Simulation and optimization framework for additive manufacturing processes
Bibliographic record
Abstract
Different from the traditional subtractive manufacturing, additive manufacturing - a more flexible and material saving manufacturing technology has been developed in these recent years. This paper presents a simulation and optimization framework for Additive Manufacturing (AM) processes in practical industry. Starting from multi-level part design, to process optimization and planning, from energy and material consumption to the Key Performance Indicator (KPI) evaluation, the paper presents a complete practical working flow of AM technologies. Four models are developed within the framework: the design model, the process optimization and planning model, the energy and material consumption model and the production model. All the four models connect subsequently one another. Their concepts and corresponding methods will be presented in order in each chapter of the paper. A close optimization loop can be formed by these models. The feedbacks of each model will be used to optimize the design as well as the process planning. Preliminary experiments data are generalized and analysed by each model.
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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".