Manufacturability of Overhang Structures Fabricated by Binder Jetting Process
Bibliographic record
Abstract
Due to the superior mechanical properties of metals and the inherent capability of additive manufacturing (AM) to fabricate complex structures, metal AM reveals a promising future in the industrial fields. Compared with other metal AM processes, Binder Jetting (BJ) process has potential of producing overhang structures without additional supports. This advantage of BJ process significantly enlarges the design freedom of complex metal parts with intricate overhang structures. However, it should be noted that there is still a certain manufacturing limitation of overhang structures for BJ process. Without the support of loose powder after the depowdering process, the green part is vulnerable to the inevitable external loads, such as self-weight. In this paper, a theoretical model has been proposed to evaluate the self-support capability of printed green parts after the depowdering process. A set of experiments has been designed to find the maximum normal stress that printed green parts can withstand. This proposed theoretical model can be used to predict manufacturability of overhang structure of any arbitrary shape. Based on this model, some design guidelines and future work are summarized at the end of this paper.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".