Fast Characterization of Surface Porosity in Additively Manufactured Components Using the Robust Analysis and Processing of Image Data (RAPID) Tool

Porosity can significantly undermine the chemical and mechanical integrity of additively manufactured components. The porosity phenomenon is closely tied to the input variables, such as feedstock quality, environmental conditions, and 3D-printing operating parameters. A tool that can characterize porosity to establish useful correlations would aid in the optimization of the additive manufacturing process. However, a free open-source, intuitive, and workflow-capable tool for porosity characterization is not currently available. Here, a user-friendly, free, and open-source tool for robust analysis and processing of image data (RAPID) is introduced. RAPID was used in this paper to study the influence of varying laser powers on porosity count and morphology in additively manufactured 316L steel. It was found that lack-of-fusion pores diminished as laser power increased from 30 W to 70 W (75.8 J/mm3). Furthermore, increasing the power from 30 W to 90 W reduced the size of surface pores ten-fold. The data also suggested a 1.5-fold improvement in fatigue limit when power was increased from 30 W to 90 W. RAPID was validated side-by-side with a well-known image processing software, ImageJ, and was found to provide 18-times-more intuitiveness owing to a streamlined workflow. These results demonstrate some of the utility RAPID can introduce practitioners and researchers in materials science and engineering. RAPID is available for download from GitHub as an executable file on both Windows and Mac operating systems.