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Taoyuan City, Taiwan

The objective of this paper is to present an integrated approach using the Taguchi method (TM), grey relational analysis (GRA) and a neural network (NN) to optimize theweld bead geometry in a novel gas metal arc(GMA)welding process. The TM is first used to construct a database for the NN. TheGRAis adopted to solve the problem of multiple performance characteristics in aGMAwelding process using activating flux. The grey relational grade obtained from the GRA is used as the output of the back-propagation (BP) NN. Then, aNNwith the Levenberg-Marquardt BP (LMBP) algorithm is used to provide the nonlinear relationship between welding parameters and grey relational grade of each weldment. The optimal parameters of the novel GMA welding process were determined by simulating parameters using a well-trainedBPNNmodel. Experimental results illustrate the proposed approach. © Springer Science+Business Media, LLC 2010. Source


Lin H.-L.,Army Academy Roc
International Journal of Advanced Manufacturing Technology | Year: 2013

The purpose of this work is to optimize the weld bead geometry of Inconel 718 alloy gas tungsten arc (GTA) welds that are coated with activating flux before welding. In order to obtain the optimal welding parameters with multiple quality characteristics (QCs) such as penetration and depth-to-width ratio (DWR) of weld bead, the Taguchi method (TM), gray relational analysis (GRA), and a neural network (NN) are employed in this work. The TM is first used to construct a database for the NN. The GRA is adopted to solve the problem of multiple QCs. The gray relational grade (GRG) obtained from the GRA is used as the output of the backpropagation (BP) NN. Then, a NN with the Levenberg-Marquardt BP (LMBP) algorithm is used to provide the nonlinear relationship between welding parameters and GRG of each specimen. The optimal parameters of the activated GTA welding process are determined by simulating parameters using a well-trained BPNN model. The experimental procedure of the proposed approach not only improves the DWR of weld bead but also increases the penetration of Inconel 718 alloy welds. © 2012 Springer-Verlag London. Source


Lin H.-L.,Army Academy Roc
Journal of Intelligent Manufacturing | Year: 2012

This work describes an application of an integrated approach using the Taguchi method (TM), neural network (NN) and genetic algorithm (GA) for optimizing the lap joint quality of aluminum pipe and flange in automotive industry. The proposed approach (Taguchi-Neural-Genetic approach) consists of two phases. In first phase, the TM was adopted to collect training data samples for the NN. In second phase, a NN with a Levenberg-Marquardt back-propagation (LMBP) algorithm was adopted to develop the relationship between factors and the response. Then, a GA based on a well-trained NN model was applied to determine the optimal factor settings. Experimental results illustrated the Taguchi- Neural-Genetic approach. © Springer Science+Business Media, LLC 2010. Source


Tang C.-W.,Army Academy Roc | Chuang S.S.C.,University of Akron
International Journal of Hydrogen Energy | Year: 2014

The water-gas shift (WGS) reaction on co-precipitated NiO-ZnO catalysts at different reduction temperatures has been studied by a temperature-programmed reaction using in situ diffuse reflectance infrared Fourier Transform Spectroscopy, coupled with mass spectroscopic (in situ DRIFTS/MS) techniques. The results reveal that a catalyst reduced at 493 K (labeled H220) showed higher activity than one reduced at 673 K (labeled H400) due to the ability of NiO on the H220 catalyst to promote CO conversion of the WGS reaction. In situ DRIFTS/MS studies show that there are three adsorbed species over the H220 catalyst at room temperature: adsorbed CO bands, molecularly adsorbed H 2O and carboxyl species. Increasing the temperature to 423 K led to the emergence of CO2 and H2 and the disappearance of carboxyl species. However, the low catalytic activity of the H400 catalyst could be attributed to the conversion of the NiO sites to reduced Ni metal sites, which (i) adsorbed CO as the strong linearly bonded CO on the catalyst surface, slowing down the CO reaction, and (ii) showed a lower H2O uptake. Copyright © 2013, Hydrogen Energy Publications, LLC. Source


Lee C.-F.,Chaoyang University of Technology | Chen H.-L.,Chaoyang University of Technology | Tso H.-K.,Army Academy Roc
Journal of Systems and Software | Year: 2010

Most of the proposed methods of reversible data hiding based on difference expansion require location maps to recover cover images. Although the location map can be compressed by a lossless compression algorithm, this lowers embedding capacity and increases computational cost during the procedures of embedding and extracting. The study presents an adaptive reversible data scheme based on the prediction of difference expansion. Since each cover pixel generally resembles its surrounding pixels, most of the difference values between the cover pixels and their corresponding predictive pixels are small; therefore, the proposed scheme gains from embedding capacity by taking full advantage of the large quantities of smaller difference values where secret data can be embedded. The proposed scheme offers several advantages, namely, (1) the location map is no more required, (2) the embedding capacity can be adjusted depending on the practical applications, and (3) the high embedding capacity with minimal visual distortion can be achieved. Moreover, the experimental results demonstrate that the proposed scheme yields high embedding capacity by comparing the related schemes that are proposed recently. © 2010 Elsevier Inc. All rights reserved. Source

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