Experimental investigation of Notched Identification based on Maximum Resistance Force in Steel Specimens using an Artificial Neural Network
Year: 2025
Type: article
Abstract: In this paper, a robust methodology is presented to identify the notch depth value in X70 steel specimens based on the maximum resistance force using an artificial neural network (ANN). The mechanical characterizations of fracture behavior of the X70 steel specimens are simulated using XFEM. The main goal is to obtain the best identification of notch depths as a function of various maximum resistances. The collected data are used as inputs and outputs for the proposed ANN using optimal parameters to identify the notch depths in different steel specimen designs based on different maximum resistance force values. The provided results showed the effectiveness of the ANN based on the convergence study of the obtained results and the accuracy of notch depth identification.
Source: Procedia Structural Integrity
Authors: A. Oulad Brahim, R. Capozucca, E. Magagnini, S. Khatir, Y. Bouzid
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412437405