Effect of in situ thermal treatment on interlayer adhesion of 3D printed polyetherimide (PEI) parts produced by fused deposition modeling (FDM)
Bibliographic record
Abstract
Polyetherimide (PEI) possesses remarkable mechanical and thermal properties that make it a promising high-performance material for a wide range of applications. In addition to its simple processability , adhesive properties , and minimal smoke emission when heated, PEI is a suitable material for additive manufacturing . Because of its low cost and straightforward method, fused deposition modeling , or FDM, is a popular additive manufacturing technique for extruding thermoplastic filaments. Parts printed with the FDM approach, on the other hand, have common drawbacks such as low mechanical strength , shape inaccuracy, high porosity, void formation , interlayer adhesion problems, and anisotropic properties. The purpose of this study was to evaluate the effect of the thermal process during printing on the interlayer adhesion strength and the mechanical performances of PEI. The thermal treatment was done on a radiant heating system at 390 °C with a printing speed of 35 mm/s. In this study, parts printed with and without radiant heating system were characterized and analyzed with scanning electron microscopy (SEM), optical microscopy (OM), X-ray microtomography (µ-CT), optical profilometry (OP), atomic force microscopy (AFM), dynamic mechanical analysis (DMA), and tensile tests . The results revealed that the treated specimen exhibited better interlayer adhesion between printed layers. The interfacial voids were less visible in treated specimens. Furthermore, the treated specimen had lower porosity (5%) than the untreated one (8%). Moreover, increases of 183% in tensile strength , 22% in elastic modulus , and 190% in elongation at break were observed for treated specimens when compared to untreated ones.
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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".