Estimation of contact lengths using deep learning neural network

Abstract

One of the most common problems in engineering is contact problems. In recent years, researchers have turned to alternative methods that can offer effective solutions in a shorter time, instead of solutions containing complex and long mathematical expressions. This study focuses on the estimation of the contact lengths in a homogeneous elastic layer suppressed by two elastic punches with two solution methods. Firstly, a new model was designed for estimation using Deep Learning Neural Network (DNN), one of the deep learning structures. Estimation of contact lengths was provided with the output of the DNN model, which was fed with the homogeneous elastic layer, the ratio of shear modules of the punches and the input parameters of punch radii. The finite element method was used as the second solution method. The problem was modeled in the ANSYS programme and the solution was made with the same parameters used in DNN modeled. The results obtained from both solutions were compared with the solutions obtained by the theory of elasticity and classical NN in the literature. It had been seen that the results obtained with DNN and ANSYS were compatible with the results obtained with analytical and classical NN and the margin of error was smaller.