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contributor authorWu, Nicholas
contributor authorWhalen, Brendan
contributor authorMa, Ji
contributor authorBalachandran, Prasanna V.
date accessioned2024-12-24T19:02:26Z
date available2024-12-24T19:02:26Z
date copyright7/22/2024 12:00:00 AM
date issued2024
identifier issn1530-9827
identifier otherjcise_24_11_111001.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303184
description abstractIn this work, we develop an efficient computational framework for process space exploration in laser powder bed fusion (LPBF) based additive manufacturing technology. This framework aims to find suitable processing conditions by characterizing the probability of encountering common build defects. We employ a Bayesian approach toward inferring a functional relationship between LPBF processing conditions and the unobserved parameters of laser energy absorption and powder bed porosity. The relationship between processing conditions and inferred laser energy absorption is found to have good correspondence to the literature measurements of powder bed energy absorption using calorimetric methods. The Bayesian approach naturally enables uncertainty quantification and we demonstrate its utility by performing efficient forward propagation of uncertainties through the modified Eagar–Tsai model to obtain estimates of melt pool geometries, which we validate using out-of-sample experimental data from the literature. These melt pool predictions are then used to compute the probability of occurrence of keyhole and lack-of-fusion based defects using geometry-based criteria. This information is summarized in a probabilistic printability map. We find that the probabilistic printability map can describe the keyhole and lack-of-fusion behavior in experimental data used for calibration, and is capable of generalizing to wider regions of processing space. This analysis is conducted for SS316L, IN718, IN625, and Ti6Al4V using melt pool measurement data retrieved from the literature.
publisherThe American Society of Mechanical Engineers (ASME)
titleProbabilistic Printability Maps for Laser Powder Bed Fusion Via Functional Calibration and Uncertainty Propagation
typeJournal Paper
journal volume24
journal issue11
journal titleJournal of Computing and Information Science in Engineering
identifier doi10.1115/1.4063727
journal fristpage111001-1
journal lastpage111001-12
page12
treeJournal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 011
contenttypeFulltext


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