Influence of process parameters for tensile test specimens printed on FDM by ABS material to attain sustainability
Bibliographic record
Abstract
Additive Manufacturing (AM) is becoming the leading innovation in many fields due to its ease in generating a 3D object by adding one layer of material over the other from a source of Computer Aided Design (CAD) model as input file. Fused Deposition Modeling (FDM) is one among the technologies available in AM, which works on material extrusion process for which the material is served in filament shape. The practice of utilizing the resources effectively by meeting the requirements of subsequent generations is internationally referred to as Sustainable Manufacturing (SM). It deals with the issues that impact the economy, society and environment. Green manufacturing approaches like reduce, reuse and recycle theories are linked with 3D Printing. In this paper research has been conducted on the studies of sustainability of the parts produced on FDM for ASTM D638 Type- IV standard tensile test specimen to optimize the process parameters for Acrylonitrile Butadiene Styrene (ABS) material by using Design of Experiments (DOE) through Taguchi technique and Analysis of Variance (ANOVA). The variables considered are print speed, orientation, layer thickness and print temperature and the responses studied are energy consumption, CO2 emission, dimensional accuracy, surface roughness and mechanical properties. The primary aim of this research is to reduce the energy consumption and CO2 emission without compromising mechanical properties, in order to achieve sustainability by finding the optimum values for the input process parameters.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".