Deep learning based porosity prediction for additively manufactured laser powder-bed fusion parts
Machine learning techniques are extensively used to understand and predict complex non-linear phenomena across various applications. Moreover, these techniques minimize the time and costs associated with experimental and numerical analysis. In this work, a deep learning technique, specifically artif...
Main Authors: | Anwaruddin Siddiqui Mohammed, Mosa Almutahhar, Karim Sattar, Ali Alhajeri, Aamer Nazir, Usman Ali |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2023-11-01
|
Series: | Journal of Materials Research and Technology |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423028090 |
Similar Items
-
A review and a statistical analysis of porosity in metals additively manufactured by laser powder bed fusion
by: Dawei Wang, et al.
Published: (2022-10-01) -
Investigation of Causal Relationships between Printing Parameters, Pore Properties and Porosity in Laser Powder Bed Fusion
by: Rong Zhao, et al.
Published: (2023-02-01) -
Tomography of Laser Powder Bed Fusion Maraging Steel
by: Pablo M. Cerezo, et al.
Published: (2024-02-01) -
Powder surface area and porosity/
by: 184745 Lowell, S., et al.
Published: (1984) -
Heterogeneous sensor data fusion for multiscale, shape agnostic flaw detection in laser powder bed fusion additive manufacturing
by: Benjamin Bevans, et al.
Published: (2023-12-01)