The influence of fused filament fabrication printing parameters on the mechanical properties of a thermoplastic elastomer
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The fused filament fabrication (FFF) process is an additive manufacturing technique used in engineering design. The mechanical properties of parts manufactured by FFF are influenced by the printing parameters. The mechanical properties of rigid thermoplastics for FFF are well defined, while thermoplastic elastomers (TPE) are uncommonly investigated. The purpose of this paper is to investigate the influence of extruder temperature, bed temperature and printing speed on the mechanical properties of a thermoplastic elastomer. Design/methodology/approach Regression models predicting mechanical properties as a function of extruder temperature, bed temperature and printing speed were developed. Tensile specimens were tested according to ASTM D638. A 3×3 full factorial analysis, consisting of 81 experiments and 27 printing conditions was performed, and models were developed in Minitab. Tensile tests verifying the models were conducted at two selected printing conditions to assess predictive capability. Findings Each mechanical property was significantly affected by at least two of the investigated FFF parameters, where printing speed and extruder temperature terms influenced all mechanical properties ( p < 0.05). Notably, tensile modulus could be increased by 21%, from 200 to 244 MPa. Verification prints exhibited properties within 10% of the predictions. Not all properties could be maximized together, emphasizing the importance of understanding FFF parameter effects on mechanical properties when making design decisions. Originality/value This work developed a model to assess FFF parameter influence on mechanical properties of a previously unstudied thermoplastic elastomer and made property predictions within 10% accuracy.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it