Weibull analysis of the effect of deposition temperature and number of layers on strength of fused deposition modeling plastics
Bibliographic record
Abstract
Fused deposition modeling is an additive manufacturing process of building up a part layer by layer, with the strength of that part reliant on the bond strength between the fused layers. It is important to select printing parameters that result in a high inter-layer bond to maximize the overall structural integrity of the part. This research aims to explore the variation in the strength in addition to the mean strength in between the layers, which is critical to ensuring a more structurally sound part and should be kept in mind while choosing the printing parameters. This challenges the notion that the printing parameter that would yield the most structurally sound part would be the part with the highest mean bond strength. This research used special test samples to isolate the interlayer bonds for tensile strength testing. The tests were performed on samples with three interlayer bonds and the data was analyzed using Weibull statistics to determine the predicted tensile strength for up to 500 active layers. The tested parameter was nozzle extrusion temperature with a range of +/- 10 oC from the manufacturer’s recommended temperature. The printed polylactic acid (PLA) material had a higher average tensile strength at 205 oC than at 215 oC. However, the samples printed at 215 oC had less variation in bond strength (lower Weibull modulus) and once the number of interlayer bonds in the parts surpassed 106 layers, the samples can be expected to have higher overall tensile strength.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".