Bibliographic record
Abstract
Purpose After experimental testing, it was recognized that a component’s strength relationship with respect to the volume material usage is inconsistent and that failures occurred in regions of voids. The purpose of this study is to present an optimal toolpath for a material extrusion process to minimize voids and discontinuities using standard parameters and settings available for any given machine. Design/methodology/approach To carry out this study, a literature review was performed to understand the influence of the build parameters. Then, an analysis of valid parameter settings to be targeted was performed for a commercial system. Fortus 400 machine build parameters are used for the case studies presented here. Optimal relationships are established based on the geometry and are to be applied on a layer-by-layer or sub-region basis and available machine build options. The component geometry is analyzed and decomposed into build regions. Matlab® is used to determine a standard (available) toolpath parameters with optimal variables (bead height, bead width, raster angle and the airgap) for each layer/build region. Findings It was found that the unwanted voids are decreased by up to 8 per cent with the new model. The final component will contain multiple bead widths and overlap conditions, but all are feasible as the available machine solutions are used to seed the model. Practical implications Unwanted voids can create failure points. Introducing an optimization solution for a maximized material fill strategy using existing build options will reduce the presence of voids and will eliminate “chimneys” or a void present in every layer of the component. This solution can be implemented using existing machine-toolpath solutions. Originality/value This study demonstrates that existing build settings and toolpath strategies can be used to improve the interior fill by performing targeted optimization strategies for the build parameters.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".