Feature extraction and process planning of integrated hybrid additive-subtractive system for remanufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Discussion regarding hybrid manufacturing has dominated research in recent years. By synergistically integrating additive and subtractive manufacturing within a single workstation, the relative benefits of each manufacturing strategy are leveraged. The ability to add, remove feature flexibly enables remanufacturing end-of-life components into a "new" part with new features and functionalities. However, in the remanufacturing context, the process planning for hybrid additive-subtractive manufacturing is still an unsolved research topic. In general, a hybrid remanufacturing process is signified by an alternating sequence of additive and subtractive operations that alternatively add and remove materials on a used part, which results in a non-unique process planning. For determining an optimal sequence for hybrid remanufacturing, a quantitative evolution mechanism is demanded. Moreover, the constraints in process planning are required to be considered. For example, the collision avoidance between the workpiece and the material-dispensing nozzle is one of the most critical limitations that affect the alternating sequence. To fill the gap, automated feature extraction and cost-driven process planning method for hybrid remanufacturing are proposed in this paper. The feature extraction, developed under the level set framework, can extract optimal and collision-free additive-subtractive features. Then, the hybrid process planning task is formulated into an integer programming model with cost estimations. A case study is conducted, and the results confirm the correctness and effectiveness of the proposed method.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it