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Information Journal Paper

Title

A ROBUST RBF-ANN MODEL TO PREDICT THE HOT DEFORMATION FLOW CURVES OF API X65 PIPELINE STEEL

Pages

  12-20

Abstract

 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.

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    APA: Copy

    RAKHSHKHORSHID, M.. (2017). A ROBUST RBF-ANN MODEL TO PREDICT THE HOT DEFORMATION FLOW CURVES OF API X65 PIPELINE STEEL. IRANIAN JOURNAL OF MATERIALS FORMING, 4(1), 12-20. SID. https://sid.ir/paper/352091/en

    Vancouver: Copy

    RAKHSHKHORSHID M.. A ROBUST RBF-ANN MODEL TO PREDICT THE HOT DEFORMATION FLOW CURVES OF API X65 PIPELINE STEEL. IRANIAN JOURNAL OF MATERIALS FORMING[Internet]. 2017;4(1):12-20. Available from: https://sid.ir/paper/352091/en

    IEEE: Copy

    M. RAKHSHKHORSHID, “A ROBUST RBF-ANN MODEL TO PREDICT THE HOT DEFORMATION FLOW CURVES OF API X65 PIPELINE STEEL,” IRANIAN JOURNAL OF MATERIALS FORMING, vol. 4, no. 1, pp. 12–20, 2017, [Online]. Available: https://sid.ir/paper/352091/en

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