The effect of process parameters on the mechanical properties of additively manufactured parts using a hierarchical multiscale model
Bibliographic record
Abstract
Purpose This paper aims to present a hierarchical multiscale model to evaluate the effect of fused deposition modeling (FDM) process parameters on mechanical properties. Asymptotic homogenization mathematical theory is developed into two scales (micro and macro scales) to compute the effective elastic and shear modulus of the printed parts. Four parameters, namely, raster orientation, layer height, build orientation and porosity are studied. Design/methodology/approach The representative volume elements (RVEs) are generated by mimicking the microstructure of the printed parts. The RVEs subjected to periodic boundary conditions were solved using finite element. The experimental characterization according to ASTM D638 was conducted to validate the computational modeling results. Findings The computational model reports reduction (E1, ∼>38%) and (G12, ∼>50%) when porosity increased. The elastic modulus increases (1.31%–47.68%) increasing the orthotropic behavior in parts. Quasi-solids parts (100% infill) possess 10.71% voids. A reduction of 11.5% and 16.5% in elastic modulus with layer height is reported. In total, 45–450 oriented parts were highly orthotropic, and 0–00 parts were strongest. The order of parameters affecting the mechanical properties is porosity > layer height > raster orientation > build orientation. Originality/value This study adds value to the state-of-the-art terms of construction of RVEs using slicing software, discarding the necessity of image processing and study of porosity in FDM parts, reporting that the infill density is not the only measure of porosity in these parts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".