Multi‐objective Bayesian modeling and optimization of 3D printing process via experimental data‐driven method
Bibliographic record
Abstract
Abstract The instability of product quality and low printing efficiency are the main obstacles to the widespread application of 3D printing in the manufacturing industry. Optimizing printing parameters can substantially improve product quality and printing efficiency. However, existing methods for optimizing process parameters primarily rely on computationally expensive numerical simulations or costly physical experiments, which cannot balance model accuracy and experiment cost. To the best of our knowledge, almost no relevant papers have been found to address the issues of product quality and printing efficiency in 3D printing from experimental data‐driven perspective. In this paper, we propose a method that integrates multiobjective Bayesian optimization (MOBO) with experimental data‐driven, aiming at obtaining more accurate optimization results at a lower cost. Distinguishing from previous studies, the proposed method utilizes experimental data instead of predicted values to update the model and find the optimal process parameters based on expected hypervolume improvement. The results of the 3D printing case study show that the proposed method can better model and optimize the highly fluctuating 3D printing process and obtain the optimal process parameters at a much lower cost. In addition, confirmatory experiments verify that the proposed method achieves higher printing efficiency while maintaining product quality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".