Bibliographic record
Abstract
Among the cutting tools that are utilized in industry broaching tools are the most expensive ones. Unlike other machining operations such as milling and turning in which a cutting tool can be used for producing a variety of shapes, the broaching tools are uniquely designed depending on the desired profile to be produced on the workpiece. Consequently, the shape of broaching tools may be altered from one case to the others. This shape can be a simple keyway or a complicated fir tree on a turbine disk. Hence, a proper design of the broaching tools has the highest priority in broaching operation. Every single feature of these expensive tools must be accurately designed to increase productivity, promote part quality and reduce manufacturing cost. A geometric model of the cutting tool and a predictive force model to estimate the cutting forces are two fundamental requirements in simulation of any machining operation. This paper presents a geometric model for the broaching tools and a predictive force model for broaching operations. The broaching tooth is modeled as a cantilevered beam and the cutting forces are predicted based on the energy spent in the cutting system. A design procedure has been also developed for identification of the optimized tool geometry aiming to achieve maximum metal removal rate (MRR) by considering several physical and geometrical constraints.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".