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Applications of Contact Length Models in Grinding ProcessesQi, Hong Sheng, Mills, B., Xu, X.P. January 2009 (has links)
yes / The nature of the contact behaviour between a grinding wheel and a workpiece in the grinding process has a great effect on the grinding temperature and the occurrence of thermal induced damage on the ground workpiece. It is found that the measured contact length le in grinding is considerably longer than the geometric contact length lg and the contact length due to wheel-workpiece deflection lf. The orthogonal relationship among the contact lengths, i.e. lc2 = (Rrlf)2 + lg2, reveals how the grinding force and grinding depth of cut affect the overall contact length between a grinding wheel and a workpiece in grinding processes. To make the orthogonal contact length model easy to use, attempts on modification of the model are carried out in the present study, in which the input variable of the model, Fn’, is replaced by a well-established empirical formula and specific grinding power. By applying the modified model in this paper, an analysis on the contributions of the individual factors, i.e. the wheel/workpiece deformation and the grinding depth of cut, on the overall grinding contact length is conducted under a wide range of grinding applications, i.e. from precise/shallow grinding to deep/creep-feed grinding. Finally, using a case study, the criterion of using geometric contact length lg to represent the real contact length lc, in terms of convenience versus accuracy, is discussed.
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Parameter Estimation of Complex Systems from Sparse and Noisy DataChu, Yunfei 2010 December 1900 (has links)
Mathematical modeling is a key component of various disciplines in science and
engineering. A mathematical model which represents important behavior of a real
system can be used as a substitute for the real process for many analysis and synthesis
tasks. The performance of model based techniques, e.g. system analysis, computer
simulation, controller design, sensor development, state filtering, product monitoring,
and process optimization, is highly dependent on the quality of the model used.
Therefore, it is very important to be able to develop an accurate model from available
experimental data.
Parameter estimation is usually formulated as an optimization problem where the
parameter estimate is computed by minimizing the discrepancy between the model
prediction and the experimental data. If a simple model and a large amount of data are
available then the estimation problem is frequently well-posed and a small error in data
fitting automatically results in an accurate model. However, this is not always the case.
If the model is complex and only sparse and noisy data are available, then the estimation
problem is often ill-conditioned and good data fitting does not ensure accurate model
predictions. Many challenges that can often be neglected for estimation involving simple
models need to be carefully considered for estimation problems involving complex
models.
To obtain a reliable and accurate estimate from sparse and noisy data, a set of
techniques is developed by addressing the challenges encountered in estimation of
complex models, including (1) model analysis and simplification which identifies the important sources of uncertainty and reduces the model complexity; (2) experimental
design for collecting information-rich data by setting optimal experimental conditions;
(3) regularization of estimation problem which solves the ill-conditioned large-scale
optimization problem by reducing the number of parameters; (4) nonlinear estimation
and filtering which fits the data by various estimation and filtering algorithms; (5) model
verification by applying statistical hypothesis test to the prediction error.
The developed methods are applied to different types of models ranging from models
found in the process industries to biochemical networks, some of which are described by
ordinary differential equations with dozens of state variables and more than a hundred
parameters.
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A Systems Biology Approach to Develop Models of Signal Transduction PathwaysHuang, Zuyi 2010 August 1900 (has links)
Mathematical models of signal transduction pathways are characterized by a large
number of proteins and uncertain parameters, yet only a limited amount of quantitative
data is available. The dissertation addresses this problem using two different approaches:
the first approach deals with a model simplification procedure for signaling pathways
that reduces the model size but retains the physical interpretation of the remaining states,
while the second approach deals with creating rich data sets by computing transcription
factor profiles from fluorescent images of green-fluorescent-protein (GFP) reporter cells.
For the first approach a model simplification procedure for signaling pathway
models is presented. The technique makes use of sensitivity and observability analysis to
select the retained proteins for the simplified model. The presented technique is applied
to an IL-6 signaling pathway model. It is found that the model size can be significantly
reduced and the simplified model is able to adequately predict the dynamics of key
proteins of the signaling pathway.
An approach for quantitatively determining transcription factor profiles from GFP reporter data is developed as the second major contribution of this work. The procedure
analyzes fluorescent images to determine fluorescence intensity profiles using principal
component analysis and K-means clustering, and then computes the transcription factor
concentration from the fluorescence intensity profiles by solving an inverse problem
involving a model describing transcription, translation, and activation of green
fluorescent proteins. Activation profiles of the transcription factors NF-κB, nuclear
STAT3, and C/EBPβ are obtained using the presented approach. The data for NF-κB is
used to develop a model for TNF-α signal transduction while the data for nuclear STAT3
and C/EBPβ is used to verify the simplified IL-6 model.
Finally, an approach is developed to compute the distribution of transcription factor
profiles among a population of cells. This approach consists of an algorithm for
identifying individual fluorescent cells from fluorescent images, and an algorithm to
compute the distribution of transcription factor profiles from the fluorescence intensity
distribution by solving an inverse problem. The technique is applied to experimental data
to derive the distribution of NF-κB concentrations from fluorescent images of a NF-κB
GFP reporter system.
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Modélisation d'un robot manipulateur en vue de la commande robuste en force utilisé en soudage FSW / Robot manipulator modeling for robust force control used in Friction Stir Welding (FSW)Wang, Ke 28 January 2016 (has links)
Le travail présenté dans cette thèse concerne la modélisation et la commande robuste en force de robots manipulateurs industriels à articulations flexibles utilisés pour le procédé FSW. Afin de réduire les temps de calcul et l'occupation de la mémoire, une approche basée sur la méthode par intervalle est proposée en vue de la simplification des modèles dynamiques des robots industriels, et contribue à identifier les paramètres d'inertie qui sont négligeables. Des études de cas sur trois types de trajectoires de test et l’analyse des couples moteurs ont démontré l'efficacité et les bonnes performances de la méthode de simplification. Ensuite, la modélisation dynamique et l'identification des paramètres du procédé FSW ont été effectuées. Les paramètres des modèles linéaires et non-linéaires de forces axiales sont identifiés. Sur la base de la modélisation du procédé FSW qui considère simultanément la cinématique du système complet, le modèle de déplacement du robot rigide, les flexibilités des articulations et le modèle dynamique de la force axiale, un contrôleur robuste en force est obtenu par la méthode de réglage fréquentielle. En outre, un simulateur du procédé FSW robotique est développé et les résultats de simulation montrent les bonnes performances du contrôleur en force. L'oscillation de la force axiale dans le procédé FSW peut être simulée en utilisant un modèle de perturbation de la position verticale de référence. / The work presented in this thesis focuses on the modeling and robust force control of flexible joints industrial robot manipulators used for FSW process. In order to reduce computation time and memory occupation, a novel interval-based approach for dynamic model simplification of industrial robots is proposed, which applies to arbitrary trajectories of whole robot workspace and contributes to obtaining negligible inertia parameters. Cases studies have been carried out on three kinds of test trajectories and torques analysis of robot dynamic equation, demonstrating the effectiveness and good performance of the simplification method. Then, the dynamic modeling and identification of robotic FSW process is performed, and the parameters of linear and nonlinear dynamic axial force process models are identified by using the plunge depth and its derivative. On the basis of the modeling of robotic FSW process which simultaneously considers the complete kinematics, the rigid robot displacement model, the joint flexibility and the dynamic axial force process model, a robust force controller can be obtained by using the frequency response approach. Besides, a simulator of robotic FSW process is developed and simulation results show good performance of the force controller. The oscillation of axial force in FSW process can be simulated when a disturbance model of initial vertical reference position is proposed and used in the simulation.
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Birds, bats and arthropods in tropical agroforestry landscapes: Functional diversity, multitrophic interactions and crop yieldMaas, Bea 20 November 2013 (has links)
No description available.
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