Recurrence network analysis of design-quality interactions in additive manufacturing

Powder bed fusion (PBF) additive manufacturing (AM) provides a great level of flexibility in the design-driven build of metal products. However, the more complex the design, the more difficult it becomes 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 explored to enhance the visibility of information and improve the AM quality control. 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 contour space) interact with quality characteristics in thin-wall builds. Note that the build orientation refers to the position of thin-walls in relation to the recoating direction on the plate, and the contour space indicates the width between rectangle hatches. 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 contour space under the significant level alpha = 0.05. Thin-walls with the width bigger than 0.1 mm printed under orientation 0 degrees are found to yield better quality compared to 60 degrees and 90 degrees. Also, thin-walls build with orientation 60 degrees are more sensitive to the changes in contour space compare to the other two orientations. As a result, the orientation 60 degrees 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.

Automated summary

Powder bed fusion additive manufacturing offers design flexibility for metal products, but controlling quality becomes challenging with complex designs. This study explores advanced imaging for improved quality control and introduces a novel generalized recurrence network for analyzing the interaction between design parameters and quality characteristics in thin-wall builds. The results demonstrate sensitivity of network features to build orientations, width, height, and contour space, providing insights for optimizing engineering design and enhancing build quality.