Study on furnace temperature curve model based on least square method
Bibliographic record
Abstract
Data fitting based on the least square method was adopted. Through the analysis of relevant data, the data were divided into five sections: small temperature region 1-5, small temperature region 6, small temperature region 7, small temperature region 8-9, and small temperature region 10-11. Then the least square method can be used to fit the five formulas, and then find out their corresponding derivative function, because the heat conduction effect of the back weld furnace on the circuit board is basically fixed, so it is assumed that the heat conduction effect of different temperatures and transmission speeds is consistent. Finally, according to the derivative function, the furnace temperature curves of the five sections can be calculated. Thus, the temperature of the midpoint 3, midpoint 6, midpoint 7 and edge 8 of the small temperature zone are 109.3℃, 155.6℃, 166.8℃ and 190.4℃, respectively. Nonlinear programming is used to solve the problem. First, constraint conditions are established according to the process boundary, and the objective function can be obtained by combining the obtained temperature acceleration. The problem is transformed into a nonlinear programming problem, and the final solution can be obtained: 182°C (small temperature range 1~5), 203°C (small temperature range 6), 237°C (small temperature range 7), 254°C (small temperature range 8~9), and the maximum speed of the conveyor belt passing through the furnace is 83cm/min.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".