University of Tabriz

About: University of Tabriz is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Population & Nanocomposite. The organization has 12141 authors who have published 20976 publications receiving 313982 citations.

INVESTIGATING THE EFFECTS OF EDM PARAMETERS ON SURFACE INTEGRITY, MRR AND TWR IN MACHINING OF Ti–6Al–4V

Abstract: Ti–6Al–4V is a kind of difficult-to-cut material with poor machinability by traditional machining methods, while electrical discharge machining (EDM) is suitable for machining titanium alloys. In this paper, three input machining parameters including pulse current, pulse on time and open circuit voltage were changed during EDM tests. To investigate the output characteristics; material removal rate (MRR), tool wear ratio (TWR) and different aspects of surface integrity for Ti–6Al–4V samples such as topography of machined surface, crack formation, white layer (recast layer) thickness and microhardness were considered as performance criteria. The variations of MRR and TWR versus input machining parameters were investigated by means of main and interaction effect plots and also verified by ANOVA results. The effect of pulse energy based on pulse on time and pulse current variations against recast layer thickness and microhardness was studied. The possibility of forming different chemical elements and compound...

Effective watershed management; Case study of Urmia Lake, Iran

Abstract: Limited water resources with uneven distribution and growing demands are the main challenges of water management in Iran. The government has planned several water resources development projects. The complex technical, socio-economical and environmental outcomes of these projects require a comprehensive evaluation. To select more adaptive and accountable projects, suitable group decision support systems are needed. In this research, 4 proposed routes of water transfer schemes to Urmia Lake in Iran, which is in danger of completely drying out, are evaluated with respect to different criteria. The criteria and weights were obtained from an organization responsible for major water infrastructures in the basin. By using an efficient multi-criteria decision making method of compromise programming, the 4 alternatives were ranked, and the most robust water transfer route was selected. This best alternative transfers about 300 million cubic meters per year from another basin in the north of the lake. Resu...

A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure

Abstract: Blasting operation is widely used method for rock excavation in mining and civil works. Ground vibration and air-overpressure (AOp) are two of the most detrimental effects induced by blasting. So, evaluation and prediction of ground vibration and AOp are essential. This paper presents a new combination of artificial neural network (ANN) and K-nearest neighbors (KNN) models to predict blast-induced ground vibration and AOp. Here, this combination is abbreviated using ANN-KNN. To indicate performance of the ANN-KNN model in predicting ground vibration and AOp, a pre-developed ANN as well as two empirical equations, presented by United States Bureau of Mines (USBM), were developed. To construct the mentioned models, maximum charge per delay (MC) and distance between blast face and monitoring station (D) were set as input parameters, whereas AOp and peak particle velocity (PPV), as a vibration index, were considered as output parameters. A database consisting of 75 datasets, obtained from the Shur river dam, Iran, was utilized to develop the mentioned models. In terms of using three performance indices, namely coefficient correlation (R2), root mean square error and variance account for, the superiority of the ANN-KNN model was proved in comparison with the ANN and USBM equations.