A review on voids of 3D printed parts by fused filament fabrication
Bibliographic record
Abstract
Fused filament fabrication (FFF), also known as fused deposition modeling (FDM™), is considered one of the most promising additive manufacturing (AM) methods for its versatility, reliability and affordability. First adopted by industries for professional uses such as rapid prototyping, then by the general public in recent years, FFF has gathered itself considerable attention. Nevertheless, despite key advancements in printer technologies and filament materials, the fabrication of robust, performing and functional parts for high-demanding practical applications remains a significant challenge. Due to intrinsic deficiencies, such as the presence of voids and weak layer-to-layer adhesion, FFF-printed parts are plagued by weak and anisotropic mechanical properties in contrast to their conventionally manufactured counterparts. With the increasing demand for designable porous structures in the fields of biomedicine, 4D printing and lightweight cellular composites, understanding the challenges presented by void presence has become more relevant than ever. As existing literature has reviewed the significance of interlayer bonding, this review focuses on documenting recent insights on the formation of voids by its categorization, research method and mechanism. The primary objective is to provide a comprehensive understanding of the two current primary methods of void research—quantitative analysis and imaging. Detailed discussions on the effects of feedstock and printing parameters on void formation are also presented. Lastly, this review discusses gaps in the current research and outlines unaddressed challenges regarding void formation and its relation with the mechanical performance of FFF 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".