Influence of material structure on deep hole machinability of super high strength steels: application to crankshaft manufacturing
Bibliographic record
Abstract
The gain of productivity in machining is generally sought through tools and/or cutting conditions optimisation however an increase in productivity is achievable too through the work-material optimisation. The metallurgical structure as well as the chemical composition of steels widely influences their ability to be machined. Mittal Steel Europe Research and Development (R&D) develops new steel grades such as the Super High Strength Steels whose tensile stresses may reach 1000 or 1200 MPa. A cooperative research programme between Mittal Steel Europe R&D and ENSAM tends to propose a methodology able to sort the steel grades in terms of ability to be manufactured (in forging and machining). This study focuses on such an industrial application: the heavy vehicles crankshaft manufacturing. The operation investigated consists in deep hole drilling of the crankshaft lubrication holes. This paper proposes some relevant criteria to compare the different steel grades and/or structures. Some experimental results are proposed.
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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) | 0.001 | 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".