Investigation of the Effect of Aluminium Addition on the Additively Manufactured SS309L Alloy
Bibliographic record
Abstract
Additive manufacturing (AM) is a highly advanced manufacturing technology that involves metal layers deposition in order to increase the efficiency of component production and costs reduction.Current study includes examining the effects of adding aluminium as an additional alloying element to austenitic stainless-steel SS 309L.Metal Inert Gas (MIG) welding apparatus was used as a heat source for the deposition of the steel in the Wire Arc Additive manufacturing method.Simultaneously during the building process, three different percentages (0, 2.5%, and 5%) of an external aluminium alloy 4043 (E4043) cold wire was introduced into the fusion zone.The focus of the study is to highlight the influence of adding E4043 on the microstructure and mechanical properties of additively manufactured SS309L.For the microstructural investigations, optical microscopy and scanning electron microscopy (SEM) were used, whereas a Vickers microhardness test was used to investigate the effect of these additions on the local mechanical properties.On the other hand, the mechanical behavior of the deposited parts was examined using tensile test.The results of the study demonstrated that the addition of the aluminum alloy is significantly affect the mechanical properties of the deposited portions.The quantity of the added aluminum is discovered to have an influence on the microstructure, hardness, and tensile strength.Moreover, the homogenizing thermal treatment improved the samples' microstructure and overall properties.The found results highlights the importance of considering double wire feeding in additive manufacturing processes to reach the desired microstructural and mechanical properties in the final products.
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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 | 0.001 | 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 | 0.001 |
| 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".