Bibliographic record
Abstract
The main objective of this research was to study the relationship between green density and compaction pressure in powdered metallurgy. Powder metallurgy has gained popularity and importance because of its near net shape, cost effectiveness and its ability to reduce the complexity of multileveled engineering components. However, powder metallurgy poses challenges that are yet to be fully understood. There are many works performed to address challenges such as the effect of friction, the tool kinematics, handling component prior to sintering and fracture under compaction. This work concentrates on the relationship between green density distribution and compaction pressure. In order to measure the relative density of compacted components, Electron Scanning Microscope was utilized. One can intuitively conceive that the relative density requires more than intuition. It was determined that highest relative density occurs at the center of the specimen and reduces toward the die-powder or punch-powder boundary. For completeness, the application of artificial neural network (ANN) and finite element (FE) model in estimation of green relative density was studied. The results of this research signify that ANN is an excellent technique to determine the relative density distribution of un-sintered compacted specimen. Moreover, finite element method can accurately estimate the average relative density of compacted specimen.
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 | 0.001 |
| 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".