Experimental investigation and development of a deep learning framework to predict process-induced surface roughness in additively manufactured aluminum alloys

A deep learning framework is developed to predict the process-induced surface roughness of AlSi10Mg aluminum alloy fabricated using laser powder bed fusion (LPBF). The framework involves the fabrication of round bar AlSi10Mg specimens, surface topography measurement using 3D laser scanning profilometry, extraction, coupling, and streamlining of roughness and LPBF processing data, feature engineering to select the relevant feature set and the development, validation, and evaluation of a deep neural network model. A mix of core and contour-border scanning strategies are employed to fabricate four sets of specimens with different surface roughness conditions. The effects of different scanning strategies, linear energy density (LED), and specimen location on the build plate on the resulting surface roughness are discussed. The inputs to the deep neural network model are the AM process parameters (i.e., laser power, scanning speed, layer thickness, specimen location on the build plate, and the x,y grid location for surface topography measurements), and the output is the surface profile height measurements. The proposed deep learning framework successfully predicts the surface topography and related surface roughness parameters for all printed specimens. The predicted surface roughness (Sa\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{a}$$\end{document}) measurements are well within 5% of experimental error for the majority of the cases. Moreover, the intensity and location of the surface peaks and valleys as well as their shapes are well predicted, as demonstrated by comparing roughness line scan results with corresponding experimental data. The successful implementation of the current framework encourages further applications of such machine learning-based methods toward AM material development and process optimization.