Themomechanical Response of an Additively Manufactured Hybrid Alloy by Means of Powder Bed Fusion
Bibliographic record
Abstract
The laser powder bed fusion (L-PBF) technique was utilized to manufacture a hybrid M789-N709 alloy by depositing M789 steel on wrought N709 steel. The tensile strength of the M789-N709 interface generated during the process has been established to be higher than that of the base materials. In the previous work of the current authors, extensive characterization of the M789-N709 interface (before and after heat treatment) was performed by means of electron backscatter diffraction, electron probe microanalysis, transmission electron microscopy with energy dispersive spectroscopy, and atom probe tomography analyses, to understand the mechanisms associated with its superior strength. In the present work, since the application of the hybrid alloy is targeted towards an elevated temperature environment, the individual high-temperature mechanical properties of M789 and N709 steels were acquired at various temperatures and strain rates using a Gleeble 563 thermomechanical system. Then, based on the flow curves, phenomenological-, and physical-based constitutive material models were established. These constitutive models can be utilized to accurately assess the high-temperature response of the hybrid alloy system using finite element analysis programs. This work demonstrates the application of thermomechanical processing and constitutive modeling in the field of metal additive manufacturing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".