Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 mm/rev and a depth of cut 0.4 mm was found to achieve lower surface roughness with, an increase in the cutting speed and feed rate with the increases of the surface roughness. In addition, an increase in the depth of cut was found to reduces the surface roughness. The outcome of this study showed that ANN is a versatile tool for prediction of surface roughness and may be easily extended with greater confidence to various metal cutting processes.
Permeability is an essential parameter in reservoir characterization because it is determined hydrocarbon flow patterns and volume, for this reason, the need for accurate and inexpensive methods for predicting permeability is important. Predictive models of permeability become more attractive as a result.
A Mishrif reservoir in Iraq's southeast has been chosen, and the study is based on data from four wells that penetrate the Mishrif formation. This study discusses some methods for predicting permeability. The conventional method of developing a link between permeability and porosity is one of the strategies. The second technique uses flow units and a flow zone indicator (FZI) to predict the permeability of a rock mass u
... Show MoreHigh-volume traffic with ultra-heavy axle loads combined with extremely hot weather conditions increases the propagation of rutting in flexible pavement road networks. Several studies suggested using nanomaterials in asphalt modification to delay the deterioration of asphalt pavement. The current work aims to improve the resistance of hot mix asphalt (HMA) to rutting by incorporating Nano Silica (NS) in specific concentrations. NS was blended into asphalt mixtures in concentrations of 2, 4, and 6% by weight of the binder. The behavior of asphalt mixtures subjected to aging was investigated at different stages (short-term and long-term aging). The performance characteristics of the asphalt mixtures were evaluated using the Marshall s
... Show MoreThe current investigation included study of leaf surface epidermis beside indumentum for the species Galium aparine L., G. ceratopodum Boiss, G. setaceum Lam., G. spurium L., and G. tricornatum Dandy, the study showed that paracytic type of stomatal complex is the only type occur in leaf. The indumentum compose of eglandular hairs vary in their apices, length and occurrence of different part of plant body
Wasit Governorate is characterized by industrials activities such as groups of asphalts and bricks factories, oil fields and thermal power plant, in addition to the agricultural activity that is widely separated, which leads to pollution of the surface soils with heavy metals. The main objective in this research is to assess heavy metals pollution and understand the distribution in the surface soils in the studied area. Twenty two surface soils samples were collected from 6 districts and 4 subdistricts within Wasit Governorate during April 2017. The results obtained showed that grain size analyzes are classified as sandy mud (sand 9.5%, silt 50.8 % and clays 39.8%). In the term of geochemic
... Show MoreThe current study aimed to assessing the water quality and discussing the hydrochemical characteristics and seasonal variation of surface water on the aspect of metals in Shatt-Al-Hilla, Babil Governorate, Central Iraq. Water samples were collected from eleven sampling sites of Shatt Al-Hila for wet season in March (18/3/2018), and a dry season in July (30/7/2018).
Surface water samples were analyzed for physiochemical parameters such as water temperature pH, EC, TDS, major ions (Ca2+, Mg2+, Na+, K+, SO42-, Cl-, and HCO3-), nutrients (NO3-, and PO43-) for both seasons and DO for one season
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