Optimal workpiece orientations for machining of sculptured surfaces
Bibliographic record
Abstract
A method for three-axis machining of sculptured surfaces with optimal workpiece orientations (set-ups) is presented. The procedure consists of two steps: accessibility analysis and clustering of points to be machined. Feasible tool orientations along which the tool can reach the cutting locations (CLs) without colliding with the workpiece are first determined using point accessibility analysis. The cutting locations are then classified into separate groups according to their accessibility domains in order to define the workpiece set-ups. Finally, the CL points are sorted out in each group to generate tool paths. The part surface is, therefore, virtually divided into a set of subareas, and each subarea is separately machined with a defined part set-up. The main objective is to minimize the number of part set-ups, to increase the number of feasible tool orientations in each set-up and to decrease tool path discontinuity. This makes it feasible and economical to utilize three-axis machines with a table with two degrees of freedom for cutting five-axis machinable sculptured surfaces. The primary application of the introduced algorithm is in machining processes, where it can efficiently determine optimal tool orientations in surface finishing. The solution is suitable for many other manufacturing applications, such as inspection, assembly, robotics, painting and welding. Two examples including a complex centrifugal pump are used for verification.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".