Evidence map›Paper›PMID 38473493›Full record

ArticleMaterials (Basel, Switzerland)2024

Numerical Study of Crack Prediction and Growth in Automotive Wheel Rims.

Soufiane Montassir, Hassane Moustabchir, Ahmed El Khalfi, Sorin Vlase, Maria Luminita Scutaru

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2024. 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. X-IGA Used for Orthotropic Material Crack Growth.Materials (Basel, Switzerland) · 2024
    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

5 authors.

Soufiane MontassirDepartment of Mechanical Engineering, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco.ORCID 0000-0001-7572-3535
Hassane MoustabchirLaboratory of Systems Engineering and Applications (LISA), National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco.
Ahmed El KhalfiDepartment of Mechanical Engineering, Faculty of Science and Technology, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco.ORCID 0000-0001-9861-8316
Sorin VlaseDepartment of Mechanical Engineering, Faculty of Mechanical Engineering, Transylvania University of Brasov, B-dul Eroilor 29, 500036 Brasov, Romania.ORCID 0000-0001-8679-2579
Maria Luminita ScutaruDepartment of Mechanical Engineering, Faculty of Mechanical Engineering, Transylvania University of Brasov, B-dul Eroilor 29, 500036 Brasov, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Finite element analysis has become an essential tool for simulating and understanding crack growth. This technique holds significant importance in the field of mechanical engineering, where it finds wide application in the design and optimization of structural components and material properties. This work began with the identification of critical zones and estimated the number of load life repeats through fatigue analysis, specifically applied to automotive rims utilizing innovative finite element methods. To investigate crack behavior, we are used the Extended Finite Element Method (XFEM) with the volumetric approach to compute the Stress Intensity Factor (SIF). The results obtained by our study align closely with experimental tests in terms of detecting the critical zone where a crack can appear. Our findings contribute to the understanding of fatigue behavior in automotive rims, offering new insights into their structural integrity and performance under various load conditions.

Indexed as

crackfailurewheel rimXFEM

Identifiers

PMID38473493
PMCPMC10934620

What OpenQuestion holds

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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.