Effect of Cutting Speed on Chipping and Wear of the SiAlON Ceramic Tool in Dry Finish Turning of the Precipitation Hardenable IN100 Aerospace Superalloy
Bibliographic record
Abstract
Inconel 100 (IN100) aerospace superalloy is used in manufacturing aero-engine components that operate at intermediate temperatures. It is considered to be a hard-to-cut material. Chipping of the tool edge is one of the major failure mechanisms of ceramic tools in finish cutting of superalloys, which causes a sudden breakage of the cutting edge during machining. Cutting temperature significantly depends on cutting speed. Varying the cutting speed will affect the frictional action during the machining operations. However, proper selection of the cutting variables, especially the cutting speed, can prevent chipping occurrence. In this work, the influence of controlling the cutting speed on the chipping formation in dry finish turning of IN100 aerospace superalloy using SiAlON ceramic tool has been investigated. Scanning electron microscope (SEM)/energy dispersing spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and three-dimensional wear measurements were used to make the investigations of the worn tool edges. It was found that variations of the cutting speeds in a certain range resulted in the generation of different lubricious and protective tribo-films. The presence of these tribo-films at the cutting region proved essential to prevent chipping of the cutting tool edge and to improve its wear resistance during finish turning of age-hardened IN 100 using SiAlON ceramic tools. Chip compression ratio and calculated values of the coefficient of friction at the tool–chip interface confirmed these results.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".