Fatigue‐Life Prediction of Additively Manufactured Material: Effects of Defect Spacing and Multi‐Site Damage

ABSTRACT Fatigue‐life prediction of additively manufactured metals is challenging due to the random distribution of process‐induced defects, whose proximity can significantly influence crack initiation and propagation, and whose multiplicity can promote multi‐site damage. Prediction approaches based on single‐crack growth often overestimate fatigue life by neglecting defect interactions and multi‐site crack propagation, particularly in low cycle fatigue and mid‐life regimes, where crack coalescence and stress field interactions play a crucial role. This study integrates small‐ and large‐crack growth test data with defect characteristics, including size and spacing obtained from X‐ray computed tomography, to estimate the fatigue life of Ti‐6Al‐4V fabricated via laser directed energy deposition. The plasticity‐induced crack closure model, FASTRAN, was employed to simulate crack propagation and compare single‐crack and multi‐crack approaches in fatigue‐life prediction. The multi‐site damage approach, which explicitly accounts for defect spatial distribution through nearest neighbor distance analysis, significantly improves fatigue‐life predictions in low cycle fatigue and mid‐life regimes, demonstrating strong agreement with experimental data.

Authors

Institutions

Publication Details

Journal
Fatigue & Fracture of Engineering Materials & Structures
Published
2025-09-01
DOI
https://doi.org/10.1111/ffe.70067
Primary Topic
Additive Manufacturing Materials and Processes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fatigue‐Life Prediction of Additively Manufactured Material: Effects of Defect Spacing and Multi‐Site Damage

James C. Newman, Aref Yadollahi, Leah Buiter
5 links
Fatigue & Fracture of Engineering Materials & Structures
Additive Manufacturing Materials and Processes
article

Fatigue‐Life Prediction of Additively Manufactured Material: Effects of Defect Spacing and Multi‐Site Damage

James C. Newman, Aref Yadollahi, Leah Buiter
article en

Abstract

ABSTRACT Fatigue‐life prediction of additively manufactured metals is challenging due to the random distribution of process‐induced defects, whose proximity can significantly influence crack initiation and propagation, and whose multiplicity can promote multi‐site damage. Prediction approaches based on single‐crack growth often overestimate fatigue life by neglecting defect interactions and multi‐site crack propagation, particularly in low cycle fatigue and mid‐life regimes, where crack coalescence and stress field interactions play a crucial role. This study integrates small‐ and large‐crack growth test data with defect characteristics, including size and spacing obtained from X‐ray computed tomography, to estimate the fatigue life of Ti‐6Al‐4V fabricated via laser directed energy deposition. The plasticity‐induced crack closure model, FASTRAN, was employed to simulate crack propagation and compare single‐crack and multi‐crack approaches in fatigue‐life prediction. The multi‐site damage approach, which explicitly accounts for defect spatial distribution through nearest neighbor distance analysis, significantly improves fatigue‐life predictions in low cycle fatigue and mid‐life regimes, demonstrating strong agreement with experimental data.

Fatigue & Fracture of Engineering Materials & StructuresVol. 48(11)
Purdue University Northwest (US), Mississippi State University (US)
Openalex Percentile: Top 77%
Additive Manufacturing Materials and Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.