Personalized ceramic artwork design innovation and process parameter optimization based on 3D printing technology
Bibliographic record
Abstract
Ceramics have many applications, covering scienti ic research, medical, industrial, jewelry, etc. Ceramic materials are stable and have a silk-like touch.Ceramic 3D printing technology is based on laser curing molding as a rapid manufacturing technology.This paper proposes a personalized design strategy for ceramic artwork, determines the degree of in luence of ceramic process parameters on the quality of laser 3D printed ceramic artwork by calculating the Pearson's correlation coef icient, and adopts numerical simulation to obtain the ceramic 3D printing quality data, calculates the error of the number of printed layers, and controls the quality of the printed ceramic artwork.The ceramic quality parameter optimization model is established.Five algorithms of SVR support vector regression, BP neural network, RF random forest, RBF radial basis function, and Kriging model are used to set up the relevant parameters of 3D printing, input the six ceramic process parameters that have been processed by the uni ied magnitude, and complete the optimization of the quality ceramic process parameters of laser 3D printing.Through the investigation and analysis of the effect of ceramic artwork design, the ceramic color designed in this paper makes the user generate positive emotions; a total of 235 positive emotions were generated, accounting for nearly 60%.The mean value of user preference for ceramic samples is analyzed.The samples with the highest user preference are sample 4, sample 6, and sample 1, and the mean values of preference are 3. 425, 3.245, and 3.148, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".