Effect of the Microstructure on the Corrosion and Fatigue Behavior of the Additively Manufactured M789 Stainless Steel
Bibliographic record
Abstract
Corrosion is one of the most destructive on the environment and also on our safety. Not only does the microstructure influence the severity of corrosion, but also does many other chemical and mechanical factors such as fatigue. For this reason, the ability to evaluate and further predict localized corrosion on materials subjected to stress has been a growing interest in an attempt to foresee the fatigue-corrosion damage before escalating into a material failure, years later. A quantitative and qualitative investigation was conducted to assess the effect of the heterogeneous microstructure on the fatigue and corrosion resistance of the additively manufactured martensitic stainless steel, AMPO M789, employed in application where a high hardness and corrosion resistance is of need. We first examined quantitatively the inclusion content of polished surfaces and the roughness of net-shaped samples, in order to estimate the fatigue strength of the alloy with statistics of extreme values and linear fracture mechanic approach. We then exposed the material to electrochemical tests in synthesized seawater, in order to identify possible corrosion mechanisms and to further assess the pits initiation and their dimensions. Considering those results, we finally predicted a possible performance of M789 in high-cycle fatigue in presence of corrosive environment. We believe our work represents a first step towards studying the synergistic effect of localized corrosion and fatigue strength on the heterogeneity of this new additively manufactured alloy.
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 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 | 0.000 | 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) | 0.000 | 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".