Friction Coefficient and Compression Behavior of Particle Reinforced Aluminium Matrix Composites

Metal matrix composites (MMCs) are materials used in a large range of engineering applications. In this paper, the relatively low-cost stir casting is evaluated with the use for Silisyum Carbite (SiC) as reinforcement and Al7075 alloy as matrix to produce MMCs with varied reinforcement from 10% to 18%. The produced composites were examined, and their wear behavior was investigated. The results showed that the mechanical properties of the MMCs decrease with the increase of the mass percentage of reinforcement and compression

Pituitary Insufficiency and Hyperprolactinemia Associated with Giant Intra- and Suprasellar Carotid Artery Aneurysm

Pituitary insufficiency secondary to internal carotid artery (ICA) aneurysm is a very rare condition. Its prevalence is reported as 0.17% (Heshmati et al., 2001). We present a case of pituitary insufficiency and hyperprolactinemia secondary to suprasellar giant intracranial aneurysm. A 71-year-old man was admitted to our clinic with symptoms of hypopituitarism, hyperprolactinemia, and visual field defect. His pituitary MRI and cerebral angiography revealed a giant saccular aneurysm filling suprasellar cistern arising from the ophthalmic segment of the right ICA. Endovascular treatment was performed on the patient to decrease the mass effect of aneurysm and improve the hypophysis dysfunction. After treatment, his one-year follow-up showed the persistence of hypophysis insufficiency, decrease of prolactin (PRL) level, and normal visual field. An intracranial aneurysm can mimic the appearance and behavior of a pituitary adenoma. Intracranial aneurysms should be taken into consideration in the situation of hypopituitarism and hyperprolactinemia. It is important to distinguish them because their treatment approach is different from the others

Comparison of regression and artificial neural network models for surface roughness prediction with the cutting parameters in CNC turning

Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance ( R2) values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning