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Record W4403441501 · doi:10.1016/j.mfglet.2024.09.127

Analysis of Metal Fused Filament Fabrication process chain for 316L stainless steel

2024· article· en· W4403441501 on OpenAlex

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

VenueManufacturing Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsFabricationMaterials scienceProtein filamentProcess (computing)MetallurgyMetalChain (unit)Composite materialComputer science

Abstract

fetched live from OpenAlex

Metal Fused Filament Fabrication (MFFF) has emerged as a prominent technology in Additive Manufacturing (AM), characterized by its cost-effectiveness, versatility in creating intricate geometries using diverse materials, and widespread availability of AM machines. However, the intricate processes of making filament, printing, debinding, and sintering in MFFF pose unique challenges, with a vast number of parameters influencing each stage. Specialized companies handle these stages for research purposes, yet detailed information on the processes remains limited. The objective of this research is to conduct a meticulous examination of the parameters governing the printing, debinding, and sintering of 316L stainless steel—a material widely employed in various industries. The goal is to provide researchers and manufacturers with a comprehensive understanding, enabling them to achieve high-density metal parts that rival those produced through Selective Laser Melting (SLM). This research employs a systematic approach in producing flawless metal parts through fine-tuning parameters such as printing speed, nozzle diameter, extruder and construction table temperatures, heating rate, nitric acid injection during debinding, and sintering atmosphere and temperature. Despite advancements in MFFF parameters in this study, a comparative analysis with SLM reveals superior mechanical properties and density in SLM-produced parts. Because, the robust bonding facilitated by a powerful energy source (laser) in SLM minimizes porosities, whereas MFFF relies solely on molten filament adherence, potentially leading to small gaps between layers. This research contributes valuable insights for achieving dense, defect-free metal parts through Metal Fused Filament Fabrication (MFFF). It sheds light on the persistent advantages of Selective Laser Melting (SLM) in terms of mechanical performance and density. The comprehensive understanding of parameters provided in this study empowers researchers and manufacturers to optimize MFFF processes for 316L stainless steel, narrowing the gap between the two technologies (MFFF and SLM) and enhancing the competitiveness of MFFF in producing high-quality metal parts.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.874

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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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