Quality improvement and evaluation for profile responses in cloud-based additive manufacturing processes
Bibliographic record
Abstract
The 3D printing cloud service platform integrates 3D printing with cloud manufacturing to enable resource sharing and transition manufacturing from mass production to personalised customisation. However, the unstable process of 3D printing, which results in high variability and low repeatability, impedes the application of cloud 3D printing platforms. How to economically and effectively monitor and control the process stability of 3D printers, especially in blockchain-based cloud 3D printing networks, has become a critical technical bottleneck. The authors propose a novel method to monitor and stabilise the fused deposition modelling (FDM) 3D printing process. This method uses profile responses to obtain sufficient quality data and reliable optimisation results from just a few specimens. First, the spatio-temporal Gaussian process model is combined with the Latin hypercube design to investigate the relationship between profile responses and process parameters. Second, under the Bayesian optimisation framework, find the optimal parameter settings that make the predicted profiles maximally conform to the specification region. Finally, evaluate the printing process under the optimal parameter settings by multivariate process capability indices. Verification tests show that the proposed method is feasible and cost-effective, promoting the application of a cloud 3D printing platform.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".