Directly from surface to bulk: rapid prediction of internal densification in laser powder bed fusion additively manufactured nickel-based superalloy using machine learning

Directly from surface to bulk: rapid prediction of internal densification in laser powder bed fusion additively manufactured nickel-based superalloy using machine learning

For the quantitative evaluation of processing quality of laser additive manufacturing, traditional characterisation methods such as Archimedes method and computed tomography (CT) face limitations in testing volume-constrained components, while optical imaging methods require destructive sample prepa...

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Bibliographic Details
Main Authors: Longhai Hu, Keyu Shi, Dongdong Gu, Han Zhang, Miao Chang, Menghuan Yin, Jinming Xing, Ruiqi Wang, Kai Tang, Kuanyang Feng, Xuechun Hei
Format: Article
Language:English
Published: Taylor & Francis Group 2025-08-01
Series:Virtual and Physical Prototyping
Subjects:
Laser powder bed fusion
GH4169
surface morphology
internal defects
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/17452759.2025.2541829
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