Information Reuse to Accelerate Customized Product Slicing for Additive Manufacturing
Bibliographic record
Abstract
Different from most traditional manufacturing processes, the productivity of additive manufacturing (AM) is independent of the geometric complexity from the object to be built. This characteristic opens up tremendous potentials to realize mass customization. However, the AM-specific parts, such as customized products, need to be represented by millions of triangle meshes. Moreover, a large number of sliced layers are needed with the increased resolution of AM machines. These together pose a fundamental challenge in slice generation. The slicing procedure for a single customized model can take tens of minutes or even hours to complete, and the time consumption becomes more prominent in the context of mass customization. We propose a new slicing paradigm which capitalizes upon the similarities among customized models, and it reuses information obtained from the template model slicing. The idea of information reuse is implemented at several different levels depending on variations between the customized model and the template model. Experimental results show that the proposed slicing paradigm can significantly reduce the time consumption on slicing process, and ultimately fulfill mass customization enabled by AM.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".