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Talar R.,Poznan University of Technology | Stoic A.,Mechanical Engineering Faculty in Slavonski Brod
Metalurgija | Year: 2012

The paper presents some results of investigation of finish machining of hardened bearing surfaces of cylindrical gear wheels. Finish machining has been performed with wedges of defined geometry made of CBN. The presented investigation results are related mainly to the wear processes of the cutting wedges. Additional results of quality examination of finish machined gear wheels have been presented, too. Source


Grizelj B.,Mechanical Engineering Faculty in Slavonski Brod | Cumin J.,Mechanical Engineering Faculty in Slavonski Brod
Metalurgija | Year: 2013

This paper deals with the effects of technological parameters used in the V-die bending process, on the obtained product properties and dimensions. By variation of the tool geometry, several cases of steel sheet bending process are observed through the FEM simulations. Also by variation of different mechanical material properties, effects on product geometry are observed. Since the automobile manufacturers mostly use the high strength steel sheet metal plates, there is a need for the successful tool construction and optimization in order to produce quality products. Source


Svalina I.,Mechanical Engineering Faculty in Slavonski Brod | Sabo K.,Josip Juraj Strossmayer University of Osijek | Simunovic G.,Mechanical Engineering Faculty in Slavonski Brod
International Journal of Advanced Manufacturing Technology | Year: 2011

Surface roughness is often taken as an indicator of the quality of machined work pieces. Achieving the desired surface quality is of great importance for the product function. The paper analyzes the influence of the cutting depth, feed rate, and number of revolutions on surface roughness. The obtained results of the experimental research conducted on the work piece "diving manifold," were used to determine the coefficients by different numerical methods of the same prediction model. The results of the surface roughness provided by the prediction functions generated in this work were compared with the results of the surface roughness obtained by using neural networks. The assessment of the surface roughness provided by models and neural networks can facilitate the work of less experienced technologists and thus shorten the time of production technology preparation. The obtained results show that the total mean square deviation in models obtained by the application of the moving linear least squares and the moving linear least absolute deviations methods is nevertheless considerably higher than by the application of the neural network method. © 2011 Springer-Verlag London Limited. Source


Sertic J.,Mechanical Engineering Faculty in Slavonski Brod | Kozak D.,Mechanical Engineering Faculty in Slavonski Brod | Samardzic I.,Mechanical Engineering Faculty in Slavonski Brod
Scientific World Journal | Year: 2014

The values of reaction forces in the boiler supports are the basis for the dimensioning of bearing steel structure of steam boiler. In this paper, the application of the method of equivalent stiffness of membrane wall is proposed for the calculation of reaction forces. The method of equalizing displacement, as the method of homogenization of membrane wall stiffness, was applied. On the example of "Milano" boiler, using the finite element method, the calculation of reactions in the supports for the real geometry discretized by the shell finite element was made. The second calculation was performed with the assumption of ideal stiffness of membrane walls and the third using the method of equivalent stiffness of membrane wall. In the third case, the membrane walls are approximated by the equivalent orthotropic plate. The approximation of membrane wall stiffness is achieved using the elasticity matrix of equivalent orthotropic plate at the level of finite element. The obtained results were compared, and the advantages of using the method of equivalent stiffness of membrane wall for the calculation of reactions in the boiler supports were emphasized. © 2014 Josip Sertić et al. Source


Svalina I.,Mechanical Engineering Faculty in Slavonski Brod | Galzina V.,Mechanical Engineering Faculty in Slavonski Brod | Lujic R.,Mechanical Engineering Faculty in Slavonski Brod | Simunovic G.,Mechanical Engineering Faculty in Slavonski Brod
Expert Systems with Applications | Year: 2013

The close price prediction model of the Zagreb Stock Exchange Crobex® index is presented in this paper. For the input/output data plan modeling the Crobex® index close price historical data are retrieved from the Zagreb Stock Exchange official internet pages. The prediction model is created in the way that for each of 5 days in advance it predicts the Crobex® close price. The prediction model is generated based on the input/output data plan by means of the adaptive neuro-fuzzy inference system method, representing the fuzzy inference system. It is of the essence to point out that for each day a separate fuzzy inference system is created by means of the adaptive neuro-fuzzy inference system method based on the same set of input/output data, the only difference being that for every separate fuzzy inference system different subsets for training and checking are used so that input variables are differently created. The input/output data set represents the historical data of the Crobex® index close price from 4 November 2010 to 24 January 2012 and the Crobex® index close price is predicted for the subsequent 5 days, the first day of prediction being 25 January 2012. After that the above mentioned input/output data set is shifted 5 days in advance and the Crobex® index close price is predicted in advance for the next 5 days starting with the last day of the input/output data set. In that way the Crobex® index close prices are predicted until 19 October 2012 based on the Crobex® index close price historical data. At the end of the paper qualitative and quantitative estimates are presented for the given approach of predicting the Crobex® index close price showing that the approach is useful for predicting within its limits. © 2013 Elsevier Ltd. All rights reserved. Source

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