Fatigue behavior of two notched cutting tool materials – High speed steel and cemented carbide
Bibliographic record
Abstract
Abstract Fatigue testing experiments and computer-simulations have been conducted for notched specimens of two cutting-tool materials: high speed steel (M42 HSS) and cemented carbide composite (WC-10Co). The effects of varying loads and notches on the fatigue lives of M42 HSS and WC-10Co, including a comparative study of the fatigue behavior of the two cutting-tool materials, have been reported. The fatigue behavior of the two cutting-tool materials has been investigated by developing their S-N curves as well as through an examination of the fracture surfaces of the materials. A fatigue life of 107 cycles corresponding to a fatigue limit of 430 × 106 N × m−2 was determined for M42 HSS. It has been found that a reduction of stress amplitude by 60 × 106 N × m−2 results in an increase of fatigue life by around 175 percent for the notched specimens of cemented carbide. The computer simulation studies involved the use of ANSYS finite element analysis (FEA) as well as SolidWorks software packages. Both experimental and simulation results were found to be in agreement. These research findings might enable engineers to select a suitable cutting tool material with a notch for application under cyclic stressed machining.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".