Safety-Oriented Optimization of Polymer Components in FDM Using MCDM
Bibliographic record
Abstract
Fused deposition modeling (FDM) has become a widely adopted additive manufacturing method for producing functional polymer components across industrial and biomedical domains. However, ensuring both mechanical performance and safety reliability remains challenging due to the sensitivity of FDM outcomes to process parameters. This study proposes a decision-making framework integrating Fuzzy Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to optimize FDM process parameters—layer thickness, infill density, print speed, and extrusion temperature—based on mechanical and safety performance indicators. Experimental and decision analyses identified an optimal configuration of 0.2 mm layer thickness, 80% infill density, 60 mm/s print speed, and 220 °C extrusion temperature, resulting in a 17.6% improvement in tensile strength and a 14.3% increase in safety factor, calculated as the ratio of maximum tensile stress to yield stress, compared to baseline settings. The proposed framework provides a systematic pathway for balancing mechanical integrity and safety reliability in polymer additive manufacturing, offering practical value for industrial optimization and sustainable design.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".