Machine learning solutions of stress concentration factors in perforated plate with single circular hole

Abstract

The discontinuities such as holes, grooves, notches and fillets in the structural geometry in structures would lead to significant increase of stress level, which can be represented by stress concentration factor (SCF), and further influence the strength of structures. Therefore, SCF plays a vital role in quantitatively understanding the influence of discontinuities on the peak stress. However, analytical or empirical solutions for predicting localized high-stress around the discontinuities are usually difficult to be derived, owing to the complex interaction of the specific boundary conditions and the discontinuities. In this work, machine learning (ML) solutions of SCF of circular holes in a finite perforated plate under tension are modeled using finite element analysis and back propagation neutral network (BPNN) technique. The locations and sizes of circular holes are input as input variables, and the SCFs are target variable. The feasibility and accuracy of the model are demonstrated through numerical examples. It’s found that the developed ML approach base on BPNN technique can provide accurate prediction of SCF value for each set of structural parameters, and vice versa.