Experimental Study of Effect of Infill Density on Tensile and Compressive Behaviors of Poly Lactic Acid (PLA) Prepered by 3D Printed (FDM)
Bibliographic record
Abstract
One of the most advanced production techniques in the manufacturing sector is the Additive Manufacturing Process (AMP), commonly known as 3D printing.Among its various methods, Fused Deposition Modeling (FDM) is widely used for fabricating intricate geometries.Unlike CNC machining, injection molding, and sculpting techniques, which typically generate 70%-90% material waste, FDM is a more efficient process that significantly minimizes material loss.This study examines the impact of infill density on the tensile and compressive behavior of Poly Lactic Acid (PLA), as well as its mechanical and fracture properties.The research focuses on a hexagonal infill pattern with varying infill percentages of 25%, 50%, 75%, and 100%.In additive manufacturing (AM), prismatic closed-cell structures, commonly known as honeycomb infill, are frequently used to enhance the mechanical integrity of printed parts due to their uniform density and periodic nature.Additionally, the study evaluates the effect of print orientation at 0° , 45° , and 90° .The ASTM D638 standard was followed in designing the tensile test specimens.The findings indicate that tensile strength and elastic modulus increase with higher infill density.While variations in print orientation (0° , 45° , and 90° ) result in only slight changes in tensile strength and elastic modulus, an increase in infill density reduces elongation at break.Furthermore, increasing the infill density also leads to higher compressive strength, demonstrating a direct correlation between structural integrity and material distribution in FDM-printed PLA components.
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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) | 0.002 | 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".