Investigating the high-strain rate response of additively manufactured 420 stainless steel through material constitutive modelling
Bibliographic record
Abstract
This work explores the high-temperature, dynamic deformation behavior of AISI 420 stainless steel (420SS) produced through laser powder bed fusion (LPBF). The material exhibits a complex dual-phase microstructure, influencing its dynamic mechanical response. The investigation employs the Split-Hopkinson Pressure Bar (SHPB) technique utilizing strain rates of 1000 s −1 and 1500 s −1 , and temperatures ranging from 298 K to 798 K. The results demonstrate that the flow stresses of LPBF-fabricated 420SS are insensitive to strain rate; however, they decrease with increasing temperature. To model these behaviors, modified Johnson-Cook, Hensel-Spittel, and modified Hensel-Spittel constitutive models, supplemented by an Artificial Neural Network (ANN)-based predictive approach, were developed. Comparative analysis of the phenomenological models indicates that the formulated constitutive equations predict flow stress values within an acceptable average absolute relative error (AARE) in the range of 8–11%. In contrast, the ANN model demonstrated superior precision and accuracy (with AARE of around 1%) over the constitutive equations in predicting the hot flow behavior. To further understand the stress/strain response and the errors generated by the models, microstructural analysis of deformed samples was performed, which revealed differences between low and high temperature dynamic tests. The melt pool structure was preserved after impact for low temperature test, which was not the case for the high temperature condition. Moreover, the dynamic deformation led to the transformation of retained austenite into martensite, leading to a peak stress of 1600 MPa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".