Adaptive Setup Planning of Prismatic Parts by Tool Accessibility Examination
Bibliographic record
Abstract
Setup planning for machining a part is to determine the number and sequence of setups (including machining features grouping in setups) and the part orientation of each setup. Tool accessibility plays a key role in this process. An adaptive setup planning approach for different types of multi-axis machine tools is proposed in this paper by investigating Tool Access Directions (TADs) of machining features, Tool Orientation Spaces (TOSs) of machine tools, and Primary Locating Directions (PLDs) of workpieces. In our approach, feasible TADs of a machining feature are predefined based on feature geometry and best practice knowledge, and denoted by unit vectors; The TOS of a machine tool is generated according to its configuration through kinematic analysis, and represented by a unit spherical surface patch; Primary locating directions and their priorities of a workpiece are determined based on the surface areas and the surface accuracy grades of non-machining surfaces. Starting from a 3-axis based machining feature grouping, setups for a 3-, 4- (or 3-axis with indexing table), or 5-axis machine can be achieved effectively by tool accessibility examination. A so-generated setup plan can provide not only the best coverage of machining features but the optimal orientation for each setup. Prismatic parts are considered in the proof-of-concept phase. Algorithms introduced here are implemented in MATLAB, and a case study is used to show the results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".