Estimation of Surface Roughness in Selective Laser Sintering Using Computational Models

Abstract In this study, a novel classification model is proposed to estimate surface roughness for the parts produced in an industrial additive manufacturing technology. The proposed model focused on selective laser sintering (SLS) technology based on polyamide 12 powder applications. A comprehensive dataset is designed to simulate the production parts and manufactured at an industrial SLS machine based on a proposed positioning strategy and random positions to test the robustness of the dataset and the model. The proposed classification model is based on Deep Neural Networks (DNN) with hyperparameters designed for the problem. Benchmark results show that the model outperforms other machine learning methods on classifying the surface roughness successfully on the test dataset. The dataset and the model provide a new user interface to estimate the surface roughness depending on the coordinates of a given product surface in a SLS production chamber and the production parameters employed in the production planning phase.