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Record W4366572636 · doi:10.1177/09544062231166839

Study of parametric effect on laser directed energy deposited Austenitic steel and Inconel functionally graded material

2023· article· en· W4366572636 on OpenAlexaff
Nehem Tudu, Mayuri Baruah, Shashi Bhushan Prasad, C. P. Paul, K. S. Bindra

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceScanning electron microscopeAusteniteNanoindentationIndentation hardnessInconelMetallurgyEnergy-dispersive X-ray spectroscopyInconel 625Deposition (geology)Austenitic stainless steelComposite materialOptical microscopeMicrostructureCorrosion

Abstract

fetched live from OpenAlex

In this paper, the effect of process parameter on functionally graded materials (FGMs) of Inconel 625 (IN625) and Austenitic steel (SS304L) deposited by laser directed energy deposition (LDED) are investigated. For this purpose, FGM walls of different gradient of mix of IN625 and SS304L at different layers are deposited on Austenitic steel (SS316L) substrate at different sets of process parameters. For this study, the process parameters considered are laser power ( P L ): 1000, 1300 and 1600 W, and scanning speed ( v): 0.3, 0.5 and 0.7 m/min. Deposited samples are then subjected to different tests such as energy dispersive spectroscopy (EDS), microhardness, scanning electron microscopy (SEM), X-ray diffraction (XRD), optical microscopy (OM), and nanoindentation. Tests performed on the samples revealed different effects of parameters on the deposition of the FGM wall. Based on the comparison of results of mechanical and metallurgical tests, it is observed that either at P L = 1300 W and/or v = 0.5 m/min, among the considered process parameters, show comparatively better results than the others.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.161
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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
Published2023
Admission routes1
Has abstractyes

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