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contributor authorHu, Zhen
contributor authorNannapaneni, Saideep
contributor authorMahadevan, Sankaran
date accessioned2022-05-08T08:40:35Z
date available2022-05-08T08:40:35Z
date copyright12/27/2021 12:00:00 AM
date issued2021
identifier issn2332-9017
identifier otherrisk_008_01_010301.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4284199
description abstractAdditive manufacturing (AM) represents a new paradigm of manufacturing where parts are manufactured using their 3D models (such as CAD models) by joining materials in a layer-by-layer manner. Widespread implementation of additive manufacturing requires robust techniques for performance evaluation, quality control, and certification. AM product quality is impacted by multiple uncertainty sources that are present at various stages of the manufacturing process, such as raw materials, process equipment, process parameters, process simulation models, and sensor measurements. Therefore, techniques of uncertainty quantification and management (UQ&
description abstractM) are essential for the quality control and certification of additive manufacturing processes. With the development of advanced simulation techniques, artificial intelligence, and big data analytics, new UQ&
description abstractM approaches are emerging to enable model-based quality control and certification, data-driven quality monitoring, and AI-based quality assurance in additive manufacturing. This special issue covers various recent advances in the field of UQ&
description abstractM with a focus on additive manufacturing.
publisherThe American Society of Mechanical Engineers (ASME)
titleSpecial Issue on Uncertainty Quantification and Management in Additive Manufacturing
typeJournal Paper
journal volume8
journal issue1
journal titleASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg
identifier doi10.1115/1.4053183
journal fristpage10301-1
journal lastpage10301-2
page2
treeASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2021:;volume( 008 ):;issue: 001
contenttypeFulltext


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