Effects of Infill Pattern on the Tensile Properties of 3D Printed Dog Bone Coupon Specimens
Bibliographic record
Abstract
Fused deposition modeling (FDM) is an additive manufacturing (AM) process in which meltable materials are fed into a print core, then melted and extruded on a printing plate.The infill pattern via AM has, no doubt, an impact on the mechanical properties of printed specimens, but the studies on this topic are limited.This study was aimed to examine the effects of infill patterns on the tensile properties of 3D printed dog-bone coupon specimens made using a desktop FDM 3D printer.Tensile specimens were designed using AutoCAD and SketchUp and made using an Ultimaker 3D printer.Five infill patterns set to 'lines' with different line direction angles (namely 0°, 30°, 45°, 60°, and 90°) and one infill pattern set to 'grid' were employed to print the specimens using polylactic acid (PLA) filament.In addition, one 'line' pattern from the above five was selected to make a group of specimens using wood-based PLA filament as the control.Tensile tests were conducted to measure the ultimate tensile strength (UTS) and modulus of elasticity (MOE) of the printed specimens.It was found that tensile properties increased with an increase in the line direction angle, with the maximum UTS and MOE appearing in the line pattern with a 90° infill line direction.It was also discovered that the woodbased PLA filament had a UTS of 11.66 MPa and an MOE of 330 MPa, which were 25.8% and 25.2% lower than those made of pure PLA filament using the 90-degree lines pattern.
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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 | 0.001 |
| 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".