Thermal Flow Instability in Metal Injection Molding: Experiment and Simulation
Bibliographic record
Abstract
Abstract Metal injection molding (MIM) is similar to plastic molding in many respects, but MIM compounds (metal powders with polymer binders) are more susceptible to thermally induced flow instability because of their higher thermal diffusivity. The flow patterns for a 17-4PH MIM compound were observed and simulated for mold filling through a diaphragm gate over a range of filling times and melt-mold interface temperatures. Simulation predicted the observed free annular jet and internal voids in the molded part and also predicted that initial contact with the outside wall of the gate would eliminate the jet, thereby reducing voids and surface defects. Parts made using a mold with a thicker gate verified these predictions. For combinations of operating conditions and mold geometry that gave large thermally induced viscosity gradients, both observation and simulation showed unstable, asymmetric flow. In these cases, flow slowed and stopped in one region of the gate and accelerated in other regions. When the flow was inherently unstable, simulations predicted an exponential growth in maximum temperature differences at symmetric locations in the mold gate. Based on 34 experimental observations and 102 simulations, a boundary was established between regions of stable and unstable flow in terms of the dimensionless Graetz number Gz (ratio of heat conduction time to fill time) and B, a dimensionless ratio indicating the sensitivity of viscosity to temperature differences in the mold. To establish a common basis for comparison of simulation and experiment, the melt-mold interface temperature was estimated using a heat transfer coefficient, which was a fixed value for experiment and a parameter for simulation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".