An energy consumption and material efficiency simulation method for additive manufacturing
Bibliographic record
Abstract
Considering the potential for new product design possibilities and the reduction of environmental impacts, Additive Manufacturing (AM) technologies are considered to possess significant advantages for automotive, aerospace and medical equipment industries.However, there is very limited research about energy and material consumption aspects of AM, which prevents evaluating the sustainability of AM.This paper presents a simulation method to calculate the energy and material consumption for AM.Based on this method, an energy and material consumption model of Binder-Jetting technology is created.Binder-Jetting (BJ) is one of the commercial AM technology which can process a variety of materials including stainless steel, ceramic, polymer and glass.Decomposition is performed to analyze the BJ printing process.A power analyzing method is developed to provide the power information for BJ model.Based on the analyses, total energy and material consumption is calculated as a function of part geometry and printing variables.Finally, test validation is performed to check the validity of the BJ model and simulation method.Case studies are performed to reveal the energy and material consumption characteristics of BJ process.This process model provides a tool to optimize part geometry design and print variables choosing with respect to energy and material 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".