Print Fidelity Metrics for Additive Manufacturing of Cement-based Materials
Edward Garboczi, Newell Moser, Joseph Biernacki, Hajar Afarani, Ebrahim Esfahani
Additively manufactured cement-based structures for infrastructure applications suffer from in-construction shape deformations, which are a strong function of process conditions and the rheology of the printing material (cement paste, mortar, or concrete). Thus, characterization of the shape of such manufactured objects is critical to establish and ensure fidelity to the original CAD model. In this study, a number of quantitative metrics were used to compare the dimensional and shape accuracy of laboratory-scale printed cement-based objects. A new method is described that uses the axes of minimum moment of inertia and the centroid as the basis for aligning and comparing objects. X-ray computed tomography (XCT) data was used to characterize both internal and external features. Details of the logic and image processing requirements are given and typical sample irregularities that lead to quantification uncertainty are illustrated. The effect of sampling statistics on metric confidence was studied and guidelines are provided for good sampling protocols. The results show the extent to which different penalty logics provide sensitivity for the detection of specific types of flaws. Furthermore, when the minimum moment of inertia is used as the basis for alignment and comparison, a high correlation is found between boundary-based and volume-based fidelity metrics. Such quantitative printability metrics are necessary to establish a basis for evaluating the repeatable shape fidelity of 3D-printed objects and for quantitatively studying how rheology affects both the manufacturing process and the final built part. The method is illustrated for benchmark printed objects fabricated using three hydrogel forming polymers as printing aids.
, Moser, N.
, Biernacki, J.
, Afarani, H.
and Esfahani, E.
Print Fidelity Metrics for Additive Manufacturing of Cement-based Materials, Additive Manufacturing, [online], https://doi.org/10.1016/j.addma.2022.102784, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932539
(Accessed December 8, 2023)