An Accuracy Investigation of Product Cost Estimation in Automotive Die Manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automotive die manufacturers face the constant challenge of producing qualitative products while having to reduce costs. However, cost reduction measures are rather insignificant during the actual manufacturing process as the most important cost-impacting decisions are taken during the design phase. Cost estimation methods attempt to determine the production cost already in the design phase; the cost can be broken down and therefore the plan times of manufacturing processes can be pre-calculated, thus enabling early capacity decisions. Many researchers have been focusing for decades on developing efficient cost estimation methods. Yet their scarce access to cost information meant that most of the developed methods could not be evaluated with real data and thus their implementation in practice being challenged. This paper reviews and classifies cost estimation methods and investigates the accuracy of the estimate based onpractical application in 190 cases. The overall aim is to determine the accuracy level of the studied methods in practice and therefore identify their application fields.
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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 | 0.000 | 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 | 0.002 |
| 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 it