Brittle or Ductile? Effects of Print Orientation and Raster Angle on Polylactic Acid (PLA) Fused Filament Fabrication (FFF) Tensile Samples
Bibliographic record
Abstract
The automotive industry leverages Fused Filament Fabrication (FFF) -based Additive Manufacturing (AM) to reduce lead time and costs for prototypes, rapid tooling, and low-volume customized designs. This paper examines the impact of print orientation and raster angle on the tensile properties of Polylactic Acid (PLA), selected for its ease of use and accessibility. Dog bone samples were designed to the ASTM D638 tensile testing standard and printed solid with a 0.2 mm layer height, two outer walls, and varying raster-fill angles, with layers alternating by 90°. Testing was conducted on the MTS Criterion Model 43, 50 kN system. Varying print orientation along the X and Y axes (double angle builds) produced a Young's modulus (YM) range of 0.7519, reflecting a 34.42% increase between the witnessed minimum and maximum values. These builds exhibited more brittle behavior than most single angle builds, except for X10 Y10 Z0 at a 45° raster (the lowest recorded YM) and X0 Y15 Z0 at a 30° raster (the highest recorded YM). The same build orientation (X0 Y15 Z0) with varying raster angles between 0°-90° resulted in a yield stress range between 2.53 and 3.31 kN/mm2. These findings show that strategic build parameter configuration enables PLA to exhibit both flexible and rigid characteristics. Therefore, when creating AM solutions, Design for Additive Manufacturing (DfAM) and material selection are important, but slicing parameters also play a crucial role in determining a part's final mechanical properties. These findings lay the groundwork for applying this experiment methodology to other thermoplastics commonly used in the automotive industry, such as ABS and Nylons. This research aims to provide detailed experimental data, highlighting extreme values and uncertainties in the tensile strength of FFF samples. Understanding how build orientation influences part strength can help engineers optimize performance and predict failure modes.
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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) | 0.002 | 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".