Computational models of a nano probe tip for static behaviors

It is difficult to predict the measurement bias arising from the compliance of the atomic force microscope (AFM) probe. The issue becomes particularly important in this situation where nanometer uncertainties are sought for measurements with dimensional probes composed of flexible carbon nanotubes mounted on AFM cantilevers. We have developed a finite element model for simulating the mechanical behavior of AFM cantilevers with carbon nanotubes attached. Spring constants of both the nanotube and cantilever in two directions are calculated using the finite element method with known Young's moduli of both silicon and multiwall nanotube as input data. Compliance of the nanotube-attached AFM probe tip may be calculated from the set of spring constants. This paper presents static models that together provide a basis to estimate uncertainties in linewidth measurement using nanotubes. In particular, the interaction between a multiwall nanotube tip and a silicon sample is modeled using the Lennard-Jones theory. Snap-in and snap-out of the probe tip in a scanning mode are calculated by integrating the compliance of the probe and the sample-tip interacting force model. Cantilever and probe tip deflections and points of contact are derived for both horizontal scanning of a plateau and vertically scanning of a wall. The finite element method and the Lennard-Jones model provide a means to analyze the interaction of the probe and sample and measurement uncertainty, including actual deflection and the gap between the probe tip and the measured sample surface.     

旋耕机运动参数的优化设计方法

首先分析了旋耕机单位体积功耗数学模型，根据数学模型的特点，提出用广义最小二乘法确定目标函数和用最速下降法优化运动参数，为旋耕机总体参数的优化设计提供了可靠的计算模型。     

一种低速重载单作用液压缸

过大的径向力是影响液压缸使用寿命的重要因素。该文介绍了一种低速重载液压缸，该液压缸采用辅助支承以及将活塞杆受推杆作用的作用点移至活塞与端盖上的导向套之间，能够极大地减小活塞与活塞杆所受的径向力，从而避免导向套磨损严重、拉缸、密封损坏、内泄大、端盖处有外泄等故障的发生，提高了液压缸的使用寿命。     

电液负载模拟器的RBF神经网络控制

针对传统控制器自适应能力和鲁棒性差、参数调节繁琐等缺陷,提出了神经网络自适应控制方案,同时针对神经网络控制器计算复杂和稳定性证明缺乏理论依据等不足问题,在传统控制理论的基础上设计了基于PID的神经网络控制方案。根据电液负载模拟器的结构特点和目前神经网络控制的发展水平,提出了基于RBF神经网络的PID控制器,并构建了负载模拟器的神经网络控制与基于Jacobian的消扰(消除多余力)结构,仿真和试验证明其具有很好的自适应性和鲁棒性。     

基于径向基神经网络杯形件拉深成形变压边力预测技术研究

分析了RBF神经网络的预测策略和方法,并建立了板料拉深成形的变压边力预测神经网络模型.采用正交设计法进行样本参数的制定,利用板材成形CAE软件Dynaform获得训练数据,利用被训练好的神经网络对薄板成形过程中变压边力的预测技术进行了研究.数值模拟结果表明,此方法对拉深成形变压边力的预测是可行的.     

基于DSP的Si_3N_4陶瓷磨削力信号检验

采用数字信号处理 (DSP)方法对Si3 N4陶瓷的磨削力信号进行了检验 ,结果表明 :Si3 N4陶瓷的磨削力信号具有不同于金属及其它非金属材料的分布规律 ,是一种具有平稳性、各态历经性的周期振动信号 ,且在总体上不服从正态分布。     

工件材料的各向异性对超精密切削变形及已加工表面粗糙度的影响

研究了单晶铜超精密切削时，工件材料的晶体取向对切削变形及表面质量的影响。切削试验结果表明，剪切角、切削力，表面粗糙度随晶体切削方向的不同而有明显的变化。根据超精密切削中切屑形成过程的晶体塑性力学模型，分析了剪切角与切削力随晶体取向的变化规律。理论分析结果与试验结果相符。     

基于多传感器数据融合的机器人腕力测量

提出根据手爪上的多个指力传感器的输出变化，通过数据融合方法得到腕力的数值。研制了标定腕力与手爪上指力之间关系的实验台，进行了标定实验。采用径向基函数神经元网络对实验数据进行训练，得到融合网络的权值。融合结果与实测结果相当吻合。     

Comparison of friction measurements using the atomic force microscope and the surface forces apparatus: the issue of scale

Results are presented of lateral force measurements using the atomic force microscope (AFM) and the surface forces apparatus (SFA). Two different probes are used in the AFM measurements; a sharp silicon nitride tip (radius R20 nm) and a glass ball (R15 m). The lateral force is measured between the (silicon nitride or glass) probe and a mica surface which has been coated by a thin lubricant film. In the SFA, a thin lubricant film separates two molecularly smooth mica surfaces (R1 cm) which are slid relative to each other. Perfluoropolyether (PFPE) and polydimethylsiloxane (PDMS) were used as the lubricant films. In the SFA where the contact diameter is largest, the PFPE film shows much lower friction than PDMS. As the size of the probe decreases, the difference in the measured friction decreases. For sharp AFM tips, no clear distinction between the tribological properties of the films can be made. Hence, the measured coefficient of friction varies according to the length scale probed, at least for small dimensions.     

Minimization of maximum tardiness on unrelated parallel machines with process restrictions and setups

We consider the problem of schedulingN jobs onM unrelated parallel machines to minimize maximum tardiness. Each job has a due date and requires a single stage of processing. A setup for dies is incurred if the type of the job scheduled is different from the previous one on that machine. For each die type, the number of dies is restricted. Because of the mechanical structure of the machines and the fitness of dies to each machine, the processing time depends on both the job and the machine. In this paper, an efficient heuristic based on guided search, record-to-record travel, and tabu lists is presented to minimize maximum tardiness. Computational characteristics of the proposed heuristic are evaluated through extensive experiments, which show that the proposed heuristic outperforms a simulated annealing method tested and is able to prescribe the optimal solutions for problems in small scales.