Effect of chromium doping on the grain boundary character of WC-Co
Bibliographic record
Abstract
The life of cutting tool inserts is critically important for efficient machining, reducing manufacturing cost, embedded energy, and enabling more complex parts to be machined. For these applications, cemented carbide (WC-Co) materials are a prime candidate. The performance of these materials can be limited by early fracture, typically via an intergranular fracture path with respect to carbide grains. This motivates further studies to understand the character of the grain boundary network so that grain boundary engineering (GBE) of WC-Co tools can be used to improve tool life and performance. In this work, we have used Rohrer et al.'s five-parameter grain boundary character distribution (GBCD) analysis to examine the grain boundary network of WC-10wt%Co and WC-10wt%Co-1wt%Cr samples (Rohrer et al., 2004a [1]). It was found that the measured area fraction of the Σ2 boundaries was comparable to the values reported in the literature despite the relatively larger grain sizes (~14 μm) and higher cobalt contents. The result suggests that chromium doping increases the area fraction of Σ2 boundaries from 12.8 % to 14.8 %. It is proposed that this is a consequence of altering the Σ2 boundary energy, as associated with adding chromium.
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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 | 0.000 |
| 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".