On the Post-Printing Heat Treatment of a Wire Arc Additively Manufactured ER70S Part
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Bibliographic record
Abstract
Wire arc additive manufacturing (WAAM) is known to induce a considerable microstructural inhomogeneity and anisotropy in mechanical properties, which can potentially be minimized by adopting appropriate post-printing heat treatment. In this paper, the effects of two heat treatment cycles, including hardening and normalizing on the microstructure and mechanical properties of a WAAM-fabricated low-carbon low-alloy steel (ER70S-6) are studied. The microstructure in the melt pools of the as-printed sample was found to contain a low volume fraction of lamellar pearlite formed along the grain boundaries of polygonal ferrite as the predominant micro-constituents. The grain coarsening in the heat affected zone (HAZ) was also detected at the periphery of each melt pool boundary, leading to a noticeable microstructural inhomogeneity in the as-fabricated sample. In order to modify the nonuniformity of the microstructure, a normalizing treatment was employed to promote a homogenous microstructure with uniform grain size throughout the melt pools and HAZs. Differently, the hardening treatment contributed to the formation of two non-equilibrium micro-constituents, i.e., acicular ferrite and bainite, primarily adjacent to the lamellar pearlite phase. The results of microhardness testing revealed that the normalizing treatment slightly decreases the microhardness of the sample; however, the formation of non-equilibrium phases during hardening process significantly increased the microhardness of the component. Tensile testing of the as-printed part in the building and deposition directions revealed an anisotropic ductility. Although normalizing treatment did not contribute to the tensile strength improvement of the component, it suppressed the observed anisotropy in ductility. On the contrary, the hardening treatment raised the tensile strength, but further intensified the anisotropic behavior of the component.
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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) | 0.005 | 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 it