Experimental Research of Optimal Die-forging Technological Schemes Based on Orthogonal Plan
Bibliographic record
Abstract
Some problems on setting up die-forging technological scheme design criteria by means of traditional methods are analyzed. The idea of mining die-forging technological schemes based on orthogonal plan is pointed out. The height and width of the hub are selected as key factors from many ones which influence the die-forging technological schemes of axisymmetric forging, then, in connection with the two factors, the relative experiments are arranged by using the two-factor-twice-composition orthogonal plan. Flash metal consumption is choosed from a lot of factors as object function whose values are measured in the experiments. When the height and width of the hub are the constant, from several experimental schemes, the technological scheme which makes flash metal consumption be the least is selected as the optimal scheme, so the design criteria of optimal die-forging technological schemes are got. Because of adopting orthogonal plan in arranging the experiments, the design and manufacturing period of forging is reduced, the developing cost of forging is cut down, and the raw and processed material consumption is decreased. In laying down the design criteria of optimal die-forging technological schemes, optimal technology is combined with artificial intelligence. In determining the relations among so many factors in die-forging technological schemes, expert’s experiences are used, and experimental results are dealt with by means of association rule mining technology, which makes the decision of die-forging schemes more reasonable and practical.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".