Additive manufacturing: a tool for engineering microstructures and mechanical behavior
Bibliographic record
Abstract
Laser Powder Bed Fusion (L-PBF) is a versatile metal additive manufacturing technique that not only enables the creation of intricate geometries but also allows for precise control over microstructural properties. By adjusting processing parameters, L-PBF facilitates the engineering of crystallographic textures and grain orientations, enhancing the mechanical performance of metallic parts. Austenitic steels exhibit Transformation Induced Plasticity (TRIP) and Twinning Induced Plasticity (TWIP) effects under deformation, which are influenced by crystallographic texture. Therefore, this contribution will provide an overview of a project on the utilization of L-PBF to produce austenitic stainless steels with tailored crystallographic textures, optimized for specific load states to improve their mechanical behavior. The study requires identifying optimal microstructures and L-PBF processing routes using machine learning methods, that enhance these effects under complex stress states. The findings are leveraged to produce site-specific, texture-optimized components with superior mechanical responses compared to conventionally manufactured parts. Co-funded by the European Union (Horizon Europe, FLEETfor55, GA no. 101192661). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union (EU) or the European Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the granting authority can be held responsible for them.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".