Influence of porosity on the fatigue life of laser powder bed fusion–produced Ti6Al4V

Becker, Thorsten Hermann; Dhansay, Nur Mohamed

doi:10.1002/mdp2.141

Cited by 4 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fatigue resistance of AM metals is very sensitive to the presence of process-induced defects, which severely degrade their cyclic performance [8,9]. Compared to defects, the microstructure of the AM metals plays a secondary role in their fatigue properties [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Predictive Damage-Tolerant Approach for Fatigue Life Estimation of Additive Manufactured Metal Materials

Psihoyos

Lampeas

2023

Metals

View full text Add to dashboard Cite

Metal Additive Manufacturing (AM) allows the fabrication of intricate shaped parts that cannot be produced with conventional manufacturing techniques. Despite the advantages of this novel manufacturing technology, the main drawback is the inferior fatigue performance of AM metal materials and parts due to the presence of process-induced defects that act as initial cracks. Reliable fatigue modeling methods that can assist the design and characterization of AM components must be developed. In this work, a computational damage-tolerance framework for the fatigue analysis of the AM metals and parts is presented. First, thermal modeling of the AM process for the part fabrication is performed to predict the susceptible areas for defect formation in the parts. From the processing of results, the characteristics of the critical defect are determined and used as input in a fracture mechanics-based model for the prediction of fatigue life of AM metals and parts. For validation purposes, the framework is utilized for the fatigue modeling and analysis of AM Ti-6Al-4V and 316L SS metals of relative experimental test cases found in the literature. The predicted results exhibit good correlation with the available experimental data, demonstrating the predictive capability of the modeling procedure.

show abstract