Process planning for 2 1/2D pocket machining: a novel framework
Bibliographic record
Abstract
The process for 2 1/2 D pocket machining generally considers the geometry of 2 1/2D pocket as a primative machining feature. But the non-unique mapping of the geometry of 2 1/2 pocket to the machining steps introduces ambiguities in the design and planning phases. Process planning consists of different tasks, such as Tool Selection, Tool Path Generation and Machining Parameter Selection, with individual research issues. Most of the prior research effort treats optimization of each task as isolated research topic. Only a few attempts had been made to generate the complete process plan by integrating the optimized tasks, that too in a sequential manner. It is well known that the cutting process geometry and kinematics link the different tasks together through the process parameters, like cutting tool diameter, cutting depths, etc. In this paper, a "Novel Integrated Process Planning Framework" is proposed to simultaneously optimize all the process planning tasks for different elemental features. To avoid any ambiguity between the parts design and the process planning phase, the geometry of 2 1/2 D pocket has been separated into Elemental Machining Surfaces (EMS): Bottom, Wall, and corner. This separation allows representing the 2 1/2 D pocket machining as a well defined problem where all the planning tasks for each EMS are considered together to generate the entire process plan. The framework incorporates a bottom-up ingration approach, where the planning tasks for each EMS are simultaneously optimized at the bottom level. Plans for different EMS are integrated at a higher level to obtain the optimal process plan for the entire 2 1/2 D pocket. Integrated process planning of other machining features as well as higher levels of planning can also ve generated by the proposed framework. The proposed framework approach avoids the use of complex algorithms and associated high computation costs by carrying out the integration of the process plans at different levels by facilitating the optimization of a manageable number of parameters with meaningful physical basis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".