Experimental Investigations of the Influence of Spent Coffee Grounds Content on PLA Based Composite for 3D Printing
Bibliographic record
Abstract
Nowadays Fused Deposition Modeling, a widely utilized additive manufacturing technology, is significantly transforming as modern production processes. Beyond basic uses to it role in sustainability, Fused Deposition Modeling offers processing potential for implanting circular economy by reducing virgin materials consumption and enhance the integration of waste food for sustainable 3D printing. This research paper investigated the production of new composite materials based on spent coffee grounds. In addition, PLA and SCG at various contents (0, 3, 5, 10, and 15 wt%) were dried and premixed, then processed into PLA/SCG composite pellets using twin-screw extrusion. These pellets were successfully converted into filaments and subsequently used for 3D printing. The effect of spent coffee grounds in PLA composites was investigated via physical and mechanical analysis of 3D printed samples. Regarding density measurements, results revealed that adding up to 5 wt% of spent coffee grounds increased the density while further additions led to a decrease which due to the printing parameters such as extrusion temperature and nozzle diameter. Considering the mechanical properties, the Young’s modulus increased once the spent coffee grounds content reached 3 wt% and then decreased. In the other hand, there was no enhancement in tensile strength and elongation at break which corroborating with density measurements. This mainly contributed to the changes in mechanical properties caused by printing parameters. This study demonstrates that coffee waste can be used as a filler in environmentally friendly composites for 3D printing, with a maximum SCG content of 15 wt%. This approach not only promotes the reuse of coffee waste but also reduces the cost of traditional PLA filaments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".