Laser powder bed fusion (L-PBF) additive manufacturing has been used to fabricate complex-shaped structures, which often consist of fine features. Due to transient process phenomena, there are differences in terms of the melt pool formation and the surface morphology depending upon the feature area and scan parameters. This study investigates the scan length effect on the surface morphology and the presence of transient length and width that may have a significant effect as the layer addition continues. For this purpose, four scan lengths (0.25 mm, 0.5 mm, 1.0 mm, and 2.0 mm) are used to fabricate six tracks with back-and-forth scanning. A full factorial design of experiments is used to form multi-track depositions with three levels of power (125 W, 160 W, and 195 W), two levels of scan speed (550 mm/s and 1000 mm/s), and four levels of hatch spacing (80 μm, 100 μm, 120 μm, and 140 μm). A white light interferometer is used to acquire the surface data, and MATLAB is used for surface topographical analysis. The results indicated that the scan length has a significant effect on the surface characteristics. The average height of multi-track deposits increases with the decrease of the scan length. Moreover, the transient length and width can be approximated based on the height variation along both the scan and transverse directions, respectively.
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Defects Classification via Hierarchical Graph Convolutional Network in L-PBF Additive Manufacturing
Three typical types of defects, i.e., keyholes, lack of fusion (LoF), and gas-entrapped pores (GEP), characterized by various features (e.g., volume, surface area, etc.), are generated under different process parameters of laser beam powder bed fusion (L-PBF) processes in additive manufacturing (AM). The different types of defects deteriorate the mechanical performance of L- PBF components, such as fatigue life, to a different extent. However, there is a lack of recognized approaches to classify the defects automatically and accurately in L-PBF components. This work presents a novel hierarchical graph convolutional network (H-GCN) to classify different types of defects by a cascading GCN structure with a low-level feature (e.g., defect features) layer and a high-level feature (e.g., process parameters) layer. Such an H-GCN not only leverages the multi- level information from process parameters and defect features to classify the defects but also explores the impact of process parameters on defect types and features. The H-GCN is evaluated through a case study with X-ray computed tomography (CT) L-PBF defect datasets and compared with several machine learning methods. H-GCN exhibits an outstanding classification performance with an F1-score of 1.000 and reveals the potential effect of process parameters on three types of defects.
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- Award ID(s):
- 2134689
- NSF-PAR ID:
- 10438211
- Date Published:
- Journal Name:
- 2022 International Solid Freeform Fabrication Symposium
- Page Range / eLocation ID:
- 1568-1580
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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