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Record W4406198902 · doi:10.1016/j.msea.2024.147782

High strain rate compressive behavior of laser powder bed fused Inconel-718

2025· article· en· W4406198902 on OpenAlexafffund
Navid Hasani, C. Dharmendra, Reza Alaghmandfard, Mohsen Keshavarzan, Foroozan Forooghi, Mehdi Sanjari, Babak Shalchi Amirkhiz, G.D. Janaki Ram, Hadi Pirgazi, Léo Kestens, A.G. Odeshi, Mohsen Mohammadi

Bibliographic record

VenueMaterials Science and Engineering A · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNatural Resources CanadaBritish Columbia Institute of TechnologyUniversity of SaskatchewanUniversity of New Brunswick
FundersAtlantic Canada Opportunities Agency
KeywordsMaterials scienceInconelCompressive strengthStrain rateStrain (injury)Composite materialLaserOpticsMedicineAlloy

Abstract

fetched live from OpenAlex

Inconel-718 (IN718) is extensively utilized in the aerospace industry, notably in applications such as aircraft engines, facing a constant risk of foreign object impact loadings. Limited studies exist on the dynamic behavior of IN718 under high strain rate loadings, crucial for addressing the challenges of elevated operational temperatures and impact risks in aircraft engines. The dynamic deformation behavior of IN718 samples processed by laser powder bed fusion (LPBF) was studied at varying strain rates. True stress-strain curves showed rapid flow stress increase and semi-serrated stress-strain curves due to strain hardening and thermal softening competition. AMS 5664 heat treatment borrowed from the aerospace materials specifications (AMS) for nickel alloys led to a 28% increase in ultimate compressive strength (UCS) at high strain rates. The aging treatment led to precipitation of uniformly distributed strengthening γ" and γ' phases. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) investigations revealed adiabatic shear band (ASB) formation during high strain-rate deformation, indicating local temperature rise. High-density dislocation networks and nanoscale γ" and γ' precipitates enhanced IN718 strength by inhibiting dislocation motion. Electron backscatter diffraction (EBSD) analysis highlighted texture changes, and the impact of strain rate on grain size distribution was observed. Slip activity increased after heat treatment, influencing ductility. Analysis of twins, kernel average misorientation (KAM), low-angle grain boundaries (LAGBs), and high-angle grain boundaries (HAGBs) were performed to investigate their contribution to the strength properties. Fracture surface analysis at 5150 s −1 revealed a complex mechanism, with outer regions exhibiting ductile features and inner regions indicating shear fracture. The Chang-Asaro (CA) model predicted IN718 flow behavior under high strain rates, subsequently incorporated into ABAQUS Explicit software for numerical simulation. Lagrangian smoothed particle hydrodynamics (SPH) in combination with the VUHARD subroutine were employed to simulate the SHPB experiments. The constitutive model incorporated in the subroutine accurately captured the nonlinear behavior of the specimens, such as equivalent plastic strain and temperature. The results demonstrated a strong validation between the experimental and numerical methodologies. • The dynamic mechanical behavior of IN718 at high strain rates was investigated. • Heat treatment increased the toughness in IN718 and resulted in a 28% UCS boost. • The strain-rate effect on dynamic deformation and fracture strain were observed. • Texture analysis indicated a shift from {100} to {110} with an increased strain rate. • Chang-Asaro and Lagrangian SPH were used for constitutive modeling and simulation.

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 score0.009
Threshold uncertainty score0.656

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 head0.207
Teacher spread0.201 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

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