Split-Hopkinson pressure bar testing and constitutive model evaluation for 7050-T7451 aluminum, IN718 superalloy & 300M steel
Bibliographic record
Abstract
The understanding of materialâs response to high strain rate loading is essential for a range of applications such as high-speed forming, machining, crashworthiness of automotive structures, and similarly ballistics impact performance of armor and engine fan blade containment structures. For reliable numerical modelling of such processes, accurate high strain rate materials data and constitutive models describing the strain rate dependence of the materialâs stress-strain response are necessary. The Split-Hopkinson pressure bar (SHPB) has been a commonly used method for evaluating the high strain rate response of materials in the range of 102s-1 to 104s-1. Measurements from this technique is useful for producing precise data to calibrate constitutive models, and to facilitate modeling and simulation of high strain rate processes.In this study, a compressive Split-Hopkinson pressure bar (SHPB) setup was used to evaluate the stress-strain response for three alloys, Aluminum 7050-T7451, Inconel 718, and 300M steel for the modelling of shot-peening, cold-work surface modification process. Shot peening involves impacting a materialâs surface with spherical media to generate sub-surface deformed layers containing strain hardening and residual stress. During the peening process, strain rates of the peened material can reach up to 105s-1 to 106s-1, which is greater than strain rates measurable using the SHPB. To enable a higher strain rate response, SHPB tests were carried out at a low temperature by cooling to represent the response of an increase in strain rate through the equivalent effect of lower temperatures and higher strain rates on the measured stress. In addition, SHPB tests were carried out at specific strain rates and test temperatures for calibrating constitutive models.From SHPB tests, Aluminum 7050-T7451 stress-strain results showed an increase in strain rate sensitivity above 103s-1 and at 25°C. For varying temperature tests measured at 2Ã103s-1, the stress-strain at -110°C showed higher strength and initial strain hardening rate compared to the resultat 25°C. Negative strain hardening occurred for results at 100°C and 200°C and the rate of thermal softening increased at 200°C. IN718 exhibited a moderate increase in strength from 103s-1 to 4Ã103s-1 at 25°C. For varying temperature tests at 4Ã103s-1, the strength increased at -110°C relative to 25°C and the strain hardening rate was comparable in both tests. At 500°C, the measured strain hardening rate was notably lower compared to the result at 25°C. 300M steel alloys tested at 3Ã10s-1 and 25°C displayed stress saturation and slight negative strain hardening with increasing strain. At a strain rate of 2.4Ã103s-1, the strength at -70°C was greater than that at 25°C, and strain hardening trends were similar for both conditions. Stress-strain response at 200°C displayed an initial increase in strain hardening prior to softening, and stress saturation at 500°C was comparable to the result at 25°C. In addition, shear failure occurred in samples tested at varying temperatures and strain to failure was comparable in all conditions.The SHPB results attained at high strain rates, varying temperatures as well as quasi-static data at 25°C, were used to evaluate the Johnson Cook (J-C) model parameter for each alloy. A modified Johnson Cook model with Voce strain hardening law and a modified Khan-Huang-Liang (KHL) model were evaluated and provided closer fit to Aluminum 7050-T7451 and IN718 results, respectively compared to the J-C model. For 300M steel, a modified J-C model with Cowper Symonds strain rate form provided comparable correlation to experiments as the J-C model. The J-C model, and models with more adequate correlations were used to extrapolate the stress at higher strain rates to represent the response encountered during peening.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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 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".