Evidence map›Paper›PMID 39920253›Full record

ArticleScientific reports2025

Investigation and prediction of fatigue performance of SLM 316 L stainless steel based on small build orientation variations and heat treatment effects.

Andrei Yankin, Asma Perveen, Didier Talamona

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Andrei YankinDepartment of Mechanical & Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
Asma PerveenDepartment of Mechanical & Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan.
Didier TalamonaDepartment of Mechanical & Aerospace Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan. didier.talamona@nu.edu.kz.

Funding

Nazarbayev University 211123CRP1615
6 · The paper itself

Abstract

Additive manufacturing (3D printing) has facilitated the creation of complex structures, including lattices with diverse build angles. Since lattices often face quasi-static and cyclic loads, understanding material properties across a wide range of printing angles, post-processing conditions, and loading scenarios is crucial. This study explores the effects of building orientations beyond conventional 0, 45, and 90 degrees on the mechanical properties of 316 L steel, produced by laser powder bed fusion. Quasi-static and cyclic tensile tests were performed to evaluate mechanical behavior in as-built and heat-treated conditions, followed by predictive model development. The ultimate strength showed a modest variation of 7% in the as-built condition, while the heat-treated state exhibited a greater variation of 13%. Fatigue tests indicated minor differences between conditions in the low-cycle region but larger gaps in the high-cycle region, where heat-treated samples generally showed superior performance. Inclination angle had a greater effect on fatigue life in the as-built state, with horizontal part orientations outperforming vertical ones. The predictive model demonstrated robust reliability, with nearly 90% of data within an accepted scatter factor of less than three. Requiring minimal experimental data (two fatigue curves per condition), the model is valuable for forecasting fatigue behavior in complex lattice structures.

Indexed as

316L stainless steelAnnealing Heat TreatmentBuild orientationFatigue of materialsOutcome prediction modelSelective laser melting

Identifiers

PMID39920253
PMCPMC11805919

What OpenQuestion holds

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Registered trials

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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.