Cutter mark cross method for improvement of contact stiffness by controlling distribution of real contact area

Contact surfaces do not make contact perfectly because such surfaces have a lot of asperities. The real contact area is much smaller than the nominal contact area, and the real contact areas has a non-uniform distribution because of the waviness in the contact surface. The contact stiffness is influenced not only by the deformation of the asperities, but also by the distribution of the real contact areas. In general, a contact surface with a uniform distribution of the real contact areas has greater contact stiffness. However, this requires a grinding finish and costs more than the cutting finish. In this study, a method for uniformly distributing the real contact areas easily, is proposed to improve the contact stiffness of a contact surface finished by cutting. The method is called the cutter mark cross (CMC) method. The allowable waviness in the CMC method is shown. In addition, the effect of the CMC method is investigated by experimentation. The results show that the real contact areas can be distributed uniformly using the CMC method. The horizontal and vertical contact stiffness can also be improved.     

A stiffness parameter and truncation error criterion for adaptive path following in structural mechanics

We explore a truncation error criterion to steer adaptive step length refinement and coarsening in incremental-iterative path following procedures, applied to problems in large-deformation structural mechanics. Elaborating on ideas proposed by Bergan and collaborators in the 1970s, we first describe an easily computable scalar stiffness parameter whose sign and rate of change provide reliable information on the local behavior and complexity of the equilibrium path. We then derive a simple scaling law that adaptively adjusts the length of the next step based on the rate of change of the stiffness parameter at previous points on the path. We show that this scaling is equivalent to keeping a local truncation error constant in each step. We demonstrate with numerical examples that our adaptive method follows a path with a significantly reduced number of points compared to an analysis with uniform step length of the same fidelity level. A comparison with Abaqus illustrates that the truncation error criterion effectively concentrates points around the smallest-scale features of the path, which is generally not possible with automatic incrementation solely based on local convergence properties.     

空气弹簧液压悬置动特性及隔振特性研究

研究了具有可变刚度阻尼的发动机空气弹簧液压悬置的动特性和隔振特性。针对影响发动机空气弹簧液压悬置动特性的关键参数上液室等效体积刚度,采用理论和试验方法研究了上液室等效体积刚度的等效值,基于该等效值建立了空气弹簧液压悬置的理论模型,对空气腔开闭两种工况进行了空气弹簧液压悬置动特性的仿真和试验研究。搭建了发动机空气弹簧液压悬置系统试验台架,研究了台架各种激励频率下的空气弹簧液压悬置的隔振特性。结果表明:本研究采用的上液室等效体积刚度进行空气弹簧进行建模是正确、可行的,提高了空气弹簧建模效率和准确性;空气弹簧液压悬置在空气腔开启和关闭两种状态下,峰值动刚度增大幅值达78.5%,峰值阻尼角增大幅值达250%。台架试验表明:当台架激励频率小于25Hz时,悬置采用大刚度大阻尼;当台架激励频率大于等于25 Hz时,悬置采用小刚度小阻尼;悬置隔振率提升明显,不同频率下提升幅值达10%～67%。     

RV减速器扭转刚度计算

扭转刚度是影响机器人运动精度、定位精度和重复定位精度的重要参数之一,本文通过对RV减速器内部齿轮副啮合刚度和各类型轴承刚度理论计算,提出了建立两个RV减速器虚拟样机,即在齿轮传动专业软件Romax中建立虚拟样机1,计算出系统变形引起的扭转刚度K1;同时在通用仿真动力学软件中建立虚拟样机2,计算出RV减速器回滞曲线,通过回滞曲线计算出扭转刚度K2,采用弹簧串联原理,计算了RV减速器扭转刚度K并通过试验验证了此方法的正确性。     

Third order optical nonlinearities characteristics of Disperse Red1 organic dye molecules inside of polymeric nanocapsules

Measuring nonlinear optical response of a specific material in a mixture, not only leads to investigate the behavior of a particular component in various circumstances, but also can be a way to select suitable combination and optimum concentration of additives and therefore obtaining the maximum nonlinear optical signals. In this work, by using dual-arm Z-scan technique, the nonlinear refractive index of Disperse Red1 (DR1) organic dye molecules inside the core of prepared polymeric nanocapsules was measured among various materials which prepared nanocapsules were made of them. Then the measured value was compared with nonlinear refractive index of DR1 solved in dichloromethane.     

Non-kinematic calibration of a six-axis serial robot using planar constraints

This paper describes a non-kinematic calibration method developed to improve the accuracy of a six-axis serial robot, in a specific target workspace, using planar constraints. Simulation confirms that the stiffness of the robot, as well as its kinematic parameters, can be identified. An experimental validation shows that the robot's accuracy inside the target workspace is significantly enhanced by reducing the maximum distance errors from 1.321 mm to 0.274 mm. The experimental data are collected using a precision touch probe, which is mounted on the flange of a FANUC LR Mate 200iC industrial robot, and a high precision 9-in. granite cube. The calibration method makes use of a linear optimization model based on the closed-loop calibration approach using multi-planar constraints. A practical validation approach designed to reliably evaluate the robot's accuracy after calibration is also proposed.     

Exactly satisfying initial conditions neural network models for numerical treatment of first Painlevé equation

In this paper, novel computing approach using three different models of feed-forward artificial neural networks (ANNs) are presented for the solution of initial value problem (IVP) based on first Painlevé equation. These mathematical models of ANNs are developed in an unsupervised manner with capability to satisfy the initial conditions exactly using log-sigmoid, radial basis and tan-sigmoid transfer functions in hidden layers to approximate the solution of the problem. The training of design parameters in each model is performed with sequential quadratic programming technique. The accuracy, convergence and effectiveness of the proposed schemes are evaluated on the basis of the results of statistical analyses through sufficient large number of independent runs with different number of neurons in each model as well. The comparisons of these results of proposed schemes with standard numerical and analytical solutions validate the correctness of the design models.     

Collapse load of composite laminates: Lower bound evaluation by stress field analytical approximation

The paper proposes a limit analysis approach to define the ultimate load capacity of orthotropic composite laminates under biaxial loading and plane stress conditions. A lower bound to the collapse load multiplier is computed by solving a maximization nonlinear problem, according to the static theorem of limit analysis. To set up the optimization problem a stress field distribution is hypothesized at lamina level, moreover inter-lamina stresses are also considered. The effectiveness and validity of the proposed approach is shown by comparing the obtained numerical predictions both with available experimental data and with other numerical results carried out by means of a different numerical lower bound approach.     

Data-driven model reduction-based nonlinear MPC for large-scale distributed parameter systems

Model predictive control (MPC) has been effectively applied in process industries since the 1990s. Models in the form of closed equation sets are normally needed for MPC, but it is often difficult to obtain such formulations for large nonlinear systems. To extend nonlinear MPC (NMPC) application to nonlinear distributed parameter systems (DPS) with unknown dynamics, a data-driven model reduction-based approach is followed. The proper orthogonal decomposition (POD) method is first applied off-line to compute a set of basis functions. Then a series of artificial neural networks (ANNs) are trained to effectively compute POD time coefficients. NMPC, using sequential quadratic programming is then applied. The novelty of our methodology lies in the application of POD's highly efficient linear decomposition for the consequent conversion of any distributed multi-dimensional space-state model to a reduced 1-dimensional model, dependent only on time, which can be handled effectively as a black-box through ANNs. Hence we construct a paradigm, which allows the application of NMPC to complex nonlinear high-dimensional systems, even input/output systems, handled by black-box solvers, with significant computational efficiency. This paradigm combines elements of gain scheduling, NMPC, model reduction and ANN for effective control of nonlinear DPS. The stabilization/destabilization of a tubular reactor with recycle is used as an illustrative example to demonstrate the efficiency of our methodology. Case studies with inequality constraints are also presented.     

A new class of nonlinear Rauch–Tung–Striebel cubature Kalman smoothers

In this paper, a new Rauch–Tung–Striebel type of nonlinear smoothing method is proposed based on a class of high-degree cubature integration rules. This new class of cubature Kalman smoothers generalizes the conventional third-degree cubature Kalman smoother using the combination of Genz׳s or Mysovskikh׳s high-degree spherical rule with the moment matching based arbitrary-degree radial rule, which considerably improves the estimation accuracy. A target tracking problem is utilized to demonstrate the performance of this new smoother and to compare it with other Gaussian approximation smoothers. It will be shown that this new cubature Kalman smoother enhances the filtering accuracy and outperforms the extended Kalman smoother, the unscented Kalman smoother, and the conventional third-degree cubature Kalman smoother. It also maintains close performance to the Gauss–Hermite quadrature smoother with much less computational cost.