Tensile strength of partially filled FFF printed parts: experimental results
Bibliographic record
Abstract
Purpose This paper aims to discuss the effect of changes of a comprehensive list of process parameters on part scalability and tensile strength of fused filament fabrication (FFF) printed parts. A number of parameters hitherto not studied such as cross-sectional area and its interaction with number of shells and infill density are presented and studied. Design/methodology/approach From a preliminary investigation, results have shown that varying the process parameters affects the ultimate tensile strength (UTS) of a FFF printed component, with component scale and number of shells as the two most significant parameters affecting the UTS. A further investigation based on the interactions of four process parameters, specimen width, b, specimen thickness, h, number of shells, n, and infill density, i, and their effects on the UTS was performed. Taguchi’s design of experiment was used to develop an experimental plan in this investigation. Specimens were printed and tested for their tensile strength until fracture and the results analyzed. Findings Results obtained support an inverse relationship between part scalability, change in cross-sectional area and the UTS of a FFF printed part. The UTS results were calculated in line with conventional method based on the gross cross-sectional area of A = (b × h). Originality/value The paper investigates the effect of part scalability on the UTS of FFF printed parts and evaluates the conventional method of calculating material tensile strength of FFF printed parts using the gross cross-sectional area of A = (b × h). The results of this findings show that the conventional method cannot be used as FFF printed parts consists of partially filled parts and not a solid component.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".