MétaCan
Menu
Back to cohort
Record W4292386884 · doi:10.1038/s41529-022-00280-y

Plastic injection molding dies using hybrid additively manufactured 420/CX stainless steels: electrochemical considerations

2022· article· en· W4292386884 on OpenAlexafffund
Ayda Shahriari, Javad Samei, Mehdi Sanjari, Mohammad Jahanbakht, Babak Shalchi Amirkhiz, Mohsen Mohammadi

Bibliographic record

Venuenpj Materials Degradation · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsNatural Resources CanadaMcMaster UniversityUniversity of New Brunswick
FundersAtlantic Canada Opportunities AgencyMitacs
KeywordsMaterials scienceDielectric spectroscopyIntermetallicCorrosionMetallurgyMicrostructureCarbideElectrochemical noiseElectrochemistryComposite materialChemistryElectrode

Abstract

fetched live from OpenAlex

Abstract This research focused on the corrosion resistance and microstructure of hybrid additively manufactured (HAM) samples of AISI 420/CX (420/CX SS) stainless steels. Potentiodynamic polarization, electrochemical impedance spectroscopy (EIS), and Mott–Schottky analyses as well as the electrochemical noise (EN) technique were used to evaluate the electrochemical behavior of the as-built and heat-treated HAM parts in NaCl solution. The results showed a more protective passive layer formed on the CX SS side. The distribution of Cr-rich M 23 C 6 carbides in matrix of 420 SS side resulted in a lower corrosion resistance compared to the CX SS side. The noise data analysis confirmed an increase in the galvanic currents of the HAM parts after heat treatment. The stochastic analysis revealed the interface in the heat-treated condition increases pit growth more than the as-built one due to the evolution of nano-sized intermetallic compounds of Al-N/ (Cr, Nb) (N, C) at the heat-treated interface area.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.219
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuenpj Materials DegradationSame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207