Analysis of the effect of a new process control agent technique on the mechanical milling process using a neural network model: Measurement and modeling

In this study, a new process control agent (PCA) technique called as gradual process control agent technique was developed and the new technique was compared with conventional process control agent technique. In addition, a neural network (ANN) approach was presented for the prediction of effect of gradual process control agent technique on the mechanical milling process. The structural evolution and morphology of powders were investigated using SEM and particle size analyzer techniques. The experimental results were used to train feed forward and back propagation learning algorithm with two hidden layers. The four input parameters in the proposed ANN were the milling time, the gradual PCA content, previous PCA content and gradual PCA content. The particle size was the output obtained from the proposed ANN. By comparing the predicted values with the experimental data it is demonstrated that the ANN is a useful, efficient and reliable method to determine the effect of gradual process control agent technique on the mechanical milling process.     

Vacuum interlock system for molecular beam epitaxy

A vacuum interlock system which permits rapid loading of substrates for molecular beam epitaxy crystal growth is described. Substrates are introduced through a separate, independently pumped, loading chamber while maintaining the main growth chamber under vacuum, thereby minimizing contamination of the evaporation sources and reducing the downtime of the system between successive growth runs. The design permits outgassing of the substrates prior to their insertion in the growth chamber, and has provisions for a later addition of a separate sputtering or chemical etching capability.     

模糊控制智能开发系统

本文介绍了模糊控制智能化开发系统,包括：模糊控制原理,基于计算智能（人工神经网,遗传算法）的模糊规则自动生成的方法,系统的功能和界面等。     

A device for heating a substrate during molecular beam epitaxy

A device for uniform heating of both optically opaque silicon and transparent sapphire large-area substrates (with a diameter of up to 100 mm) in vacuum to high temperatures of 1250–1450°C is described. Using the device, it is possible to carefully prepare silicon and sapphire substrates in situ for growing Si and SiGe epitaxial layers by molecular beam epitaxy method.     

Hill非二次屈服准则次数m值识别及应变控制研究

正确确定本构模型中的物性参数是金属成形过程准确分析和模拟的基础。以Hill非二次屈服准则为基础,应用人工神经网络(ANN)技术建立了材料在变形过程中不同应力状态下物性参数m值的识别方法,并在MTS试验机上进行了薄壁管拉扭试验,通过试验中各阶段的实际应变增量值与m值识别前后计算所得理论应变增量值的比较,验证了识别所得m值以及根据识别所得m值进行应变控制的正确性。     

A neural-network-based methodology for the prediction of surface roughness in a turning process

A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.     

Accurate simplistic predictive modeling of nonlinear dynamic processes

In this article, a new simplistic way of predictive modeling of process variables in nonlinear dynamic processes is introduced. This approach, which is semi-empirical, is demonstrated on a simulated continuous stirred tank reactor. Model development uses a first-order-plus-dead-time structure and only two or three input changes for determining the coefficients. This approach is evaluated for a variety of situations which include measured output, unmeasured output, extrapolation beyond the input range, various levels of dead time, various levels of measurement error, large dynamics, and various levels of nonlinear behavior. In the situation of unmeasured output, the proposed approach is very accurate and in the other cases it is extremely accurate and far superior to linear regression and artificial neural twork models.     

Large-area plan-view sample preparation for gaas-based systems grown by molecular beam epitaxy

We describe a method for plan-view transmission electron microscopy (TEM) sample preparation that takes advantage of extreme etch-rate selectivity in GaAs and AlAs in HF/H₂O solutions. GaAs/In_xGa_1-xAs/GaAs strained-layer films (x = 0.05, 0.10, 0.19, 0.22) were chemically lifted off using this technique and were mounted on Cu TEM grids such that TEM transparent areas of up to 1 × 2 mm of constant thickness (196.4 nm) could be viewed. This simple, large-area plan-view technique uses only chemical methods and significantly extends the usefulness of TEM for the evaluation of crystal quality in GaAs-based epitaxial systems. The method requires the growth of a release layer of AlAs (10 nm thick) prior to the layered structure of interest.     

Predicting product quality with backpropagation: A thermoplastic injection moulding case study

This application uses live data from a thermoplastic injection moulding manufacturer to examine the feasibility and effectiveness of using backpropagation artificial neural networks for predictive quality control. Preprocessing and post processing of live data, formulating neural predictive strategies, selecting architecture and parameters, and handling of temporal aspects are topics. Performance of the neural networks are compared to other quality control methods, including control charts and statistical techniques. This case study demonstrates that even manufacturers who have modest expertise in computing and limited hardware and software availability can successfully use neural networks for data analysis and modelling.     

基于递归型小波神经网络的感应电动机伺服驱动系统自适应控制

针对感应电动机伺服驱动系统具有的多变、强耦合、慢时变等非线性特性和不确定性扰动,传统的位置速 度PID控制策略不能保证轨迹跟踪的精度和良好的动态品质的问题;保证系统对系统内部参数波动和外界不确定 性扰动具有较好的鲁棒性,在矢量控制策略的基础上,提出了基于递归型小波神经网络的自适应控制方案。神经 网络参数的在线学习机制采用delta自适应律并结合了BP算法和梯度下降法,算法简单,计算量大大减少。仿真 的结果验证了方案的有效性。     

人工神经网络技术在数控加工误差控制中的应用

在介绍了人工神经网络模型特点的基础上,描述了采用BP算法的三层感知器神经网络模型的工作原理,及其在数控加工误差控制中的具体运用。     

传感器动态补偿的神经网络逆系统方法

给出一种基于神经网络逆系统的传感器动态补偿策略。无需传感器具体模型和参数，即可实现传感器系统的近似单位线性化，达到动态补偿的目的。仿真实验和动态标定试验结果表明，应用这种新型的易于工程实现的动态补偿方法可显著地提高传感器的动态特性，有效改善传感器的动态品质。     

基于电流变材料的切削颤振在线监控技术研究

提出可以利用电流变材料的电控流变特性,通过在线调控切削系统动态特性以提高切削稳定性。并针对镗削系统,开发出一套可根据实时采集的切削振动信号自适应地快速调整系统动态特性以避免颤振发生的颤振智能监控系统。     

Neural network adaptive sliding mode control for omnidirectional vehicle with uncertainties

This paper presents a novel neural network adaptive sliding mode control (NNASMC) method to design the dynamic control system for an omnidirectional vehicle. The omnidirectional vehicle is equipped with four Mecanum wheels that are actuated by separate motors, and thus has the omnidirectional mobility and excellent athletic ability in a narrow space. Considering various uncertainties and unknown external disturbances, kinematic and dynamic models of the omnidirectional vehicle are established. The inner-loop controller is designed based the sliding mode control (SMC) method, while the out-loop controller uses the proportion integral derivative (PID) method. In order to achieve the stable and robust performance, the artificial neural network (ANN) based adaptive law is introduced to model and estimated the various uncertainties disturbances. Stability and robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNASMC method is verified and compared with the classical PID controller and SMC controller through both the computer simulation and the platform experiment. Results validate the effectiveness and robustness of the NNASMC method in presence of uncertainties and unknown external disturbances.     

神经网络用于凸轮机构稳速控制

凸轮机构由于其优良的工作性能而被广泛用于高精度往复运动系统中.但由于它的变转动惯量、变负载力矩、间隙等非线性动力学特性,给含有凸轮机构的机电系统稳速控制带来了很大难度.在诸多影响稳速控制精度的因素中,负载扰动力矩的影响是最主要的.本文利用神经网络逼近非线性函数的能力和自适应、自学习的特点建立了实际系统的负载扰动力矩神经网络模型,并基于全补偿原则设计了补偿环节,实现了对扰动力矩的动态补偿.实验结果表明这种方法可以有效解决凸轮系统中固有的周期性扰动对稳速控制的不利影响,切实提高稳速控制精度,具有一定的实用价值.     

抑制光束抖动的快速反射镜复合控制

提出了改进的抑制光束抖动的快速反射镜控制算法,用于提高卫星平台捕获跟瞄(ATP)系统的瞄准精度.根据自适应前馈控制和经典PID控制算法的特点,分析了两种算法复合控制的优势.探讨了现有直接复合控制算法中自适应算法不收敛的原因,并依据解耦理论引入了解耦的复合控制算法,解决了现有直接复合算法不收敛的问题.最后,对经典PID控...     

把神经网络应用于丝杠磨削过程的建模与控制

提出了利用两个人工神经网络对丝杠的磨削过程进行建模与预测控制的思想.其中,网络1用于复映传动链、热变形和力变形等误差源与工件螺距误差的关系,即建模;网络2根据网络1的输出和工件螺距误差的仿真值而预报输出下一采样周期的综合补偿控制量.通过一系列试验研究,证明此控制策略能减少工件螺距误差80%以上,有效提高了试件丝杠的磨削精度.     

电动机控制系统的智能化故障诊断技术

电动机控制系统运行中的故障诊断问题可视为特殊的模式识别问题,而ANN是处理模式识别最有效的技术之一。对用于故障诊断器的BP网络应用了改进算法,并提供了常见故障样本,读者可进一步扩充完善。实际使用证明该方法完全能完成故障的在线诊断。     

An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload

Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller.     

智能统计工序质量控制的体系研究

针对统计工序质量控制的要求,提出了智能控制体系的基本框架,论述了控制图模式的分类及其表达。对智能统计工序质量控制的控制图模式识别、控制图异常模式的参数估计和诊断分析专家系统3个主要方面进行了分析,并提出了解决方案和系统模型。在模型构造中,采用小波概率神经网络进行控制图的模式识别和控制图异常模式的参数估计。模拟仿真和实际应用结果表明：该方法结构简单、收敛速度快、识别准确率高,能够满足控制图在线检测和分析的需要。