双曲柄机构的最佳传动角设计

在满足机构的杆长约束条件和使机构具有最佳传动角要求的前提下，探讨了双曲柄机构的尺寸可行区域，并分析了对传动最有利的双曲柄机构的设计方法。     

Force analysis of bearings on a modified mechanism using proposed recurrent hybrid neural networks

Due to different load conditions on four-bar mechanisms, it is necessary to analyze force distribution on the bearing systems of mechanisms. A proposed neural network was developed and designed to analyze force distribution on the bearings of a four bar mechanism. The proposed neural network has three layers: input layer, output layer and hidden layer. The hidden layer consists of a recurrent structure to keep dynamic memory for later use. The mechanism is an extended version of a four-bar mechanism. Two elements, spring and viscous, are employed to overcome big force problem on the bearings of the mechanism. The results of the proposed neural network give superior performance for analyzing the forces on the bearings of the four-bar mechanism undergoing big forces and high repetitive motion tracking. This continuation of simulation analysis of bearings should be a benefit to bearing designers and researchers of such mechanisms.     

按许用传动角设计曲柄摇杆机构的非迭代方法

本文提出了已知摇杆摆角、行程速比系数和许用传动角设计曲柄摇杆机构的非迭代方法。     

铰接转向机构液压传动的动载分析

根据运动学和动力学原理 ,对铰接转向机构的恒流液压系统进行了研究 ,并在实例计算的基础上得到结论。为达到等速转向 ,消除动载 ,必须按照一定的规律去改变油缸在转向过程中的供油量     

广角极光成像仪滤光片的热设计及试验

针对广角极光成像仪对其光学系统温度的特殊需求,对成像仪使用的高温滤光片进行了热设计及相关试验。介绍了广角极光成像仪的光机结构,尤其是滤光片组件的结构。通过建立传导和辐射热阻的计算方程组,分析了由滤光片到镜筒整个换热路径中存在的热阻及其影响因素。然后,以影响热阻的因素作为设计变量,分析了影响镜筒温度的敏感变量。最后,提出了高温滤光片的热设计方案。真空验证试验表明:在高温和低温两种极端工况下,滤光片温度水平分别稳定在105.8℃和138.2℃,其控温准确度优于±2.5℃,控温稳定度优于0.75℃/min;反射镜组和探测器窗口温度水平和温差都满足热控指标。得到的结果显示,基于热阻和温度灵敏度分析的设计方法,能够快速明确影响热阻的敏感设计变量,减少设计过程的盲目性。空间高温滤光片的热设计满足了光学系统的要求,保证了广角极光成像仪滤光片组件与反射镜之间的温差。     

一种新型输电线路除冰机器人的本体结构设计

针对现有除冰机器人除冰效率较低、运行不稳定等特点,通过对国内外除冰方法的比较,设计了一种新型的输电线路除冰机器人.该机器人采用铣削与敲击复合的除冰方式,并且应用组合切削刀这种新型结构,保证了机器人可以高效、安全的去除线路上的覆冰;通过对组合切削刀切削力的计算保证了切削能力,同时降低了能耗;整个机器人由三臂组成,每只手臂设有摆臂机构和伸缩机构,可以按照规定的路径,通过位姿的调整,实现灵活越障;机器人可以通过对控制箱位置的调整,提高越障时的稳定性.     

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.     

Acoustic diagnosis of a pump by using neural network

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.     

四缸摆动活塞式压缩机传动机构设计

为提高摆动活塞式压缩机的流量,满足提高物料输送效率的要求,对单缸压缩机的传动机构进行了参数计算和运动特性分析,提出一种新型四缸组合传动机构设计方案,实现四缸同时驱动。试验结果证明,参数计算正确,结构紧凑、合理,方案可行。     

神经元网络在甘蔗收获机智能设计系统评价体系中的应用

针对甘蔗收获机智能设计系统中评价体系的缺乏智能性、对设计专家经验知识要求较高以及评价准确度较低的缺陷,阐述了将神经元网络嵌入智能设计系统评价体系,以提高该体系的可靠性,并探讨了神经元网络在评价体系中的应用形式和模式转换,建立了对甘蔗收获机中重要部件性能进行评价的神经元网络。     

Control of chemical processes using neural networks: implementation in a plant for xylose production

This work demonstrates the use of artificial intelligence for control of xylose reactor performance in a paper factory. Two types of neural networks are used, a perceptron for the temperature controller and an adaptive formulation for the noise filter. The results show an improvement in the temperature stabilization time with respect to a classic PID control.     

A robust control with a neural network structure for uncertain robot manipulator

A robust position control with the bound function of neural network structure is proposed for uncertain robot manipulators. The uncertain factors come from imperfect knowledge of system parameters, payload change, friction, external disturbance, and etc. Therefore, uncertainties are often nonlinear and time-varying. The neural network structure presents the bound function and does not need the concave property of the bound function. The robust approach is to solve this problem as uncertainties are included in a model and the controller can achieve the desired properties in spite of the imperfect modeling. Simulation is performed to validate this law for four-axis SCARA type robot manipulator.     

A decision tree of neural networks for classifying images of wood veneer

Artificial neural networks for the classification of wood veneer by an automatic visual inspection system are presented. Initially, a single large neural network is implemented with eleven image features as inputs and thirteen outputs — one for each class of veneer. In order to improve on the classification accuracy of this single network, a decision tree of smaller and more specialised modular neural networks is introduced to achieve a classification by successive refinement. This results in a substantial improvement in classification accuracy. A key process in the design of a modular neural network is the use of normalised inter-class variation in the selection of the most appropriate image features to be used for its particular specialised classification task.     

Concept design of checking fixture for auto-body parts based on neural networks

The concept design of checking fixtures for auto-body parts is a highly complex process that requires a human designer to draw from his rich experience. By exploiting recent advances in CAD/CAM and artificial intelligence techniques, one may constrain multiple solutions such that only good designs are considered. In this paper, a method of selecting type for checking fixtures is proposed that harnesses advantages of neural networks. This method attempts to capture relevant domain knowledge and is used to produce acceptable solutions. The method is applied to a case problem and the suggested checking fixture type is compared to one offered by a human designer. The agreement between the two solutions is very close.     

Effect of flow-rate measurement accuracy on unaccounted for gas in transmission networks

Unaccounted-for-Gas (UAG) in modern natural gas transmission networks is a very debated topic among researchers and network managers, since UAG significantly affects the physical and commercial balancing of the network itself. On the technical hand, UAG is caused by the unavoidable errors in measuring and estimating gas quantities in the network and it is either positive or negative. However significant and tendentially positive/negative UAG values often occur in modern networks and this can be ascribed to the rise of systematic errors. Furthermore, cyclic trends of UAG are often observed and the correlation with the accuracy of flow-rate measurements has not been adequately investigated, despite this latter is widely recognized as a crucial issue. In this paper, the rangeability faults of flow-meters installed at the interconnections with regional and city networks have been investigated, together with the effect of the drift of the instrument due to the lack of subsequent calibrations. From the analysis carried out, it was found that about 12% of the average daily flow-rates measured in a whole year at the exit points of the Italian network are below the minimum flow-rate of the flow-meter and that a significant correlation between monthly UAG and the number of flow-meter rangeability faults exists.     

靠模式弯箍机构设计与转角数模修正

为提高钢筋任意角度(-180.～180.)连续自动弯曲成形精度和效率,引入靠模成形设计方法,提出了一种新型箍筋模钢筋弯箍设计方案.基于弹复理论,建立了钢筋弯曲过程中弯箍转盘转角与钢筋弯曲成形角度之间的函数关系式.运用ANSYS/LS-DYNA非线性有限元分析软件建立了弯箍系统的有限元模型,验证了弯箍转盘转角与箍筋角度之间的数学模型的精确性,基于对钢筋不同成形角度的回弹角度模拟,对转盘转角数模进行了修正.结果表明,该机构设计合理,满足柔性生产要求,便于实现高精度成形自动控制.     

Statistical simulation of shift force for a manual transmission

Statistical simulation approaches are proposed to evaluate the shift feeling for a manual transmission. First, shift force simulator for the manual transmission is developed by considering the dynamic models of the external and internal linkage, synchronizer, and drivetrain. It is found that the shift force by the simulator shows a good correlation with the test results. Using the simulator, two kinds of statistical simulation approaches are proposed and the objective parameters that can be used to evaluate the shift feeling quantitatively are obtained. It is expected that the shift force simulator with the statistical approaches, developed in this study can be used asa useful design tool to evaluate the shift feeling in the initial design stage.     

A study on the fault diagnosis of roller-shape using frequency analysis of tension signals and artificial neural networks based approach in a web transport system

Rollers in the continuous process systems are ones of key components that determine the quality of web products. The condition of rollers (e.g. eccentricity, runout) should be consistently monitored in order to maintain the process conditions (e.g. tension, edge position) within a required specification. In this paper, a new diagnosis algorithm is suggested to detect the defective rollers based on the frequency analysis of web tension signals. The kernel of this technique is to use the characteristic features (RMS, Peak value, Power spectral density) of tension signals which allow the identification of the faulty rollers and the diagnosis of the degree of fault in the rollers. The characteristic features could be used to train an artificial neural network which could classify roller conditions into three groups (normal, warning, and faulty conditions). The simulation and experimental results showed that the suggested diagnosis algorithm can be successfully used to identify the defective rollers as well as to diagnose the degree of the defect of those rollers.     

一种平面十杆曲柄滑块机构传动特性的研究

本文提出了一种具有对称结构的平面十杆曲柄滑块机构。理论分析表明,该种机构的传动函数与曲柄滑块机构的传动函数基本一致,但滑块上没有横向力。设计实例表明了该种机构用作为冲压机工作机构的优越性。     

传动角最优的测井仪推靠系统优化设计

通过对微球聚焦测井仪推靠系统主传动机构的运动学、动力学和行程传动角分析.按照实际工况要求,建立了以工作行程内推靠系统主传动机构传动角与期望传动角均方根波动最小为目标的多变量优化设计数学模型.并通过实例计算,对比分析优化前后测井仪推靠系统主传动机构工作行程内传动角的曲线,结果证明,优化效果显著,优化结果改善了推靠系统主传...