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Record W2994961930

Characterization of Laser-Cladded AISI 420 Martensitic Stainless Steel for Additive Manufacturing Applications

2019· article· en· W2994961930 on OpenAlexfundno aff
Mohammad K. Alam

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsnot available
FundersUniversity of WaterlooGovernment of OntarioUniversity of Windsor
KeywordsMetallurgyMaterials scienceMartensitic stainless steelLaserMartensiteCharacterization (materials science)MicrostructureNanotechnologyOptics
DOInot available

Abstract

fetched live from OpenAlex

Laser cladding is an additive manufacturing (AM) process that uses lasers to melt and deposit metallic powders in layer by layer to coat a substrate or to build three dimensional object. However, the AM industry encounters problems in handling residual stresses in the cladded parts or coating that lead to high hardness and distortion. Also, anisotropic properties developed in the laser-cladded AM parts are a challenge to use them as a functional component. This study aims to understand those problems with the laser-cladding AM process using AISI 420 martensitic stainless steel (MSS) powder in a coaxial direct powder deposition method. Primarily, this study focuses on the effect of process parameters, microstructural evolution, and associated residual stress development in the single bead of laser-cladded 420 MSS. Subsequently, the study was expanded to analyze the mechanical behavior of additive manufactured 3D samples using systematic approaches with X-ray diffraction, scanning and transmission electron microscopy (SEM/TEM), electron backscattered diffraction (EBSD) and MTS mechanical testing frame. This study revealed that laser speed has the most significant effect on the microhardness, while the powder feed rate has the most significant effect on the bead geometry. A detailed TEM study discovered various morphologies of martensitic phases that explained the reason behind the development of residual stress throughout the three zones, such as bead zone (BZ), dilution zone (DZ), and heat affected zone (HAZ) in a single bead clad. A high profile tensile residual stress (310–486 MPa) was observed in the upper BZ, while compressive stress (420–1000 MPa) was seen in the rest of the BZ and the DZ. This laser-cladded stainless steel vi showed a ~16% increase in yield strength (YS ~ 521 MPa), ~ 63% increase in tensile strength (TS ~ 1774 MPa), and a ~ 22% increase in ductility in terms of percentage of area reduction when compared with a similar 420 commercial grade MSS (YS - 483 MPa, TS - 1087 MPa), in the rolling direction with pre-hardened condition. The study showed that a post-cladding heat treatment at 565 °C for an hour reduced the tensile residual stress substantially in a single bead clad. A similar heat treatment also improved the fracture mode of 3D AM sample from brittle to ductile fracture and changed the anisotropic properties of the as-cladded sample in the transverse direction. This indicated that for design purposes, a simple post-cladding heat treatment (at 565 °C for an hour) is very important to minimize the anisotropy in the mechanical properties of as-cladded transverse sample. Also, it showed that a parts building technique with 30° angle to the base improved the ultimate tensile strength and partially eliminated the directionality issue. These findings could be important information for the designers with respect to “design for AM strategies.” It is expected that the above findings will be useful for the laser-based additive manufacturing application of AISI 420 martensitic stainless steel in designing functional components. However, the ratio of the yield strength vs. tensile strength of as-cladded AM sample needs to be improved to use this AM alloy in potential automotive applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.134
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.009
GPT teacher head0.185
Teacher spread0.176 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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