Flank Wear Progression During Machining Metal Matrix Composites
Bibliographic record
Abstract
The machining of composites present a significant challenge to the industry. The abrasive reinforcements cause rapid tool wear and increases the machining cost. The results from machining metal matrix composites (MMCs) with conventional tools show that the main mechanism of tool wear includes two-body abrasion and three-body abrasion. A more flexible method that can be considered as a cost-saving technique is therefore sought for studying the machinability characteristics of these materials. In the previous paper, a methodology for predicting the tool flank wear progression during bar turning of MMCs was presented (Kishawy, Kannan, and Balazinski, Ann. CIRP, 54/1, pp. 55–59). In the proposed model, the wear volume due to two-body and three-body abrasion mechanisms was formulated. Then, the flank wear rate was quantified by considering the tool geometry in three-dimensional (3D) turning. Our main objective in this paper is to validate the proposed model by conducting extensive bar turning experiments under a wide range of cutting conditions, tool geometries, and composite material compositions. The cutting test results showed good agreement between predicted and measured tool wear progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".