Advances in interlayer bonding in fused deposition modelling: a comprehensive review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it