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The effects of turning 316L steel in a laser assisted machining are presented in this paper. The properties of 316L stainless steel are also shown in this article. In order to show correlation between the technological parameters, microgeometry of cutting tools and geometrical structure of surface, turning of material in grade 316L supported by laser has been executed. In addition, optical examination of cutting inserts has been performed and geometrical measurements of machined surfaces have been taken. The results of researches on the effects of the technological parameters and cutting tool’s microgeometry on the geometrical structure of the 316L steel surface after turning in LAM conditions are described.
Shear stress is one of the most critical parameters in hydraulic and coastal engineering, which is often measured indirectly. Since there is no instrument to measure this parameter directly and given that it is usually calculated by measuring other parameters such as velocity and pressure and using some equations, shear stress measurement is often accompanied with large measurement errors. In this study, a new technique and direct measurement using physical modeling in a hydraulic knife-edge flume and load cell were employed to measure the shear stress in a rectangular channel with rigid unsubmerged vegetation with Dv= 20, 25, and 32mm in
Q=25 and 30 Lit/S and y=10, 12, 17, and 20 cm. The results indicate that the shear stress and the dimensionless ratio decrease in a constant flow discharge with increasing the flow depth. It was also shown that the shear stress would be enhanced with an increase
in vegetation diameter due to increasing vegetation density against flow. According to dimensionless ratios of and in the graphs and considering the trend lines with appropriate correlation coefficients, some equations were presented to calculate the shear stress in the concerned range.
The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH4-N explained BOD and BOD, NH4-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH4-N.
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[ 10] Tung P. D., Mathematical
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