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Record W4415303664 · doi:10.1177/16878132251364690

Effect of solution and aging treatments on the mechanical properties, and fracture behavior of additively manufactured maraging steel

2025· article· en· W4415303664 on OpenAlexaff
Narges Omidi, Pedram Farhadipour, Mohamed Meher Monjez, Asim Iltaf, Noureddine Barka, Abderrazak El Ouafi

Bibliographic record

VenueAdvances in Mechanical Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsMaraging steelAnnealing (glass)Indentation hardnessFractographyDimpleBrittlenessNecking

Abstract

fetched live from OpenAlex

Regarding the importance of annealing and solution treatments on the final microhardness of maraging steel, this study investigates the impact of different treatment times and temperatures on the microhardness of additively manufactured maraging steel. Annealing temperatures ranged from 815°C to 845°C, annealing times from 60 to 120 min, aging temperatures from 540°C to 580°C, and aging times from 90 to 180 min. The study examines the unique features of the fractured surface and the behavior of maraging steel after these treatments, as well as the critical limits of these treatments on the microstructure and fracture behavior. Results show that the oxygen diffusion rate in additively manufactured material is significantly higher than in bulk material, leading to considerable oxide formation, especially in porosities, after heat treatment. This phenomenon could contribute to porosity regrowth, resulting in abnormal dimple sizes in the fractography of additively manufactured maraging steel. An aging temperature of 580°C is critical, leading to severe necking under tensile load, resulting in low UTS, microhardness, and elongation, despite extreme softness and brittle fracture behavior. Aging temperature is identified as the most effective parameter on microhardness, with increasing temperatures reducing it. Annealing time and temperature have the opposite effect, with maximum microhardness at lower aging and higher annealing temperatures.

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 score0.274
Threshold uncertainty score0.590

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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