A Feature Extraction Algorithm for Hybrid Manufacturing and Its Application in Robot-Based Additive and Subtractive Processes
Bibliographic record
Abstract
Establishing efficient, non-traditional manufacturing methods is critical for achieving a circular economy. Remanufacturing, the process in which a part is modified to restore original or like-new functionality, can improve manufacturing sustainability by closing the supply chain loop. A critical remanufacturing technique is hybrid manufacturing: combined additive and subtractive manufacturing processes that enable features to be added or removed from an existing part. This paper proposes a method for automating the feature extraction process for hybrid manufacturing, which is otherwise labor-intensive. The feature extraction is performed between a CAD model of the desired part and a 3D scan of the actual part to be remanufactured. The extracted features are then classified for the optimal manufacturing process and exported as STL files, which can be utilized as inputs for various tool path planning algorithms. An experiment is performed on an end-of-life case study to validate the proposed methodology. Additionally, simulations of the additive and subtractive remanufacturing processes are included to demonstrate one potential application of the proposed feature extraction process.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".