Optimizing the parameters for force, temperature, and metal removal rate: a multi-feature fusion model for titanium alloy milling
Bibliographic record
Abstract
In the processing of titanium alloy, the milling parameters determine the process temperature and force. Increasing the milling temperature and force can affect the quality of the titanium alloy produced. In this study, we developed a multi-feature fusion model for high-quality titanium alloy workpieces. In the milling experiments with different milling parameters, an infrared thermal imager and a three-dimensional dynamometer were used to collect the time-domain signals for temperature near the tip of the milling cutter and the milling force. Based on the experimental data, a multi-feature fusion model was established with the milling temperature, milling force, and metal removal rate as the targeted variables, and the milling parameters as the optimized parameters. Based on the particle swarm optimization algorithm, the optimal milling parameters within the test parameters were resolved using the multi-feature fusion model. The results show that: within the milling parameter range of the experimental design, the optimal solutions for the milling parameters are: milling speed of 22.14 m/min; feed speed of 8.25 mm/min; milling depth of 1.36 mm. The multi-feature fusion model resulted in lower milling temperature and force, and provides theoretical guidance for scientifically designing the parameters for the milling process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".