Determining the Parameters of Gurson–Tvergaard–Needleman Model for Predicting the Failure of Wrought and Fused Filament Fabricated 17-4 PH Stainless Steel
Bibliographic record
Abstract
Abstract Metal additive manufacturing is an emerging technology for creating metallic parts, with metal fused filament fabrication (FFF) rapidly gaining popularity due to its cost-effectiveness. Despite the acceptable mechanical properties of additively manufactured metals using FFF, a significant technical challenge is the presence of undesirable porosity, which affects material performance. This study aims to model the material behavior of FFF 17-4 PH stainless steel, considering its porosity, using the Gurson–Tvergaard–Needleman (GTN) damage model. The GTN model, which incorporates the micromechanical behavior of ductile metals, shows great potential for failure prediction. The GTN model parameters were identified for both wrought and FFF 17-4 PH stainless steel through a series of proposed methods. Initial void volume fractions were determined using density measurements. The evolution of void volume fractions was experimentally assessed through interrupted uniaxial tensile tests, leading to the analytical derivation of three void nucleation parameters based on continuum damage mechanics. Additional GTN model parameters related to material failure were determined through microscopic analysis of rupture surfaces and finite element (FE) trial-and-error methods. FE simulations using the GTN damage model, represented as porous metal plasticity in abaqus, were conducted to verify the identified parameters. The results demonstrated that the numerical calculations of the FE model are in good agreement with the experimental data. The use of experimentally derived GTN model parameters from the proposed methods effectively predicts material behavior, particularly in the post-necking region where traditional FE modeling fails to simulate the realistic material response.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".