Data-Efficient Surrogate Model for Rapid Prediction of Temperature Evolution in a Microscale Selective Laser Sintering System

Abstract

Current metal additive manufacturing (AM) systems suffer from limitations on the minimum feature sizes they can produce during part formation. The microscale selective laser sintering (μ-SLS) system addresses this drawback by enabling the production of parts with minimum feature resolutions of the order of a single micrometer. However, the production of microscale parts is challenging due to unwanted heat conduction within the nanoparticle powder bed. As a result, finite element (FE) thermal models have been developed to predict the evolution of temperature within the particle bed during laser sintering. These thermal models are not only computationally expensive but also must be integrated into an iterative model-based control framework to optimize the digital mask used to control the distribution of laser power. These limitations necessitate the development of a machine learning (ML) surrogate model to quickly and accurately predict the temperature evolution within the μ-SLS particle bed using minimal training data. The regression model presented in this work uses an “Element-by-Element” approach, where models are trained on individual finite elements to learn the relationship between thermal conditions experienced by each element at a given time-step and the element's temperature at the next time-step. An existing bed-scale FE thermal model of the μ-SLS system is used to generate element-by-element tabular training data for the ML model. A data-efficient artificial neural network (NN) is then trained to predict the temperature evolution of a 2D powder-bed over a 2 s sintering window with high accuracy.