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Record W4410482800 · doi:10.3390/met15050558

The Effects of ECAP and Recovery Treatment on the Microstructure and the Mechanical, Tribological, and Corrosion Properties of 316L Steel

2025· article· en· W4410482800 on OpenAlexaff
Ata Radnia, Mostafa Ketabchi, Anqiang He, Guijiang Diao, Dongyang Li

Bibliographic record

VenueMetals · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTribologyMicrostructureMaterials scienceMetallurgyCorrosion

Abstract

fetched live from OpenAlex

316L steel is widely used in various industries and is also one of the metallic materials used for biomedical applications because of its excellent mechanical properties, corrosion resistance, and biocompatibility. This article reports a comprehensive study on the effects of equal channel angular pressing (ECAP) and subsequent recovery treatment on the microstructure and the mechanical, tribological, and corrosion properties of 316L. The process includes an initial annealing at 1050 °C for 2 h to obtain a homogenous microstructure, ECAP at room temperature with a 120° inner angle, and subsequent recovery treatment at 340 °C for 1 h. The microstructure was investigated with an optical microscope and a transmission electron microscope. The mechanical properties were evaluated with hardness and compression tests. The corrosion behavior was analyzed with dynamic polarization tests. The wear test was performed using a scratching tester, and the volume loss was measured with a profilometer. The results of the study demonstrate that the ECAP–recovery sample exhibits improved properties compared to both the annealed sample and the ECAP sample. The corrosion tests show that the ECAP sample has a corrosion resistance higher than that of the annealed sample but lower than that of the ECAP–recovery sample. The ECAP–recovery sample shows the highest wear resistance and corrosive wear resistance among the three samples.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.113

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.193
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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