Effect of Binder Constituents and Solids Loading on the Rheological Behavior of Irregular Iron-Based Feedstocks
Bibliographic record
Abstract
Abstract This work presents an experimental approach used to evaluate the influence of binder constituents and solids loading on the rheological behavior and molding properties of irregular shape iron-based feedstocks used in low-pressure powder injection molding (LPIM). Thirty-six (36) different feedstocks formulated from one new in-development iron-based powder and different wax-based binder systems (paraffin wax (PW) with surfactant and thickening agents) were obtained for solids loading varying from 50 to 68 vol%. The viscosity profiles were experimentally measured at different temperatures using a rotational rheometer in order to quantify the threshold proportions of each ingredient in the binder systems, identify the best feedstock candidates, and calculate their moldability indices, which were finally validated using real-scale injections. Results confirmed that the best feedstock formulation was the one containing paraffin wax with 1 vol% stearic acid (SA) used as a surfactant, 2 vol% ethylene-vinyl acetate (EVA) used as a thickening agent, and 2 vol% carnauba wax (CW) used as a shrinking agent. An irregular shape iron-based feedstock with maximum solids loading of 58 vol% was successfully injected.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".