Low-Cycle Fatigue Life Prediction of Titanium-Based Intermetallic Alloys Using Machine Learning and Finite Element Analysis.

Qiwen Xu, Guoqian Song, Xingwu Li, Yanju Wang, Aixue Sha, Yuanyuan Wei, Wenfeng Hao

Abstract read

In one paragraph

Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Research Progress in Biomedical Materials.Biomolecules · 2026
Review
Stress-Strain Hysteresis Loop-Based Machine Learning Models for Predicting Metal Fatigue Life Under Uncertainty.Materials (Basel, Switzerland) · 2025
Article
A Surrogate Artificial Neural Network Model for Estimating the Fatigue Life of Steel Components Based on Finite Element Simulations.Materials (Basel, Switzerland) · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Qiwen XuCollege of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.

Guoqian SongCollege of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.

Xingwu LiMaterials Evaluation Center for Aeronautical and Aeroengine Application, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, China.

Yanju WangMaterials Evaluation Center for Aeronautical and Aeroengine Application, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, China.

Aixue ShaMaterials Evaluation Center for Aeronautical and Aeroengine Application, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, China.

Yuanyuan WeiCollege of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.

Wenfeng HaoCollege of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China.ORCID 0000-0001-6738-4991

Funding

Financial support from the WDZC program of BIAM 2019-363National Science and Technology Major Project J2009-VI-0003-0116the financial support provided by Central Guiding Local Science and Technology Development Funds of China 2024ZY0163

6 · The paper itself

Abstract

This study explores the low-cycle fatigue characteristics of three structural components fabricated from Ti

Indexed as

fatigue damagefinite elementmachine learningTi2AlNb-based alloystitanium alloys

Identifiers

PMID40333551

DOI10.3390/ma18081887

PMCPMC12028867

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.