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In this paper, a computational method to predict the corrosion resistance of some ironbased powder metallurgy (P/M) materials is proposed. The samples prepared from atomized iron powder and pre-alloyed iron base powders powders were the materials analyzed in this paper. The compacted samples were obtained by powder metallurgy process, respectively pressing and sintering of the mixed powders. The samples were compacted at a pressures of 400 and 600 MPa. The sintering temperature was approximately 1.150 В°C. The inf...

The goal of the work reported in this paper is to describe and develop an artificial neural network (ANN) to evaluate the effect of processing parameters such as density, pressing and sintering time on microstructural characteristics including porosity and microstructure of some iron-based powder metallurgy (PM) materials. There are various methods for generating models to predict the the effect of processing parameters. The materials used in this study are prealloyed iron-based powders. The particle size of the p...

The purpose of this paper was to study the effect of carbon content on the dimensional characteristics of some powder metallurgy (PM) materials. The powders used in this study are atomized pure iron and prealloyed iron-based powders with Cu, Ni and Mo. Some elements as copper, nickel, molybdenum, manganese and phosphorus are the most common alloying element added in powders form because of its low cost, availability and capability to improve the properties of alloys. The particle size of the analyzed powders is ra...

The objective of the study was to evaluate the effect of processing parameters such as pressing and sintering time on microstructural characteristics including porosity and microstructure of some iron-based powder metallurgy (PM) materials. The powders used in this study are prealloyed iron-based powders. The particle size of the powders is ranging from 45 to 150 ?m. Following mixing the powders with zinc stearate 1% for 30 minutes, the studied powders were single pressed in a die at a pressure of 600 MPa. Zn-stea...

A learning algorithm was used as an artificial neural network (ANN) tool to predict the mechanical properties of some iron-based powder metallurgy (P/M) materials. Specimens prepared from atomized iron powder and from pre-alloyed iron base powders powders were analyzed in this paper. The specimens were fabricated by press and sintering of the mixed powders. The samples were compressed in a universal mechanical testing machine to a pressure of 400 and 600 MPa. The sintering temperature was approximately 1.150 пїЅC....