An approach for mechanical property optimization of fused deposition modeling with polylactic acid via design of experiments
Bibliographic record
Abstract
Purpose This paper aims to present the influences of several production variables on the mechanical properties of specimens manufactured using fused deposition modeling (FDM) with polylactic acid (PLA) as a media and relate the practical and experimental implications of these as related to stiffness, strength, ductility and generalized loading. Design/methodology/approach A two-factor-level Taguchi test matrix was defined to allow streamlined mechanical testing of several different fabrication settings using a reduced array of experiments. Specimens were manufactured and tested according to ASTM E8/D638 and E399/D5045 standards for tensile and fracture testing. After initial analysis of mechanical properties derived from mechanical tests, analysis of variance was used to infer optimized production variables for general use and for application/load-specific instances. Findings Production variables are determined to yield optimized mechanical properties under tensile and fracture-type loading as related to orientation of loading and fabrication. Practical implications The relation of production variables and their interactions and the manner in which they influence mechanical properties provide insight to the feasibility of using FDM for rapid manufacturing of components for experimental, commercial or consumer-level use. Originality/value This paper is the first report of research on the characterization of the mechanical properties of PLA coupons manufactured using FDM by the Taguchi method. The investigation is relevant both in commercial and consumer-level aspects, given both the currently increasing utilization of 3D printers for component production and the viability of PLA as a renewable, biocompatible material for use in structural applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".