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Record W4327941906 · doi:10.3390/jmmp7020071

Weldability of 316L Parts Produced by Metal Additive Manufacturing

2023· article· en· W4327941906 on OpenAlex
Hamdi Selmi, Jean Brousseau, Gabriel Caron-Guillemette, Stéphane Goulet, J. G. Desjardins, Claude Belzile

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsAlstom (Canada)Transport CanadaUniversité du Québec à Rimouski
Fundersnot available
KeywordsWeldabilityRapid prototypingWelding3D printingManufacturing engineeringProcess (computing)FusionProcess engineeringMaterials scienceFabricationFusion weldingMechanical engineeringComputer scienceEngineering drawingEngineeringMetallurgyComposite material

Abstract

fetched live from OpenAlex

The processes of metal additive manufacturing (AM) are no longer confined to rapid prototyping applications and are seeing increasing use in many fields for the production of tools and finished products. The ability to design parts with practically zero waste, high precision, complex geometry, and on-demand fabrication are among the advantages of this manufacturing approach. One of the drawbacks of this technique is the productivity rate, as the parts are made layer by layer, which also increases the production cost. Moreover, even the working space is limited, especially for the powder bed fusion technique. In view of these disadvantages and in order to guarantee the profitability of this process, it should be oriented to the production of complex components that have a limited volume with a design adapted to additive manufacturing. One solution with which to circumvent these drawbacks is to combine the 3D printing process with conventional manufacturing processes. When designing products, one may choose to use additive manufacturing to create locally complex parts and assemble them with parts produced by conventional processes. On the other hand, and due to the limited AM printing chamber space, it may be necessary to print large parts in multiple smaller parts and then assemble them. In order to investigate the weldability of stainless steel 316L parts produced by laser powder bed fusion (L-PBF), the mechanical behavior of different welding assemblies is tested. Five configurations are studied: non-welded AM specimens, two AM parts welded together, one AM part and one laser cut part welded together, two laser-cut parts welded together, and non-welded laser cut specimens. Welding is performed using the Pulsed Gas Metal Arc Welding process (GMAW-P). Specimen strength is assessed through static and fatigue tests. The results demonstrate that 316L AM parts are weldable, and the tensile and fatigue properties of L-PBF 316L welded components and welded laser cut components are comparable. GMAW-P welding led to lower fatigue results for AM components than for other configurations, but the difference is not important. It was observed that welding defects may have a direct impact on mechanical properties.

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.

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)0.000
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 score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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