Multi Objective Study on Machining Characteristics of AISI H-11 Tool Steel Prepared by Different Processing Techniques
Bibliographic record
Abstract
In the current study, hard turning of AISI H11 tool steel is done using TiAlN coated cutting tool. The workpiece is prepared by three different processing conditions (Solution treated, Heat treated, Cryogenic treated). The machining performance are studied by the input parameters; cutting speed (60, 90, 120 m/min) and feed rate (0.06, 0.12, 0.18 mm/rev) with a constant depth of cut of 0.5mm using Taguchi’s L9 design. The responses like, surface quality, wear on the tool, and forces generated for all three specimens are studied. The hardness of all three components is measured using Vicker’s micro-hardness tester. The experimental results proved that feed rate is the most influential parameter in deciding, surface roughness, cutting force and tool wear. The utility concept approach is applied and found, solution treated sample with 120 m/min of cutting speed and 0.06 mm/rev of feed rate produces optimal results in all three response criterion.
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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 | 0.000 | 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".