@inproceedings{4d16d58956674e6e8c7ac3640aefffb4,
title = "Probabilistic Modelling of Defects in Additive Manufacturing: A Case Study in Powder Bed Fusion Technology",
abstract = "Implementation of additive manufacturing into product manufacturing suffers from the challenge of part defects prediction. Due to interdependencies of design variables and manufacturing parameters in achieving suitable part quality, modelling methods are necessary to provide simulation capabilities for part quality analysis at early stages of product development. A systematic methodology is proposed to extract cause-effect relationships among variables and to transform this causal model into a Bayesian network. The Bayesian network is then used to predict the effect of specific design and manufacturing parameters on part defects and to estimate the needed input parameters backwards, based on acceptable output values. ",
author = "Hossein Mokhtarian and Azarakhsh Hamedi and Hari Nagarajan and Suraj Panicker and Eric Coatanea and Karl Haapala",
note = "jufoid=76433 EXT={"}Haapala, Karl{"} EXT={"}Hamedi, Azarakhsh{"} ; CIRP Conference on Manufacturing Systems ; Conference date: 01-01-2000",
year = "2018",
month = dec,
day = "31",
doi = "10.1016/j.procir.2019.03.234",
language = "English",
series = "Procedia CIRP",
publisher = "Elsevier",
pages = "956--961",
booktitle = "52nd CIRP Conference on Manufacturing Systems",
address = "Netherlands",
}