Segmentation and shear localization when turning TiMMC (Titanium Metal Matrix Composites)
Bibliographic record
Abstract
Titanium Metal Matrix Composite (TiMMC) is a new class of material, which has a high potential in industrial applications. The TiC ceramic particles in the titanium matrix improve its physical properties; however they also cause high abrasive wear of the cutting tool during machining. Machining TiMMC produces segmented chips which have been attributed to "plastic instability". However their cause and mechanism are not well studied for cutting experiments. The segmentations are characterized by Adiabatic Shear Bands (ASB). Extensive theoretical and experimental studies toward an improved understanding of the shear localization started since the early seventies. ASB are observed to occur in many materials when subjected to high strain, and high strain rates. Most studies of ASB were done for impact tests. Lately due to the advances in new imaging technology, ASB have been more thoroughly studied. Accordingly, Dynamic Recrystallization (DRX) seems to be a phenomenon preceding ABS, and it results in very small equiaxed grains. Furthermore super high strain rates have been identified in the shear zones. In ballistic experiments ASB have been identified as a precursor to fracture, which outlines the importance of this phenomenon. This paper focuses on the understanding of the segmentation phenomenon and the ASB when machining TiMMC, while observing the effects of the TiC particles on the ASB.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".