The Experts below are selected from a list of 114348 Experts worldwide ranked by ideXlab platform

J. Ni - One of the best experts on this subject based on the ideXlab platform.

  • Dynamic Modeling for Machine Tool Thermal Error Compensation
    Journal of Manufacturing Science and Engineering, 2003
    Co-Authors: Hong Yang, J. Ni
    Abstract:

    This paper proposes a new thermal Error Modeling methodology called the Dynamic Thermal Error Modeling which improves the accuracy and robustness of the machine tool thermal Error model. The characteristics of the thermoelastic system are investigated from the dynamic system viewpoint. The pseudo-hysteresis effect is revealed to be the major factor causing poor robustness of the conventional static thermal Error model. System identification theory is applied to build the dynamic thermal Error model for machine tool thermal Error on-line prediction. The Modeling procedure for the linear Output Error (OE) model is illustrated using simulation work for both one-dimensional spindle and two-dimensional machine structure thermal deformations. Model performance evaluation through spindle experiments shows that the thermal Error dynamic model has advantages over the conventional static model in terms of model accuracy and robustness.

  • the improvement of thermal Error Modeling and compensation on machine tools by cmac neural network
    International Journal of Machine Tools & Manufacture, 1996
    Co-Authors: S Yang, Jingxia Yuan, J. Ni
    Abstract:

    In this paper, a cerebellar model articulation controller (CMAC) neural network is proposed for thermal Error Modeling in machine tools. The CMAC is a systematic learning algorithm which can search for the nonlinear and interaction characteristics between the thermal Errors and temperature field on the machine tools. The CMAC is investigated in terms of accuracy in prediction, robustness to sensor placement, speed of learning, and tolerance to sensor failures. Experimental measurements of the spindle drift Errors for both a horizontal machining center and a CNC turning center were performed using capacitance sensors and thermal sensors. Results show that the CMAC model has better performance than other Modeling methods in robustness to sensor placement and speed of learning. This makes determination of the sensor locations easier, and reduces calibration time. In addition, a sensor failure detection algorithm is developed to provide better reliability.

Mohammed Atiquzzaman - One of the best experts on this subject based on the ideXlab platform.

  • Error Modeling schemes for fading channels in wireless communications a survey
    IEEE Communications Surveys and Tutorials, 2003
    Co-Authors: H Bai, Mohammed Atiquzzaman
    Abstract:

    Network system designers need to understand the Error performance of wireless mobile channels in order to improve the quality of communications by deploying better modulation and coding schemes, and better network architectures. It is also desirable to have an accurate and thoroughly reproducible Error model, which would allow network designers to evaluate a protocol or algorithm and its variations in a controlled and repeatable way. However, the physical properties of radio propagation, and the diversities of Error environments in a wireless medium, lead to complexity in Modeling the Error performance of wireless channels. This article surveys the Error Modeling methods of fading channels in wireless communications, and provides a novel user-requirement (researchers and designers) based approach to classify the existing wireless Error models.

Hao Tang - One of the best experts on this subject based on the ideXlab platform.

  • a new geometric Error Modeling approach for multi axis system based on stream of variation theory
    International Journal of Machine Tools & Manufacture, 2015
    Co-Authors: Hao Tang, Jian Duan, Shuhuai Lan, Huanyi Shui
    Abstract:

    Abstract This paper introduces a new geometric Error Modeling approach for multi axes system (MAS) based on stream of variation (SOV) theory, especially for multi-axis precision stage. SOV is used for measuring product quality for some complicated multi operations system, which is widely used in Error propagation in engineering field. This paper introduces SOV concept into geometric Error Modeling for MAS. Instead of different process in manufacture, the new Error Modeling approach regards each axis as a station in MAS, and calculates the deviations after each station which is considered as upstream factor to next station. It is clear to observe how geometric Errors give influence and how deviations accumulate. Different with conventional methods which are only used for Error compensation in machine tools, the new Error model is beneficial for sensitive Error control and optimal configuration selection in design part. In addition, the new Error Modeling has some merits such as debugging easily due to observe the deviations after every station. A case study of new Error Modeling procedure for six-axis stage (SAS) in optoelectronic packaging system (OPS) is developed, and applications related to Error reduction order and optimal configuration selection are processed based on the new Error model.

S Yang - One of the best experts on this subject based on the ideXlab platform.

  • position geometric Error Modeling identification and compensation for large 5 axis machining center prototype
    International Journal of Machine Tools & Manufacture, 2015
    Co-Authors: Gaoyan Zhong, S Yang, Chaoqun Wang, Enlai Zheng
    Abstract:

    Abstract This paper presents a position geometric Error Modeling, identification and compensation method for large 5-axis machining center prototype. First, regarding the prototype as a rigid multi-body system, a geometric Error model has been established, which supports the identification of position geometric Error associated with a translational axis by using laser interferometer, and a rotational axis by using laser tracker. Second, based on this model, an improved identification approach named as virtual rigid-body is put forward for calculating positioning Error of each large translational axis. Detailed derivation of a generalized matrix equation is given. Third, analytical models based on the least-squares theory were adopted to compute Error values at an arbitrary position for Error compensation. Finally, the identified position geometric Errors were compensated by using recursive software-based Error compensation method. The results show that the position accuracy of large machining center prototype has been improved with compensation and up to the design requirements.

  • accuracy improvement of miniaturized machine tool geometric Error Modeling and compensation
    International Journal of Machine Tools & Manufacture, 2006
    Co-Authors: Jaeha Lee, Yu Liu, S Yang
    Abstract:

    Abstract A novel capacitance–sensor based multi-degree-of-freedom (DOF) measurement system has been developed for measuring geometric Errors of a miniaturized machine tool (mMT) overcoming the size limitations. In the present work five geometric Error components of a three-axis mMT are measured simultaneously along each axis and the squareness Errors are determined by the slopes of straightness Error profiles. Least-squares fitting method is used to represent the analytical models of geometric Errors. A kinematic chain consisting of various structural members of mMT is introduced to establish the positional relationships among its coordinate frames. Based on this kinematic chain a general volumetric Error model has been developed to synthesize all geometric Error components of a miniaturized machine tool. Then, a recursive compensation method is proposed to achieve Error compensation efficiently. Test results show that the positioning accuracy of miniaturized machine tool has been improved with compensation.

  • the improvement of thermal Error Modeling and compensation on machine tools by cmac neural network
    International Journal of Machine Tools & Manufacture, 1996
    Co-Authors: S Yang, Jingxia Yuan, J. Ni
    Abstract:

    In this paper, a cerebellar model articulation controller (CMAC) neural network is proposed for thermal Error Modeling in machine tools. The CMAC is a systematic learning algorithm which can search for the nonlinear and interaction characteristics between the thermal Errors and temperature field on the machine tools. The CMAC is investigated in terms of accuracy in prediction, robustness to sensor placement, speed of learning, and tolerance to sensor failures. Experimental measurements of the spindle drift Errors for both a horizontal machining center and a CNC turning center were performed using capacitance sensors and thermal sensors. Results show that the CMAC model has better performance than other Modeling methods in robustness to sensor placement and speed of learning. This makes determination of the sensor locations easier, and reduces calibration time. In addition, a sensor failure detection algorithm is developed to provide better reliability.

Louis J Durlofsky - One of the best experts on this subject based on the ideXlab platform.

  • Error Modeling for surrogates of dynamical systems using machine learning
    International Journal for Numerical Methods in Engineering, 2017
    Co-Authors: Sumeet Trehan, Kevin Carlberg, Louis J Durlofsky
    Abstract:

    Summary A machine-learning-based framework for Modeling the Error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed ‘Error indicators’ (i.e., features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model Error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model Error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering), and subsequently constructs a ‘local’ regression model to predict the time-instantaneous Error within each identified region of feature space. We consider two uses for the resulting Error model: (1) as a correction to the surrogate-model QoI prediction at each time instance, and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model Error (e.g., time-integrated Errors). We apply the proposed framework to model Errors in reduced-order models of nonlinear oil–water subsurface flow simulations, with time-varying well-control (bottom-hole pressure) parameters. The reduced-order models used in this work entail application of trajectory piecewise linearization in conjunction with proper orthogonal decomposition. When the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged Errors. This article is protected by copyright. All rights reserved.

  • Error Modeling for surrogates of dynamical systems using machine learning
    arXiv: Numerical Analysis, 2017
    Co-Authors: Sumeet Trehan, Kevin Carlberg, Louis J Durlofsky
    Abstract:

    A machine-learning-based framework for Modeling the Error introduced by surrogate models of parameterized dynamical systems is proposed. The framework entails the use of high-dimensional regression techniques (e.g., random forests, LASSO) to map a large set of inexpensively computed `Error indicators' (i.e., features) produced by the surrogate model at a given time instance to a prediction of the surrogate-model Error in a quantity of interest (QoI). This eliminates the need for the user to hand-select a small number of informative features. The methodology requires a training set of parameter instances at which the time-dependent surrogate-model Error is computed by simulating both the high-fidelity and surrogate models. Using these training data, the method first determines regression-model locality (via classification or clustering), and subsequently constructs a `local' regression model to predict the time-instantaneous Error within each identified region of feature space. We consider two uses for the resulting Error model: (1) as a correction to the surrogate-model QoI prediction at each time instance, and (2) as a way to statistically model arbitrary functions of the time-dependent surrogate-model Error (e.g., time-integrated Errors). We apply the proposed framework to model Errors in reduced-order models of nonlinear oil--water subsurface flow simulations. The reduced-order models used in this work entail application of trajectory piecewise linearization with proper orthogonal decomposition. When the first use of the method is considered, numerical experiments demonstrate consistent improvement in accuracy in the time-instantaneous QoI prediction relative to the original surrogate model, across a large number of test cases. When the second use is considered, results show that the proposed method provides accurate statistical predictions of the time- and well-averaged Errors.