Tensile Mechanical Behaviour of Multi-Polymer Sandwich Structures via Fused Deposition Modelling
Bibliographic record
Abstract
The application of single homogeneous materials produced through the fused deposition modelling (FDM) technology restricts the production of high-level multi-material components. The fabrication of a sandwich-structured specimen with different material combinations using conventional thermoplastics such as poly (lactic acid) (PLA), acrylonitrile butadiene styrene (ABS) and high impact polystyrene (HIPS) through the filament-based extrusion process can demonstrate an improvement on its properties. This paper aims to assess among these materials, the best material sandwich-structured arrangement design, to enhance the mechanical properties of a part and to compare the results with the homogeneous materials selected. The samples were subjected to tensile testing to identify the tensile strength, elongation at break and Young's modulus of each material combination. The experimental results demonstrate that applying the PLA-ABS-PLA sandwich arrangement leads to the best mechanical properties between these materials. This study enables users to consider sandwich structure designs as an alternative to manufacturing multi-material components using conventional and low-cost materials. Future work will consider the flexural tests to identify the maximum stresses and bending forces under pressure.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".