A method for predicting hot tearing during alloy solidification and its application in AZ91D magnesium alloy
Publication type: Journal Article
Publication date: 2025-03-01
scimago Q1
wos Q1
SJR: 1.159
CiteScore: 8.8
Impact factor: 5.7
ISSN: 13506307, 18731961
Abstract
Hot tearing is a critical defect in casting, welding, and additive manufacturing. In this work, we introduced a tendency index of hot tearing, ε̇ḟs (the ratio of strain rate ε̇ to the rate of change of fraction of solid ḟs), and a new criterion for hot tearing propagation, ε̇ḟscritical<ε̇ḟs. To evaluate and apply our model, the hot tearing in actual AZ91D alloy castings were calculated. Experimental results are consistent with numerical predictions using the index and criterion we proposed. Mechanisms of hot tearing formation are described, factors influencing hot tearing propagation are discussed, and practical methods for mold design and process optimization in minimizing hot tearing formation are suggested.
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Total citations:
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Citations from 2024:
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(100%)
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Li T. et al. A method for predicting hot tearing during alloy solidification and its application in AZ91D magnesium alloy // Engineering Failure Analysis. 2025. Vol. 169. p. 109194.
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Su C., Gu G. A method for predicting hot tearing during alloy solidification and its application in AZ91D magnesium alloy // Engineering Failure Analysis. 2025. Vol. 169. p. 109194.
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TY - JOUR
DO - 10.1016/j.engfailanal.2024.109194
UR - https://linkinghub.elsevier.com/retrieve/pii/S1350630724012408
TI - A method for predicting hot tearing during alloy solidification and its application in AZ91D magnesium alloy
T2 - Engineering Failure Analysis
AU - Su, Chenxi
AU - Gu, Guangqi
PY - 2025
DA - 2025/03/01
PB - Elsevier
SP - 109194
VL - 169
SN - 1350-6307
SN - 1873-1961
ER -
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@article{2025_Li,
author = {Chenxi Su and Guangqi Gu},
title = {A method for predicting hot tearing during alloy solidification and its application in AZ91D magnesium alloy},
journal = {Engineering Failure Analysis},
year = {2025},
volume = {169},
publisher = {Elsevier},
month = {mar},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1350630724012408},
pages = {109194},
doi = {10.1016/j.engfailanal.2024.109194}
}