Adaptable Multi-Objective Optimization Framework: Application to Metal Additive Manufacturing
Bibliographic record
Abstract
<title>Abstract</title> The aim of this work is to introduce an adaptable framework for Multi-Objective Optimization (MOO) in Metal Additive Manufacturing (AM). The framework accommodates diverse design variables and objectives, enabling iterative updates via Bayesian optimization for continuous improvement. It employs space-filling design and Gaussian Process regression for high-fidelity surrogate models. A Sensitivity Analysis (SA) measures the input contributions. Multi-Objective Optimization (MOO) was performed using an evolutionary algorithm. Using literature data, the framework optimizes the surface roughness (SR) and porosity of the AM part by controlling the laser parameters. The GP model achieves cross-validation with an R² of 0.79, and with low relative mean errors. SA highlights the dominance of hatch distance in SR prediction and the balanced influence of laser speed and power on the porosity. This framework promises significant potential for the enhancement of AM technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".