In this research, a radial basis function artificial neural network (RBF-ANN) Model was developed to predict the hot deformation flow curves of API X65 pipeline steel. The results of the developed Model were compared with the results of a new phenomenological Model that has recently been developed based on a power function of Zener-Hollomon parameter and a third order polynomial function of strain power m (m is a constant). Root mean square error (RMSE) criterion was used to assess the prediction performance of the investigated Models. According to the results obtained, it was shown that the RBF-ANN Model has a better performance than that of the investigated phenomenological Model. Very low RMSE value of 0.41 MPa was obtained for RBF-ANN Model, which was less than one-tenth of the RMSE value of 4.74 MPa obtained for the investigated constitutive equation. The results can be further used in mathematical simulation of hot metal forming processes.

In this study, the effect of welding heat input on microstructure and mechanical properties of dissimilar joints of API-X42 and API-B pipeline steels was investigated. Evaluation of the microstructures showed that increasing the welding heat input decreased acicular ferrite in weld metal microstructure, while amount of Widmanstatten ferrite, polygonal ferrite and grain boundary ferrite increased. Also, results of microhardness test showed that by increasing the heat input, hardness of weld metal and the heat affected zone decreased. Tensile test results showed that as the heat input increased, fracture transferred from base metal of API-B to the heat affected zone. Impact test results also showed that increasing the welding heat input could sharply drop the impact energy of the heat affected zone for both base metals due to extensive grain growth.

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.

Plant development can be defined as a programmed qualitative change in plant form, which leads plant to maturity, and researchers call it as phasic development or phenology. Recognizing the timing of occurring each development stage is necessary for managing system in order to yield increment. The timing of occurring development stages depend on climate, genotype specifications and sowing date then determination of these times in different regions is difficult and it is only possible through the using of crop simulation Models which can predict the timing of occurrence each development stage by integrating effective factors. The Model was constructed based on linear equation of plant temperature response. In order to Model evaluation two experiments were carried out in agricultural and natural resources research center of Khuzistan in 2003-2004 and 2004-2005 cropping years. Wheat development stages were determined based on Kirby and Appleyard’s scale by stereoscopic microscope and required GDD for each development stage as well. The constructed Model was calibrated and run for simulation. Comparison of simulated and observed data showed that the Model can strongly predict wheat development stages.