Multiscale characterization of Ti-induced grain refinement in additively manufactured austenitic stainless steel
Bibliographic record
Abstract
• Mn-aided Ti inoculation refines grains and mitigates columnar growth in AM 316L. • Ti addition promotes formation of TiO, FeTi, and C14 Laves phases, affecting microstructure. • Annealing redistributes phases, enhancing ductility and homogenizing hardness. • TiMn co-inoculation improves strength but reduces corrosion resistance in 316L. In-situ inoculation of grain-refining elements suppresses columnar grain growth and reduces mechanical anisotropy in additively manufactured metals, while enhancing strength via the Hall-Petch effect. However, the refinement mechanism of Ti in austenitic stainless steel remains unclear. This study investigates Mn-assisted Ti inoculation in 316L stainless steel (SS316L), followed by annealing. Despite near-full densification, localized Ti enrichment formed coarse, brittle FeTi and C14 Laves intermetallic clusters, surrounded by ultrafine ferritic grains within an austenitic matrix. Elevated annealing temperatures dissolved Laves phases and promoted Ti diffusion, resulting in dispersed TiO particles and ferritic domains. Refined Laves phases were redistributed to grain boundaries and triple junctions. Mechanical testing showed improved ductility with increasing annealing temperature: ultimate tensile strength decreased from 650 MPa to 610 MPa, while elongation rose from 13 % to 38 %. Hardness mapping revealed more uniform distribution, though overall hardness dropped from 370 HV to 210 HV. Electrochemical corrosion tests in saline solution indicated that phase transformations induced by Ti-Mn co-inoculation compromised corrosion resistance, increasing susceptibility to degradation in aggressive environments.
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".