Research on the relationship between turning temperature rising and turning vibration based on particle swarm optimization
Bibliographic record
Abstract
To study the correlation between turning temperature, turning vibration, and turning parameters, a prediction model for turning temperature (workpiece–tool interface temperature) was established. Through the turning test, the turning temperature near the knifepoint was collected by an infrared thermometer, and the time-domain signal of turning vibration was collected by a three-way acceleration sensor. Principal component analysis (PCA) and response surface method (RSM) were used to analyze the characteristic values of vibration acceleration and turning temperature under different turning parameters. The analysis shows that the cutting depth (depth of cut) is the key factor that affects the turning vibration and the turning temperature. Model A was established with turning parameters as independent variables and turning temperature rise as dependent variables, and model C was established with turning parameters and turning vibration as independent variables and turning temperature rise as dependent variables. Models B and D were obtained by using an adaptive particle swarm optimization (APSO) algorithm based on models A and C. According to the test results of the models, the correlation coefficient of the prediction model is D > C = B > A, indicating that the multiple regression models B and D optimized by APSO can better predict the turning temperature rise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".