Advances in interlayer bonding in fused deposition modelling: a comprehensive review
Bibliographic record
Abstract
Fused deposition modeling (FDM) has established itself as a major additive manufacturing technology for the production of parts made of polymer and composite materials. A critical challenge in FDM is achieving strong interlayer bonding (IB), which worsens mechanical anisotropy and compromises the overall functionality of fabricated parts. To overcome this limitation, researchers have developed a range of advanced techniques, including pre-printing modifications (e.g. filament material modification), in-process interventions (e.g. preheating, vibration, and ultrasonic-assisted FDM), and post-processing methods (e.g. ultrasonic strengthening, annealing, microwave welding, and electromagnetic induction welding). Each of these techniques has been investigated, showing its pros and cons. This article also explores recent advancements aimed at enhancing IB, explaining their underlying mechanisms, highlighting key results, and critically evaluating their overall effectiveness. This review synthesises the state-of-the-art in IB enhancement strategies and their influence on resultant part properties. Consequently, further investigation into optimising existing methods and developing innovative approaches is essential for realising the full potential of FDM in advanced manufacturing applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".