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In this paper a neural network with a feed forward topology and a back propagation algorithm was used to investigate the effect of chemical composition on hardness and impact energy in API X65 microalloyed steel. Experimental data was obtained by cutting 100 specimens from pipes manufactured in industrial scale (with 1219 mm diameter, 14.3 mm wall thickness, with similar heats and manufacturing processes). The chemical analysis, Vickers hardness and Charpy impact tests were conducted then according to requirements specified by API 5L standard. The weight percent of C, Si, Mn, P, S, Ni, Cr, Mo, Al, Cu, V, Ti, Nb and Ca were considered as input parameters of the network; while Vickers hardness and Charpy impact energy were considered as output. Scatter diagrams and two statistical criteria: correlation coefficient and mean squared relative error were used to evaluate the prediction performance of developed ANN model. With regard to the exact performance of the developed neural network, it was used then to investigate the effect of chrome and vanadium on Vickers hardness and Charpy impact energy of tested steel.

Keywords: Neural Network, Chemical composition, Vickers Hardness, Charpy Impact Energy, API X65 Microalloyed Steel

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