Numerical Optimization of Drucker-Prager-Cap Model Parameters in Powder Compaction Employing Particle Swarm Algorithms
Bibliographic record
Abstract
A growing number of scholars are drawn to using numerical approaches powered by computer simulations as a potential solution to industrial problems. Replicating the compaction process in powder metallurgy with accuracy is one such issue. The Drucker-Prager-Cap model requires parameter calibration as the most used method for simulating powder compaction. This paper addresses this issue and presents a new technique for doing so. Utilizing Abaqus software 2020, the compaction process was simulated for the benchmark powder, which is the alloy Ag57.6-Cu22.4-Sn10-In10. The difference between simulation results and experimental data was reduced by applying the Particle Swarm Optimization technique in Python. The suggested approach may accurately forecast the Drucker-Prager-Cap model parameters, as demonstrated by comparing the optimized parameters utilizing the research’s method with their experimental values. The findings revealed how well the suggested approach in this study calibrated the DPC model, yielding three parameters—Young’s modulus, material cohesion, and hydrostatic pressure yield stress—with respective RMSEs of 1.95, 0.12, and 324.64 concerning their experimental values.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".