Machine Learning of Fatigue Strength of Hybrid and Additively Manufactured Aluminum Alloys in VHCF Regime-Reference-Cited by-同舟云学术

Machine Learning of Fatigue Strength of Hybrid and Additively Manufactured Aluminum Alloys in VHCF Regime

Published:2023-05-01 Issue: Volume: Page:262-276
ISSN:
Container-title:Advances in Accelerated Testing and Predictive Methods in Creep, Fatigue, and Environmental Cracking
language:
Short-container-title:

Author:

Awd Mustafa¹^ORCID,Walther Frank¹^ORCID

Affiliation:

1. TU Dortmund University, NRW, D-44227, DE

Publisher

ASTM International

Reference34 articles.

1. A Review of Hybrid Manufacturing Processes—State of the Art and Future Perspectives;Zhu;International Journal of Computer Integrated Manufacturing,2013

2. J. O. Milewski, Additive Manufacturing of Metals: From Fundamental Technology to Rocket Nozzles, Medical Implants, and Custom Jewelry (Cham, Switzerland: Springer International, 2017).

3. Evaluation of Surface/Interface Quality, Microstructure and Mechanical Properties of Hybrid Additive-Subtractive Aluminium Parts;Bhaduri;CIRP Annals,2019

4. AIMS: A Metal Additive-Hybrid Manufacturing System: System Architecture and Attributes;Manogharan;Procedia Manufacturing,2015

5. A Novel Method for Additive/Subtractive Hybrid Manufacturing of Metallic Parts;Du;Procedia Manufacturing,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A systematic review on high cycle and very high cycle fatigue behavior of laser powder bed fused (L-PBF) Al-Si alloys;Engineering Failure Analysis;2023-12

2. A review on the enhancement of failure mechanisms modeling in additively manufactured structures by machine learning;Engineering Failure Analysis;2023-09