Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms
The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process...
Main Authors: | Andrea Tridello, Alberto Ciampaglia, Filippo Berto, Davide Salvatore Paolino |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/13/7/4294 |
Similar Items
-
Comparison study on additive manufacturing (AM) and powder metallurgy (PM) AlSi10Mg alloys
by: Chen, Biao, et al.
Published: (2020) -
Numerical investigation on effect of different projectile nose shapes on ballistic impact of additively manufactured AlSi10Mg alloy
by: Mahesh Naik, et al.
Published: (2024-01-01) -
A Comparative Study of Fatigue Energy Dissipation of Additive Manufactured and Cast AlSi10Mg Alloy
by: Chunxia Yang, et al.
Published: (2021-08-01) -
Influence on Fatigue Strength of Post-Process Treatments on Thin-Walled AlSi10Mg Structures Made by Additive Manufacturing
by: Nicola Spignoli, et al.
Published: (2023-01-01) -
Fatigue Lifetime Analysis of a Bicycle Frame Made by Additive Manufacturing Technology from AlSi10Mg
by: Matúš Margetin, et al.
Published: (2022-07-01)