Optimising the fused filament fabrication process employing the experimental design approach: An expository paradigm under cold weather conditions and lightweight specimens
Bibliographic record
Abstract
Among all 3D printing technologies , open chamber filament material extrusion (ME) is a rapidly growing technique to many extents. Despite the benefits, various topics concerning the robustness and quality of the 3D−printed parts remain vague, especially when operating in cold weather conditions. An engineering polymer , acrylonitrile−butadiene−styrene (ABS), has been utilised due to its immense applicability in automotive industries and its low cost. However, different process parameters, their correlation, and various environmental factors affect the enactment of filament ME components. In the current research, the effect of ME 3D printing process parameters such as layer thickness, extrusion temperature , and raster angle were selected after preliminary testing and optimised for surface roughness and tensile strength for ABS under cold weather conditions for 60 % infill rate lightweight specimens by using response surface methodology (RSM). It has been observed that mean surface roughness decreases as layer thickness and raster angle decrease and extrusion temperature increases (close to 4.24 µm). Maximum tensile strength is also reported at minimum layer thickness and higher extrusion temperature. Furthermore, the tensile fractured surface morphology has revealed the close packing of layers at 0º/90º raster angle, 240 ºC extrusion temperature, and 0.1 mm layer thickness (about 31 MPa). The study outcomes can assist industries operating in cold weather conditions in their pursuit of achieving high mechanical performance and superior surface finish. Beyond optimizing print quality, the study highlights the need for developing more resilient printing methodologies that can adapt to environmental fluctuations. Furthermore, this research offers a valuable contribution to sustainability efforts, as achieving high performance with lightweight materials can reduce material waste and energy consumption.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".