Using spectral extrapolation to resolve surface characterization from limited rough surface data of laser powder bed fusion additively manufactured parts

Abstract The proliferation of metal additive manufacturing has brought into focus the crucial role of surface characterization in determining the properties and performance of printed parts across diverse applications. This study aims to explore methods for analyzing surface roughness parameters of laser powder bed fusion additively manufactured as-built specimens through a variety of spectral techniques. The discussion focuses on critical parameters, data analysis methods, and practicality of usage. This study explores utilizing two-dimensional datasets (line scans) to define three-dimensional based parameters and generate relevant surfaces to provide further insight into the measurements. The surface generation methods utilize spectral information and fractal parameters from the two-dimensional datasets to produce artificial surfaces (i. e. spectral extrapolation). These artificial surfaces are evaluated against three-dimensional measurements (area scans) using established areal surface roughness parameters. The proposed approach enables a comprehensive assessment of surface parameters acquired from both optical and contact profilometry methods, facilitating comparative analysis. The results show that the spectral and fractal extrapolation can be effective at predicting the 3D surface parameters such as roughness and valley depth from single line scans, but should be employed with great care. Some spectral methods produce predictions of roughness and maximum valley depth that are within 20% of the full measured surfaces. This work also explores the use of the bearing area curve based reduced valley depth for additively manufactured parts, but produces mixed results.