Sub-Second Prediction of the Heatmap of Powder-Beds in Additive Manufacturing Using Deep Encoder–Decoder Convolutional Neural Networks
A. Sofi, Bahram Ravani
Abstract
Abstract Physical modeling of the transient temperature during the Selective Laser Sintering (SLS) Additive Manufacturing (AM) process is essential for the characterization of the quality and structural integrity of the final products. The conventional numerical models used to simulate the thermal field of Additively Manufactured structures (AM structures) are time-consuming and could not be directly used to develop a real-time simulation or a process control system. This paper presents a deep learning encoder–decoder Convolutional Neural Network (CNN) model to predict the thermal field of AM structures. For deep learning training purposes, a time-consuming physics-based simulation was used to create a dataset including thousands of two-dimensional (2D) position-time representations of the laser head with different process parameters and their corresponding heatmap of AM structures. The deep learning model developed based on this dataset is capable of sub-second prediction of the heatmap being more than 41,000 times faster than the physics-based model. The resulting sub-second computational time of the developed deep learning model allows real-time process simulation as well as provides a basis for developing a process control system for the AM process in the future.