155,216 research outputs found
Surface profile prediction and analysis applied to turning process
An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks
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Searching for improvement
Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents
Research activities at the Institute of electrotechnology in the field of metallurgical melting processes
A wide range of industrial metallurgical melting processes are carried out using electrothermal and electromagnetic technologies. The application of electrotechnologies offers many advantages from technological, ecological and economical point of view. Although the technology level of the electromagnetic melting installations and processes used in the industry today is very high, there are still potentials for improvement and optimization. In this paper recent applications and future development trends for efficient use of electromagnetic processing technologies in metallurgical melting processes are described along selected examples which are part of the research activities of the Institute of Electrotechnology of the Leibniz University of Hannover
Uncertainty in the manufacturing of fibrous thermosetting composites: A review
Composites manufacturing involves many sources of uncertainty associated with material properties variation and boundary conditions variability. In this study, experimental and numerical results concerning the statistical characterization and the influence of inputs variability on the main steps of composites manufacturing including process-induced defects are presented and analysed. Each of the steps of composite manufacturing introduces variability to the subsequent processes, creating strong interdependencies between the process parameters and properties of the final part. The development and implementation of stochastic simulation tools is imperative to quantify process output variabilities and develop optimal process designs in composites manufacturing
Cell organization in soft media due to active mechanosensing
Adhering cells actively probe the mechanical properties of their environment
and use the resulting information to position and orient themselves. We show
that a large body of experimental observations can be consistently explained
from one unifying principle, namely that cells strengthen contacts and
cytoskeleton in the direction of large effective stiffness. Using linear
elasticity theory to model the extracellular environment, we calculate optimal
cell organization for several situations of interest and find excellent
agreement with experiments for fibroblasts, both on elastic substrates and in
collagen gels: cells orient in the direction of external tensile strain, they
orient parallel and normal to free and clamped surfaces, respectively, and they
interact elastically to form strings. Our method can be applied for rational
design of tissue equivalents. Moreover our results indicate that the concept of
contact guidance has to be reevaluated. We also suggest that cell-matrix
contacts are upregulated by large effective stiffness in the environment
because in this way, build-up of force is more efficient.Comment: Revtex, 7 pages, 4 Postscript files include
Optimal modelling and experimentation for the improved sustainability of microfluidic chemical technology design
Optimization of the dynamics and control of chemical processes holds the promise of improved sustainability for chemical technology by minimizing resource wastage. Anecdotally, chemical plant may be substantially over designed, say by 35-50%, due to designers taking account of uncertainties by providing greater flexibility. Once the plant is commissioned, techniques of nonlinear dynamics analysis can be used by process systems engineers to recoup some of this overdesign by optimization of the plant operation through tighter control. At the design stage, coupling the experimentation with data assimilation into the model, whilst using the partially informed, semi-empirical model to predict from parametric sensitivity studies which experiments to run should optimally improve the model. This approach has been demonstrated for optimal experimentation, but limited to a differential algebraic model of the process. Typically, such models for online monitoring have been limited to low dimensions.
Recently it has been demonstrated that inverse methods such as data assimilation can be applied to PDE systems with algebraic constraints, a substantially more complicated parameter estimation using finite element multiphysics modelling. Parametric sensitivity can be used from such semi-empirical models to predict the optimum placement of sensors to be used to collect data that optimally informs the model for a microfluidic sensor system. This coupled optimum modelling and experiment procedure is ambitious in the scale of the modelling problem, as well as in the scale of the application - a microfluidic device. In general, microfluidic devices are sufficiently easy to fabricate, control, and monitor that they form an ideal platform for developing high dimensional spatio-temporal models for simultaneously coupling with experimentation.
As chemical microreactors already promise low raw materials wastage through tight control of reagent contacting, improved design techniques should be able to augment optimal control systems to achieve very low resource wastage. In this paper, we discuss how the paradigm for optimal modelling and experimentation should be developed and foreshadow the exploitation of this methodology for the development of chemical microreactors and microfluidic sensors for online monitoring of chemical processes. Improvement in both of these areas bodes to improve the sustainability of chemical processes through innovative technology. (C) 2008 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved
Information Storage and Retrieval for Probe Storage using Optical Diffraction Patterns
A novel method for fast information retrieval from a probe storage device is
considered. It is shown that information can be stored and retrieved using the
optical diffraction patterns obtained by the illumination of a large array of
cantilevers by a monochromatic light source. In thermo-mechanical probe
storage, the information is stored as a sequence of indentations on the polymer
medium. To retrieve the information, the array of probes is actuated by
applying a bending force to the cantilevers. Probes positioned over
indentations experience deflection by the depth of the indentation, probes over
the flat media remain un-deflected. Thus the array of actuated probes can be
viewed as an irregular optical grating, which creates a data-dependent
diffraction pattern when illuminated by laser light. We develop a low
complexity modulation scheme, which allows the extraction of information stored
in the pattern of indentations on the media from Fourier coefficients of the
intensity of the diffraction pattern. We then derive a low-complexity maximum
likelihood sequence detection algorithm for retrieving the user information
from the Fourier coefficients. The derivation of both the modulation and the
detection schemes is based on the Fraunhofer formula for data-dependent
diffraction patterns. We show that for as long as the Fresnel number F<0.1, the
optimal channel detector derived from Fraunhofer diffraction theory does not
suffer any significant performance degradation.Comment: 14 pages, 11 figures. Version 2: minor misprints corrected,
experimental section expande
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