Energy Consumption Modeling of 3D-Printed Carbon-Fiber-Reinforced Polymer Parts
Bibliographic record
Abstract
Three-dimensionally printed carbon-fiber-reinforced polymer (3DP-CFRP) has become an important contributor to commercialized additive manufacturing. Due to carbon fiber infills, the 3DP-CFRP parts can enjoy highly intricate geometry, enhanced part robustness, heat resistance, and mechanical properties. With the rapid growth of 3DP-CFRP parts in the aerospace, automobile, and consumer product sectors, evaluating and reducing their environmental impacts has become an urgent yet unexplored issue. To develop a quantitative measure of the environmental performance of 3DP-CFRP parts, this paper investigates the energy consumption behavior of a dual-nozzle fused deposition modeling (FDM) additive manufacturing process which includes melting and deposition of the CFRP filament. An energy consumption model for the melting stage is first defined using the heating model for non-crystalline polymers. Then, the energy consumption model for the deposition stage is established through the design of experiments approach and regression by investigating six influential parameters comprising the layer height, infill density, number of shells, travel speed of gantry, and speed of extruders 1 and 2. Finally, the energy consumption models are combined and experimentally tested with two different CFRP parts. The results show that the developed energy consumption model demonstrated over 94% accuracy in predicting the energy consumption behavior of 3DP-CFRP parts. The developed model could potentially be used to find a more sustainable CFRP design and process planning solution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".