Sn-Cu-Ni-Ce钎料对激光钎焊焊点力学性能的影响

采用Sn-Cu-Ni-Ce无铅钎料,研究了半导体激光软钎焊和红外再流焊方法对QFP48器件和0805矩形片状元件两种典型元器件的钎焊性能,针对使用不同成分钎料所得到的钎焊焊点,采用微焊点强度测试仪研究了其焊点力学性能的分布规律.结果表明,使用Sn-Cu-Ni-Ce钎料时,最佳激光输出电流显著高于Sn-Ag-Cu钎料或Sn-Pb钎料.Sn-Cu-Ni-Ce钎料成分相同时,半导体激光软钎焊得到的焊点力学性能显著优于红外再流焊焊点的力学性能;稀土元素Ce的加入能够改善Sn-Cu-Ni无铅钎料焊点的力学性能,Ce含量达到0.03%时,焊点的力学性能最佳.     

超低银SnAgCu钎料微焊点力学性能

研究了复合添加Ga/Nd元素对超低银Sn-0.3Ag-0.7Cu钎料微焊点力学性能的影响. 结果表明，复合添加适量Ga/Nd元素可以显著改善微焊点界面组织，抑制微焊点界面附近金属间化合物的生成，从而提高微焊点的力学性能，相比母合金微焊点剪切力提高幅度达到约16%；微焊点的力学性能随着时效时间的增加而降低，降低幅度优于未添加Ga/Nd的微焊点. Sn-0.3Ag-0.7Cu-0.5Ga-0.1Nd钎料微焊点的力学性能在时效处理后仍保持较好的水平，已接近Sn-3.8Ag-0.7Cu钎料微焊点的90%，具有良好的工业应用前景.     

不同钎料对QFP焊点可靠性影响的有限元分析

采用有限元方法研究了不同钎料钎焊QFP器件焊点的可靠性.结果表明,焊点根部、焊趾部位以及引线和焊点交界处为应变集中区域.分析探讨了Sn3.8Ag0.7Cu,Sn9Zn,Sn63Pb37三种钎料的模拟结果,焊点的应变曲线图显示,Sn63Pb37钎料焊点的等效应变最大,Sn9Zn钎料居中,Sn3.8Ag0.7Cu焊点的等效应变最小,表明Sn3.8Ag0.7Cu替代Sn63Pb37作为微元器件组装的组装材料具有更好的焊点力学性能.通过分析QFP64和QFP208两种器件焊点应力曲线图可以看出,QFP208器件焊点的应力值小于QFP64器件焊点的应力值,从而具有更高的可靠性.     

热循环对片式电阻Sn-Cu-Ni-Ce焊点力学性能的影响

研究了在热循环试验条件下,片式电阻Sn-Cu-Ni-Ce焊点力学性能的变化规律以及不同含量的稀土元素Ce对Sn-Cu-Ni-Ce焊点力学性能的影响.结果表明,随着热循环次数的增加,片式电阻Sn-Cu-Ni-Ce焊点的剪切力逐渐降低;在长时间热循环条件下,焊点开始萌生裂纹,导致焊点可靠性下降.与此同时,添加微量稀土元素Ce可有效提高Sn-Cu-Ni-Ce焊点的力学性能,随着Ce元素含量的增加,焊点的力学性能逐渐提高,当Ce元素含量为0.05%左右时,焊点的力学性能最佳,且在长时间热循环条件下仍然优于其它焊点.     

细间距器件无铅焊点力学性能和断口形貌分析

分别采取红外再流焊和激光再流焊焊接细间距器件，研究了SnPb，SnAgCu和SnAg三种钎料在不同焊接热源下焊点的力学性能。结果表明，激光再流焊对应焊点的力学性能优于红外再流焊，该现象在使用SnAg钎料的情况下尤为显著，同时无铅焊点优于传统的SnPb焊点。对焊点断口形貌进行研究，发现激光焊接的条件下，焊点断口韧窝较多、较深，断口的撕裂棱朝固定的方向延伸，而红外热源焊接的条件下，断口韧窝较少，较浅，两种情况下焊点韧窝开裂均为穿晶开裂。     

用于大面积芯片互连的纳米银膏无压烧结行为

文中采用化学还原法制备出一种可以用于低温烧结的纳米银膏，通过对低温无压烧结纳米银焊点的组织结构、力学性能和失效模式进行了分析，系统地讨论了无压烧结焊点中烧结银组织的渐进性组织演变规律，获得了互连焊点尺寸对烧结银连接性能和可靠性的影响. 在烧结温度250 ℃，保温时间1 h的条件下，焊点面积小于等于3 mm × 3 mm时，无压烧结焊点强度可以达到70 MPa以上. 随着尺寸的增加，焊点抗剪强度逐渐降低，但焊点尺寸为10 mm × 10 mm时仍然保持20 MPa以上的抗剪强度. 断面形貌表征结果显示，焊点面积越大，烧结银层塑性变形程度越低. 所有尺寸焊点的断面形貌从中心到边缘处均存在渐进性的组织演变，边缘处均呈现剧烈的塑性变形.     

Nd对Sn-0.7Cu-0.05Ni焊点组织与力学性能的影响

研究了添加微量稀土元素Nd对Sn-0.7Cu-0.05Ni/Cu无铅焊点再流焊和150 ℃时效条件下焊点界面组织与力学性能的影响. 结果表明，添加适量Nd(质量分数为0.06%)可以优化焊点界面组织，减缓时效过程中Sn-0.7Cu-0.05Ni/Cu界面化合物的生长速率，提高焊点力学性能，增强焊点的可靠性. 时效过程中，添加了0.06%Nd的Sn-0.7Cu-0.05Ni钎料焊点的剪切力始终保持最大，在时效1 440 h后，Sn-0.7Cu-0.05Ni-0.06Nd/Cu焊点的剪切力相比未添加稀土的Sn-0.7Cu-0.05Ni钎料提高了31.9%.     

Sn-Cu-Ni焊点纳米压痕试验分析

为研究金属间化合物与无铅焊点力学性能之间的关系，采用纳米压痕法测定了Sn-Cu-Ni焊点中金属间化合物及钎料基体的弹性模量和压痕硬度等力学性能参量．结果表明，Sn-Cu-Ni焊点中（Cu，Ni）6Sn5金属间化合物的弹性模量为113．2GPa±4．8GPa，压痕硬度为5．59GPa±0．32GPa，均与钎料基体有较大差...     

Sn-Cu-Ni(-Ce)焊点热循环可靠性

研究了Sn-Cu-Ni(-Ce)焊点在长时间热循环条件下的力学性能,以及热循环对焊点界面处金属间化合物的形成与长大行为的影响.试验发现,焊点界面处金属间化合物在长时间热循环条件下有明显长大的趋势,并在2000周期后出现了Cu3Sn相,导致焊点力学性能下降,而微量Ce元素能有效抑制界面处以及钎料内部金属间化合物的粗化,从而缓解热循环对焊点力学性能的不利影响.结果表明,随着热循环周期的增加,引脚焊点的拉伸力逐渐降低,添加0.05%Ce元素可有效提高焊点的可靠性.     

细间距器件焊点力学性能与数值模拟

采用再流焊方法对细间距器件进行钎焊试验，针对使用SnAgCu和SnPb两种钎料对应的焊点进行了研究。结果表明，SnAgCu焊点的拉力明显高于SnPb焊点，说明SnAgCu对应焊点的抗拉强度更高。对焊点断口组织分析发现，SaAgCu焊点拉伸断裂方式为韧性断裂，而SnPb焊点的断裂方式兼有脆性断裂和韧性断裂的特征。运用非线性有限元模拟，针对焊点在温度循环载荷作用下的力学性能分析，发现SnAgCu焊点的应力应变以及非线性应变能明显小于SnPb焊点对应的数值。说明SnAgCu焊点的可靠性明显高于SnPb焊点。模拟结果和试验研究结果吻合，该研究为无铅钎料的进一步研究提供理论指导。     

基于转矩估计与磁链补偿的SRM转矩脉动控制北大核心

由于开关磁阻电机强非线性、强耦合等特点,导致传统磁链控制过程中转矩脉动过大。针对该问题,提出了一种基于SRM转矩特性神经网络的瞬时转矩估计与磁链前馈补偿相结合的控制策略。利用神经网络构建了SRM的瞬时转矩估计器,在该网络结构中设计了能够体现SRM转矩变化规律的激励函数,对神经网络的输入进行预处理,通过自适应学习率训练,实现对瞬时转矩的实时估计。根据转矩估计得到的转矩偏差求得磁链偏差,在磁链模型基础上实现对磁链的前馈补偿,通过磁链滞环控制配合下实现对SRM转矩脉动的抑制。仿真实验表明,基于瞬时转矩估计和磁链前馈补偿的控制方案相较于传统控制策略可以有效地抑制转矩脉动,改善了系统的动态性能。     

Misalignment detection and enabling of seam tracking for friction stir welding

Abstract

This paper describes a technique for determining the position of a friction stir welding (FSW) tool with respect to the weld seam during welding. Forces are used as a feedback signal, and a general regression neural network is trained to predict offset position given weld forces. Experimental results demonstrate the accuracy of the developed position predictor. This technique is proposed for online misalignment detection or as a position estimator for in-process tracking of the weld seam for FSW and robotic FSW.     

Neural network based defect detection and depth estimation in TNDE

For many years, applications of the TNDE (Thermographic NonDestructive Evaluation) technique has been limited due to the complex non-linearity nature of related inversion problems such as defect depth estimation. Artificial neural networks have recently obtained success in revealing and providing quantitative information concerning defects in TNDE. In this paper, a three dimensional thermal model for non-homogenous materials such as carbon fiber reinforced plastic (CFRP) is first given. The modeling results are compared with the analytical solution based on Duhamel's theorem. Two back propagation neural networks (NN) as defect detector and depth estimator are then presented. Finally, simulated and experimental results are presented and discussed.     

Artificial neural network corrosion modeling for metals in an equatorial climate

The majority of the metals used in the distribution and transmission electric energy lines, such as cables, towers and accessories are susceptible to the corrosion degradation process. For that reason, studying the factors that influence the atmospheric corrosion is an important issue. In this paper, an artificial neural network model was developed with linear and sigmoidal functions, aiming to predict low-carbon steel, copper and aluminum corrosion rates according to environmental parameters in the area of São Luis – Maranhão, Brazil. The area along the “702 – São Luis II –Presidente Dutra” 500 kV transmission line, located in an equatorial region, is employed for this purpose. A specific methodology was developed to determine the local corrosivity rate for these metals. Five atmospheric corrosion stations (ACS) were installed along the 702 transmission line in an extension of 200 km. Along with the meteorological data, local pollutants were collected and analyzed during a period of two years. In the same period, specimens were exposed to this atmosphere and periodically collected for corrosion evaluation. The obtained results indicate that the neural network can be used as a good corrosion estimator.     

The application of neural networks in self-tuning constant force control

The constant force control gradually becomes an important technique of modern manufacturing processes. For example, the constant turning or cutting force is a useful approach for increasing the metal removal rate and increasing the tool life. The variation of machining condition may cause the robustness of a classical control theory (PID) to become ineffective, even make a control system unstable. The pole placement self-tuning control (PSTC) theory with a recursive least square parameters estimator is proposed to adapt the environmental variety. Unfortunately, the adaptability and the robustness of a self-tuning control system cannot be maintained in good condition simultaneously all the time. In this paper, a self-tuning controller equips with a neural network parameter classifier in conjunction with a least square estimator is developed to improve the adaptability and the robustness in suffering the obvious environmental variation. In order to verify the applicability of this control method, a prototype system is designed and constructed to resemble the feed rate mechanism of lathe. The dynamic responses of this force control system with different estimators are compared based upon the experimental data. The contact force is measured from a load cell and adjusted by regulating the feed rate.     

An adaptive feed strategy for a center-break reduction cell

An adaptive estimator of alumina concentration was developed for an industrial 140 kA prebake center-break fed aluminum reduction cell. The estimator employs a simple nonlinear model of an aluminum cell based on a mass balance of alumina and the correlation between alumina concentration and ohmic resistance. An extended Kalman filter is used to provide a best estimate of alumina concentration, and the covariance of the measurement noise is recursively updated using a forgetting factor. The estimator was tested using both simulated and real data, and it was shown that the estimator could be used as the basis of a feed-control strategy for similar cells.     

Characterization of the grinding process by acoustic emission

The properties of a ground surface can be estimated on-line during manufacturing based on the analysis of acoustic signals emitted by the grinding process. This possibility is demonstrated using an experimental system comprising an external grinding machine, a data acquisition unit and an artificial neural network. In the initial phase of system application, an empirical model of the grinding process is formed in the memory of the neural network by self-organized learning driven by empirical data consisting of the acoustic emission spectrum and a surface roughness correlation function. After learning, the system applies the model to estimate the correlation function of the surface profile from the input acoustic emission spectrum. For this purpose, non-parametric regression, based on the conditional average estimator, is utilized. Experiments were done on the grinding of hardened steel workpieces by a corundum wheel. During formation of the model, the surface profile and its correlation function were determined off-line, while in testing system performance the surface correlation function was estimated on-line from the acoustic emission spectrum. With respect to the estimation error, three characteristic periods of the process were observed corresponding to grinding with a newly dressed, slightly worn, and worn out wheel. The best estimation is obtained during grinding by a slightly worn wheel.     

基于神经网络的直流无刷电机控制系统

提出了一种直流无刷电动机的N-PI转速调节器的设计方法.在直流无刷电动机的高性能速度跟踪中,若仅采用传统的PI调节器,则难以克服系统超调和短时振荡问题.采用复合N-PI的控制方法,利用神经网络的自学习自适应功能在线调整PI控制参数.文中提出了一种模型参考自适应与神经网络相结合的控制策略,利用在线辨识技术,对参数变化实时补偿,及时修正神经网络权值的计算.最后,在Matlab/Simulink下进行了仿真,结果表明,运用这种设计方法很好地抑制了超调和振荡.     

Strength estimation of self-piercing rivets using lower bound limit load analysis

Abstract

This paper summarises the authors' work on strength and failure mode estimation of self-piercing rivets (SPRs) for automotive applications. First, the static cross tension strength of an SPR joint is estimated using a lower bound limit load based strength estimator. Failure mode associated with the predicted failure strength can also be identified. It is shown that the cross tension strength of an SPR joint depends on the material and gage combinations, rivet design, die design and riveting direction. The analytical rivet strength estimator is then validated by experimental rivet strength measurements and failure mode observations from nine SPR joint populations with various material and gage combinations. Next, the estimator is used to optimise rivet strength. Two illustrative examples are presented in which rivet strength is improved by changing rivet length and riveting direction from the original manufacturing parameters.     

基于多特征融合与GA-BP模型的滚动轴承故障识别

针对滚动轴承故障识别问题,基于遗传算法(GA)和BP神经网络等技术,提出一种GA-BP神经网络模型。该模型以训练数据的输出误差作为目标函数,利用遗传算法对BP神经网络的初始权值和阈值进行优化选择。将经验模态分解能量比和时域特征相结合的特征向量作为BP神经网络的输入,对滚动轴承不同工况下的故障进行识别。滚动轴承故障诊断的实例表明:该模型较传统BP神经网络模型具有更好的收敛精度、收敛速度和识别率。