A part-mix batch-sizing and machinability data system for milling operations: an optimal sustainable cost of quality approach
Bibliographic record
Abstract
With increased global competition and higher demand for sustainability in emerging markets, manufacturers are actively exploring new avenues to reduce production costs without compromising product quality. To address this challenge, a novel mixed integer nonlinear model is formulated by incorporating internal quality costs, environmental impact considerations, and the impact of buffer size to solve the micro-computer aided process planning problem. The scope covered is limited to milling operations for a part mix involving different materials being machined. Surface roughness is used to evaluate the desired quality level of finish. The internal quality failure cost model, including scrap and rework, is developed based on Taguchi's quadratic loss function. Mathematical programming is employed to validate the results of genetic algorithms (GAs). Because of the nonlinear nature of the model, GAs has been used. Considering strict quality cost measures, the model minimises internal quality-related costs while minimising the environmental impact.
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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 | 0.001 |
| 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".