Capability Enhancing of CO2 Laser Cutting for PMMA Sheet Using Statistical Modeling and Optimization
Bibliographic record
Abstract
Laser cutting is a widely used manufacturing process, and the quality of the resulting cuts plays a crucial role in its success. This research employed the Design of Experiments (DOE) to investigate the impact of input process parameters on kerf quality during the laser cutting of 5 mm polymethyl methacrylate (PMMA) sheets. Response surface methodology (RSM) was utilized to model the relationship between the input parameters and the kerf quality, with regression equations developed for each response using the Design Expert software. A statistical analysis revealed the significant effects of high laser power, cutting speed, and focal plane position on kerf quality. Optimization, guided by the desirability function, identified optimal parameter combinations that offered the most favorable tradeoff among various responses. Optimal conditions were found to involve a high laser power, a cutting speed ranging from 4 to 7 mm/s, and a focal plane position at the center. Experiments indicated the suitability of the models for practical applications. An overlay plot analysis revealed a weak negative correlation between the laser power and the cutting speed, while the focal plane’s position could be adjusted independently.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".