Segregation Measurement of Inconel 718 Feedstocks Used in Low-Pressure Metal Injection Molding
Bibliographic record
Abstract
Low-pressure metal injection molding (LP-MIM) is an advanced manufacturing technology where a wax-based feedstock is injected into a complex shape before densification heat treatments. Feedstock is generally designed to minimize segregation, maximize flowability, maximize the strength of the molded component, maximize the solid loading potential and ease of debinding. In this study, the emphasis is placed on the evaluation of the effect of segregation on different wax-based Inconel 718 superalloy feedstocks used in LP-MIM. In powder metallurgy, particle or phase segregation generates a fluctuation of the particle distribution in powder-binder mixtures from point to point. Such demixing generally occurs before or during the injection process, and can lead to the formation of defects such as cracks, distortions or heterogeneous shrinkage of the sintered parts. Different wax-based feedstocks were poured in cylindrical hot molds (95°C), maintained in molten state for 1 minutes or for 60 minutes, and rapidly cooled to room temperature. The specimens were then extracted from the top and bottom regions of each cylindrical part. A thermogravimetric analysis technique was used to measure the volume fraction of powder at these two locations in order to quantify the degree of segregation in green parts. The best candidate feedstocks minimizing segregation are the mixtures containing only paraffin wax, or those containing paraffin wax and ethylene vinyl acetate combined. An increase in the time spent in the molten state and the use of beeswax or stearic acid promote the powder-binder separation of feedstocks.
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 | 0.000 |
| 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".