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contributor authorGhumman, Umar Farooq
contributor authorFang, Lichao
contributor authorWagner, Gregory J.
contributor authorChen, Wei
date accessioned2023-08-16T18:39:52Z
date available2023-08-16T18:39:52Z
date copyright2/13/2023 12:00:00 AM
date issued2023
identifier issn1087-1357
identifier othermanu_145_6_061002.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4292287
description abstractAdditive manufacturing (AM) simulations offer an alternative to expensive AM experiments to study the effects of processing conditions on granular microstructures. Existing AM simulations lack support from reliable validation techniques. The stochastic nature and spatial heterogeneity of microstructures make it difficult to validate the simulated microstructures against experimentally obtained images through statistical measures such as average grain size. Another challenge is the lack of reliable and automated methods to calibrate the model parameters, which are unknown and difficult to measure directly from experiments. To overcome these two challenges, we first present a novel metric to quantify the difference between granular microstructures. Then, using this metric in conjunction with Bayesian optimization, we present a framework that can be used to reliably and efficiently calibrate the model parameters. We employ this framework to first calibrate the substrate microstructure simulation and then the laser scan microstructure simulation for Inconel 625. Results show that the framework allows successful calibration of the model parameters in just a small number of simulations.
publisherThe American Society of Mechanical Engineers (ASME)
titleCalibration of Cellular Automaton Model for Microstructure Prediction in Additive Manufacturing Using Dissimilarity Score
typeJournal Paper
journal volume145
journal issue6
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4056690
journal fristpage61002-1
journal lastpage61002-11
page11
treeJournal of Manufacturing Science and Engineering:;2023:;volume( 145 ):;issue: 006
contenttypeFulltext


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