An optimization strategy based on head losses minimization is developed for the least cost design of water distribution networks. A new weighting approach is suggested for calculating the initial flow distribution and optimum pipe diameters of the weighted flow distribution is presented by using least square method. In the mean time homogenous and isotropous head losses are maintained with implications of head loss path choice. The model is employed for designing and/or modifying pipe sizes while the classical HardyCross network solver is used to balance the flows. The whole algorithm is programmed and applied to a two-looped network selected from the literature and the results are presented on a comparative basis. A FORTRAN software with the necessary steps in the flow chart is written for the optimization calculations in this paper.

In this study, machinability behavior of AA7075 aged at different time lengths was examined experimentally and by using artificial neural network prediction model. The hardness values were measured after the heat treatment processes. Homogenized reference samples and aged samples were machined by turning processes. On the one hand, the wear occurring on the cutting edge during machining, and the cutting forces depending on cutting speed and surface roughness were investigated. Surface roughness values for each reference material and aged sample were measured using processing parameters. Acquired surface roughness values formed a surface roughness prediction model by using artificial neural networks. The results showed that the surface roughness of the samples decreases while the cutting speed of the lathe increases. In the prediction model formed by using surface roughness acquired after the machinability tests, cutting force, cutting speed and aging process were used as input parameters. Surface roughness as a result of machinability tests were used as output parameters of the proposed prediction model. High coefficient of determination, R2 rate, obtained in the formed prediction model showed that the model is successful in the prediction of surface roughness.

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