Integration of nanoindentation and finite element method for interpretable tensile properties: A cross-scale calculation method of uneven joints
Bibliographic record
Abstract
Nanoindentation testing and its Reverse Analysis Method (RAM) show great potential in understanding the tensile properties of metallic alloys with various microstructures. Nevertheless, the tensile properties of heterogeneous materials such as nickel-based superalloy welded joints have not been well interpreted by combining the microstructures and nanoindentation results, due to their diverse and complex microscopic zones, which throws shade on the properties of separated zones in the material. Here we demonstrated a new method of implanting nanoindentation results into Finite Element Method (FEM) and applied the method to the welded joints with the zones of various microstructure features. The local properties are calculated by the nanoindentation data using RAM, and used as input of Finite Element (FE) simulation of an identical indentation process, to in turn verify the accuracy and reliability of the reverse model. The simulation results reveal that the global mechanical behaviors, such as Young's modulus, yield strength and strain hardening exponent, are related to the local properties to a great extent. Thus, the global properties can be verified by simulation straight after experiments, taking consideration of local properties and dimension parameters of different zones. It is shown that the maximum error between calculation of RAM and testing is within 5.1% in different zones, and the errors of maximum indentation depth and residual depth obtained by FE simulation are less than 2.4%, which indicates that the method provides a reliable prediction of mechanical properties of superalloy welded joints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".