工艺参数一涂层性能之间复杂非线性关系的难题.

Abstract：

The implementation of multi-layer artificial neural networks (ANNs) in robotic plasma spraying was discussed and an intelligent process model was constructed to fully describe the relationships between process parameters and coating properties. Influences of plasma arc current, spray distance, robot scanning space and scanning velocity on coating properties, i.e. residual stress and porosity, were systematically studied based on the present model. Prediction can be effectively carried out after the learning of the experimental database. Theoretical analysis shows the prediction results agree well with the experiments. It is favorable to fully investigate the complex and nonlinear relationships between processing parameters and coating properties as well as to overcome the limited information indicated by the discrete variable in the processing results.     

An artificial neural network for detecting weld position in arc welding process

A kind of self-organizing artificial neural net work used for weld dete ction is presented in this paper, and its concepts and issues are discussed. The network can transform the weld visual information into typical patterns and match with the weld data collected on-line, and so realize the accurate detection of the weld position in arc welding process.     

Predicting effects of diffusion welding parameters on welded joint properties by artificial neural network

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基于人工神经网络的焊缝宽度预测

研究了用神经网络预测焊缝宽度的方法。首先对焊接质量检测系统的一些相关问题进行了研究,考虑了弧焊特性的提取、焊接质量的预报以及人工神经网络模型(ANN)的应用,设计了一个基于人工神经网络的焊接质量检测系统,给出系统的组成结构,ANN被用于预测焊缝宽度,建立了焊缝宽度预测的人工神经网络模型。为了验证建立的ANN模型的可行性,进行了仿真研究。仿真结果表明,所建立的ANN模型可预测焊缝宽度,基于人工神经网络的焊接质量检测系统是有效的。     

紧密对接激光焊焊缝位置神经网络预测

激光焊接过程中,控制激光束准确对中焊缝是获得良好焊件的关键。以低碳钢板紧密对接激光焊(焊缝间隙不大于0.1 mm)作为研究对象,利用磁光传感法摄取焊接过程中焊缝区域磁光图像。分析焊缝区域图像特征,定义并提取紧密对接焊缝位置坐标,以前时刻的焊缝位置及其变化值作为输入量,当前时刻焊缝位置坐标作为输出量,应用神经网络建立焊缝位置的预测模型。试验结果表明,建立的前馈型神经网络能够较好地预测焊缝位置坐标,为激光焊缝及时纠偏和自动跟踪奠定基础。     

GH99合金TIG焊接接头拉伸性能的人工神经网络预测

利用Matlab7.0软件建立了用于预测GH99高温合金焊接接头拉伸性能的改进算法的多层BP神经网络.以焊接电流、焊接速度、脉冲频率、重熔次数、板厚、装配间隙、坡口与连接形式作为输入参数,抗拉强度、屈服强度和断后伸长率分别作为输出值.结果表明,改进算法的多层BP神经网络能够很好的预测GH99高温合金TIG焊接接头的拉伸性能,抗拉强度、屈服强度与断后伸长率预报值与试验值的平均相对误差分别为-0.76%,1.71%和2.30%.     

Detecting and tracking of welding line in visual plasma robotic welding

The formation of stable back beads in joining of the thick materials is important in order to achieve high-quality welded metal joints. Plasma welding uses the high welding current density, which is suitable for thick materials. The keyhole in the plasma welding depends on the pilot gas and the welding current. The voltage behaviour depends on the keyhole situation. If the torch is moved away from the welding line in conventional GMA welding, the welding voltage and the welding current are changed due to variations of the arc length. But the welding voltage does not change with the arc length in plasma welding, because the welding voltage depends on the situation of the keyhole. The authors tried to observe the weld pool on the top side by using a CCD camera. The timing of the shutter in the CCD camera is investigated to take clear images of the weld pool. As a result, the clear images of the weld pool were taken when the welding current was reduced to 30 A and an interference filter of 950 nm was attached to the CCD camera. The weld pool shape was changed with the torch position in the groove. The image-processing method was developed to detect the top of the weld pool. The torch position was estimated by processing the weld pool images. The digital control was designed to trace the welding line. The performance of the controller was verified by carrying out tracking experiments.     

基于人工神经网络的点焊熔核直径预测

从电极位移曲线上提取出的两个特征值及焊接能量值作为输入值,熔核直径为输出值,建立了基于BP算法的铝合金直流点焊熔核直径预测模型.该模型为隐层结点数为5的三层结构,隐层转移函数为Sigmoid函数,输出层的转移函数为线性函数.对实测结果与仿真结果进行了对比分析,结果表明,45.2%的预测值与实测值相差不超过0.5 mm,77.4%的预测误差不超过1 mm,94.3%的预测误差没有超过2 mm.回归分析结果为A=0.878T 0.982.     

大功率激光焊背面焊缝宽度神经网络预测

针对焊接过程中熔透及焊缝背面成形难以直接检测的问题,通过焊件正面和侧面的传感特征信息,对焊件背面的焊缝宽度进行预测. 用视觉传感器获取激光焊接过程中包含焊接特征信息的图像,对图像进行分割分层、模式识别和空域图像处理,准确提取焊接特征信息,发现焊接特征信息随着焊接路径的变化有着相应的变化趋势. 建立包含两个隐含层的贝叶斯神经网络,用提取到的9组特征信息作为输入,对焊件背面焊缝宽度进行预测. 通过10组焊件背面焊缝宽度的预测值与实际值的比较,验证了贝叶斯神经网络具有良好的预测能力,在焊缝不理想的状态下,也具有较好的预测能力.