1,721,008 research outputs found
A neuro-fuzzy approach to select welding conditions for welding quality improvement in horizontal fillet welding
No full textMany researchers have developed algorithms to control welding parameters for a desired weld bead geometry. Unstable welding conditions induce an unsound bead, resulting in poor mechanical properties at welded joints. Generally, the dominant variables affecting weld bead geometry are welding current, are voltage, and welding speed. In practice, it is difficult to determine the proper combination of welding conditions because of excessive nonlinear and complex characteristics of welding processes. The relationship between welding conditions and weld defects cannot be easily represented by mathematical models, and it is difficult to predict weld bead geometry resulting from welding conditions. A fuzzy rule based method and neural network method are proposed: the neural network method predicts welding conditions appropriate for the desired weld bead geometry, and the fuzzy rule based method chooses appropriate welding conditions for avoiding weld defects such as undercut and overlap in horizontal fillet welding. Performance of the proposed neuro-fuzzy system was evaluated through experiments, which showed that the system can effectively check and adjust welding conditions in regard to weld defects
Optimum design based on mathematical model and neural network to predict weld parameters for fillet joints
No full textThe welding process variables of welding current, are voltage, welding speed, gas flow rate, and offset distance, which influence weld bead shape, are coupled with each other but not directly connected with weld bead shape individually. Therefore, it is very difficult and time consuming to determine the welding process variables necessary to obtain the desired weld bead shape. Mathematical modeling in conjunction with many experiments must be used to predict the magnitude of weld bead shape. Even though experimental results are reliable, prediction is difficult because of the coupling characteristics. In this study, the 2(n-1) fractional factorial design method was used to investigate the effect of welding process variables on fillet joint shape. Finally, a neural network based on the backpropagation algorithm and an optimum design based on mathematical modeling were implemented to estimate the weld parameters for the desired fillet joint shape. Mathematical modeling based on multiple nonlinear regression analysis was used for modeling the gas metal are welding (GMAW) parameters of the fillet joint. It was shown that the neural network and optimum design for estimating the weld parameters could be effectively implemented, which resulted in little error percentage difference between the estimated and experimental results
Structural Effects of Polymeric Acid on the Properties of Polymeric Acid Doped Polyaniline
No full text
CONCURRENT CRYSTALLIZATION IN POLYPROPYLENE NYLON-6 BLENDS USING MALEIC-ANHYDRIDE GRAFTED POLYPROPYLENE AS A COMPATIBILIZING AGENT
No full textThis paper describes the nonisothermal crystallization of molten blends of two semicrystalline polymers, polypropylene (PP) and Nylon-6 (N6). A discussion details the effect of the concentration of the compatibilizing agent, maleic anhydride grafted polypropylene (MAH-g-PP), on the crystallization behavior. The crystallization thermograms showed one crystallization peak or two crystallization peaks, which were significantly affected by the presence of MAH-g-PP. The crystallization temperature of N6 levels off down as the concentration of MAH-g-PP increases, whereas that of PP stays at a roughly constant temperature. These blends containing the compatibilizing agent exhibited concurrent crystallization at the crystallization temperature of PP. The crystallization behavior are also studied by optical microscopy under crossed polarizers. (C) 1994 John Wiley & Sons, Inc
Architecture of basic building blocks in protein and domain structural interaction networks
No full textMotivation: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. Results: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (> 10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs.This work was supported by Korea Research Foundation Grant (KRF-2003-041-D20490). We would like to thank CHUNG Moon Soul Center for BioInformation and BioElectronics and the IBM SUR program for providing research and computing facilities. BJ is supported under grant IMT-2000-C3-4 of the Ministry of Information and Communication of South Korea and Biogreen21 fund
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