#7303. Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

October 2026publication date
Proposal available till 27-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Industrial and Manufacturing Engineering;
Artificial Intelligence;
Software;
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Abstract:
There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. It is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters.
Keywords:
Additive manufacturing; Creep; Machine learning; Nickel superalloy; Predictability; Process–structure–property relationship

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