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Record W2320941965 · doi:10.1115/imece2014-36697

Cost Optimization of FDM Additive Manufactured Parts

2014· article· en· W2320941965 on OpenAlex
Hargurdeep Singh, Farzad Rayegani, Godfrey C. Onwubolu

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

VenueVolume 2A: Advanced Manufacturing · 2014
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsSheridan College
Fundersnot available
KeywordsRaster graphicsProcess (computing)Orientation (vector space)Computer scienceManufacturing costFused deposition modelingAir gap (plumbing)Suspension (topology)Cost estimateEngineering drawingReliability engineeringMechanical engineeringEngineering3D printingMathematicsMaterials scienceArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes the experiments carried out on fused deposition modelling (FDM) machine to investigate the effects of process parameters on the cost of producing suspension arm and articulated rod. The process parameters considered include build orientation, raster width, and air gap. Using the cost estimation procedure described in this paper, the best option for part orientation, raster width, and air gap is realized which impact on the cost of manufacturing the part. Consequently, before committing to run the FDM machine, users can anticipate process parameters that will minimize manufacturing cost. However, in some cases, other objectives need be considered such as functionality since the configuration that leads to minimum cost may not necessarily result in optimum functionality.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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