Development of 1D-convolutional Neural Network-based Height Profile Prediction Model in Directed Energy Deposition Process Using Melt-pool Image Data

Abstract

Directed energy deposition (DED) process is a representative metal additive manufacturing technology that uses a flexible deposition head mainly used for repairs in space and marine industries. The DED process saves time and money as it repairs only damaged parts and components. Therefore, a geometric control is important to fill the volume of the target damaged area economically and accurately. However, efficiency depends on process parameters such as laser power, scanning speed. This study proposes a one-dimensional convolutional neural network (1D-CNN) model to predict the height profile of the DED parts utilizing melt-pool image data. First, DED experiments were performed for a total of nine cases considering laser power and scanning speed as parameters. The collected melt-pool image data was pre-processed and only those related to the regions of interest were extracted. Initially, a total of 15 features were extracted from size, shape, location, and brightness from the melt-pool images. Then, 10 critical ones, selected through a permutation feature importance evaluation method, were input to the 1D-CNN algorithm to predict height profiles of the deposited layers. In testing phase, a mean absolute percentage error (MAPE) of 9.55% was achieved, and thus, applicability of the proposed model was verified.