Optimising the fused filament fabrication process employing the experimental design approach: An expository paradigm under cold weather conditions and lightweight specimens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it