Analysis of the interface properties of multi-material fused filament fabricated (FFF) printed polymer composite structures
Bibliographic record
Abstract
Interface characteristics in multi-material 3D-printed (MM3DP) structures are a critical factor in determining the strength of the structures and predicting failure. This study investigated the shear behavior of multi-material single-lap joints fabricated entirely in a single process using Fused Filament Fabrication without the use of adhesives or post-processing techniques such as welding. The joints, comprising various combinations of PLA, PETG, and carbon-fiber reinforced PETG (PETC) were fabricated. The lap joints were subjected to shear testing, with strain fields and failure locations analyzed using two-dimensional digital image correlation (2D DIC) from synchronized front and side camera views. Results show that certain multi-material joints, particularly PETG-PETC, achieved a shear strength of 2.701 MPa, comparable to the 2.923 MPa strength of the strongest homogenous joint (PLA-PLA), and exceeding that of other homogenous joints. The presence of short carbon-fibers in PETC enhances adhesion, likely due to mechanical interlocking taking place at the interface region. These findings demonstrate that multi-material FFF, when optimized for interface compatibility and process parameters, can yield composite structures with superior mechanical properties compared to single-material prints. The successful fabrication of lap joints in a single print process highlights the feasibility of producing robust, functional multi-material components for advanced engineering 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".