Towards material and process agnostic features for the classification of pore types in metal additive manufacturing. (March 2023)

• Record Type: Journal Article
• Authors: Vandecasteele, Mathieu; Heylen, Rob; Iuso, Domenico; Thanki, Aditi; Philips, Wilfried; Witvrouw, Ann; Verhees, Dries; Booth, Brian G.
• Is Part Of: Materials & design. Volume 227(2023)
• Publication Date: 2023-03
• Subjects: Metal additive manufacturing -- Powder-bed fusion -- Porosity -- Pore classification -- X-ray tomography; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2023.111757
• Languages: English
• ISSNs: 0264-1275

Abstract

Graphical abstract: Highlights: An effective strategy for pore type classification in X-ray tomography is presented. Pore geometry was studied across three materials and various printer settings. Relative measurements of pore geometry generalize better than absolute measures. The proposed features enable a general-purpose classifier across print scenarios. Abstract: The manufacturing of metal parts via powder-bed fusion is often still facing quality issues due to microstructural porosity. Minimizing this porosity remains a priority and requires the optimization of printing process parameters. While the analysis of printed parts using X-ray computed tomography can localize and identify the pore types (e.g. keyhole or lack-of-fusion pores), these pore types can be difficult to identify across printer settings and print materials. Therefore, there is a need for a material and process agnostic approach. This work presents such an approach by considering a set of geometric pore features that do not differ considerably across print scenarios. These features are then leveraged for supervised pore type classification. The distributions of pore features were analyzed in different materials and under varying laser parameters, showing that they behave in a generic way. For classification, it is observed that they outperform other features leveraged in the state-of-the-art for pore classification in a single material, reaching up to 93.0% accuracy. Additionally, accuracies up to 90.2% for cross-material classification were observed by training on pores of one material and validating on another. These results pave the way to a general-purpose pore classification method usable across materials and process conditions.