Recurrence Network Analysis of Design-quality Interactions in Additive Manufacturing

Published

April 12, 2021

Author(s)

Yan Lu, Ruimin Chen, Hui Yang

Abstract

Powder bed fusion (PBF) additive manufacturing (AM) provides a greater level of flexibility in the design-driven build of metal products. However, the more complex the design is, the more difficult to control the quality of AM builds. The quality challenge persistently hampers the widespread application of AM technology. Advanced imaging (e.g., X-ray computed tomography scans and high- resolution optical images) has been increasingly invested to enhance the visibility of information and improve the AM quality control. However, realizing the full potential of imaging data depends on the advent of information processing methodologies for the analysis of design-quality interactions. This paper presents a design of AM experiment to investigate how design parameters (e.g., build orientation, thin-wall width, thin-wall height, and hatching distance) interact with quality characteristics in thinwall builds. First, we develop a novel generalized recurrence network (GRN) to represent the AM spatial image data. Then, GRN quantifiers, namely degree, betweenness, pagerank, closeness, and eigenvector centralities, are extracted to characterize the quality of layerwise builds. Further, we establish a regression model to predict how the design complexity impacts GRN behaviors in each layer of thin-wall builds. Experimental results show that network features are sensitive to build orientations, width, height, and hatching distance under the significant level α = 0.05. Thin-walls with the width bigger than 0.1 mm printed under orientation 0◦ are found to yield better quality compared to 60◦ and 90◦. Also, thin-walls build with orientation 60◦ are more sensitive to the changes in hatching distance compare to the other two orientations. As a result, the orientation 60◦ should be avoided while printing thin-wall structures. The proposed design-quality analysis shows great potential to optimize engineering design and enhance the quality of PBF-AM builds.

Citation

Additive Manufacturing

Volume

39

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.addma.2021.101861

Local Download

Keywords

Additive Manufacturing, Recurrence Network, Design of Experiments, Engineering Design, Quality Control, Thin-wall Structure

Citation

Lu, Y. , Chen, R. and Yang, H. (2021), Recurrence Network Analysis of Design-quality Interactions in Additive Manufacturing, Additive Manufacturing, [online], https://doi.org/10.1016/j.addma.2021.101861, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931064 (Accessed May 7, 2026)

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