On the possibility of using additive technologies in the production of crankshafts
Bibliographic record
Abstract
This study demonstrated the fundamental feasibility of producing a crankshaft using electric-arc wire 3D growth of metal parts on the WI 1500 process system. Specifically, the feasibility of manufacturing a crankshaft using Sv-08KhN2GMTA wire, which complies with GOST 2246–70, was investigated.Testing determined the chemical composition, mechanical properties, hardness, non-metallic inclusion content, grain size, micro- and macrostructure, and microhardness of the test sample.The results showed significantly higher relative elongation, relative contraction, and impact toughness compared to a hot-rolled blank, demonstrating the high strength and ductility of the material. The sample's microstructure is represented by bainite with clearly defined layers, ensuring excellent mechanical properties. The sample's macrostructure is free of defects such as porosity, pinholes, and cracks, and has a dense, uniform structure with typical characteristics of 3D printing. The sample is characterized by a high purity level of non-metallic inclusions, which also positively impacts its mechanical properties. The hardness of the samples produced by the weld deposition method exceeds that of hot-rolled blanks, making them promising for use under high loads and wear.Analysis of the obtained results convincingly demonstrates that additive manufacturing using the fused adhesion method (WAAM) ensures isotropic physical and mechanical properties. Moreover, this sample production method ensures the required quality of macro- and microstructural parameters, which is critical for ensuring the performance characteristics of the final product.
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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 | 0.001 |
| 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".