Estimation of welded joint strength using genetic algorithm approach

The genetic algorithm approach is extended to the estimation of mechanical properties of the joining of brass materials. The mechanical properties of joint parts can be improved by selecting suitable parameters. The strength of the joint parts is affected by many factors, such as the gap between the parts, the torch angle, the quantity of the shielding gases, the pulse frequencies and the electrode tip angle during welding operations. Since all these factors affect the mechanical properties of the welded joint parts, the effects of these parameters need to be cautiously investigated. The present paper describes the use of the stochastic search process that is the basis of Genetic Algorithms (GA), in developing the strength value of the welded parts. Non-linear estimation models are developed using GAs. Developed models are validated with experimental data. The Genetic Algorithm Welding Strength Estimation Model (GAWSEM) is developed to estimate the mechanical properties of the welded joint for the brass materials. The effects of five welding design parameters on the strength value using the GAWSEM have been examined. The results indicated that the changes of the gap between the joint parts and the torch angle have an important effect on the welded joint strength value and the optimum quantity of the shielding gas and the pulse frequencies exist in the tensile strength of welded joints. (C) 2005 Elsevier Ltd. All rights reserved.C1 Pamukkale Univ, Fac Engn, Dept Mech Engn, TR-20020 Camlik, Denizli, Turkey

genetic algorithm approach

The bonding strength of adhesives is influenced by many factors such as, the surface roughness, bonding clearances, interference fit, temperature, and material of the joining parts, etc. Since all these factors affect the strength of the adhesively joined parts, the effects of these parameters need to be investigated. The present paper describes the use of stochastic search process that is the basis of Genetic Algorithm (GA), in developing fatigue strength estimation of adhesively bonded cylindrical components. Nonlinear estimation models are developed using GA. Developed models are validated with experimental data. Genetic Algorithm Fatigue Strength Estimation Model (GAFSEM) is developed to estimate the fatigue strength of the adhesively bonded tubular joint using several adherent materials, such as steel, bronze and aluminum materials. (C) 2004 Elsevier Ltd. All rights reserved.C1 Pamukkale Univ, Fac Engn, Dept Mech Engn, TR-20020 Camlik, Denizli, Turkey

fossil fuels in Turkey

The main objective is to investigate Turkey's fossil fuels demand, projection and supplies by using the structure of the Turkish industry and economic conditions. This study develops scenarios to analyze fossil fuels consumption and makes future projections based on a genetic algorithm (GA). The models developed in the nonlinear form are applied to the coal, oil and natural gas demand of Turkey. Genetic algorithm demand estimation models (GA-DEM) are developed to estimate the future coal, oil and natural gas demand values based on population, gross national product, import and export figures. It may be concluded that the proposed models can be used as alternative solutions and estimation techniques for the future fossil fuel utilization values of any country. In the study, coal, oil and natural gas consumption of Turkey are projected. Turkish fossil fuel demand is increased dramatically. Especially, coal, oil and natural gas consumption values are estimated to increase almost 2.82, 1.73 and 4.83 times between 2000 and 2020. In the figures GA-DEM results are compared with World Energy Council Turkish National Committee (WECTNC) projections. The observed results indicate that WECTNC overestimates the fossil fuel consumptions. (C) 2008 Elsevier Ltd. All rights reserved

Artificial Neural Networks

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (C) 2009 Elsevier Ltd. All rights reserved