Artificial neural network application for parameter prediction of heat induced distortion

Abstract

Heat induced distortion has been widely studied over the years, in order to provide reliable results, thermal elastic-plastic FEM analysis have been used to estimate the distortions produced by the heat source. However this type of analysis often involves long computational time and requires high degree of technical knowledge by the user, moreover it’s mainly performed to specific regions that limit the scope of the analysis. In order to provide a tool for the prediction of the line heating phenomena, an artificial neural network (ANN) is used. ANN is a powerful tool to predict complex phenomena, and in addition, it is very attractive because of the relatively modest hardware requirements and fast computational time. In this paper, parameter prediction for the heat induced distortion as an inverse problem is performed by ANN, using, the inherent deformation from a gas heating FEM analysis and their heating conditions as the training data. Exploratory analysis of the data and the model were performed to accurately predict the heating conditions. The prediction of the necessary heating conditions to generate an arbitrary deformation in the plate is a step forward in the automation of the line heating forming process. The possibility of predicting arbitrary heat induced distortion problem by an ANN model is shown.