Investigating the Effect of Energy Density on Corrosion Susceptibility of Additively Manufactured Thin-walled Cobalt Chrome Alloy
Bibliographic record
Abstract
Metal additive manufacturing is an innovative technology based on fabricating near-net shaped metallic components from a digital model in a layer-by-layer manner. Although, it has many advantages over conventional subtractive methods, understanding the correlation between process parameters and properties of additively manufactured alloys is key to producing components with optimal performance. Hence, in this study corrosion susceptibility of laser powder bed fusion additively manufactured cobalt chromium alloy (CoCr) alloy produced with three different energy densities (0.58 J/m, 0.87 J/m, 2.26 J/m) was investigated. Some of the CoCr alloys produced were subjected to post-build heat treatment by hot isostatic pressing (HIP). Corrosion resistance of both as-built and HIP CoCr alloys in 0.5 M H2SO4solution at room temperature was investigated using gravimetric method. The results indicated that additively manufactured (AM) CoCr alloy produced with energy density of 0.58 J/m and 2.26 J/m respectively were less susceptible to corrosion compared to that produced with energy density of 0.87 J/m which is the standard energy density recommended by the Original Equipment Manufacturer (OEM). This result was consistent with the trend observed in previously reported mechanical properties of the AM CoCr alloys.
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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 | 0.001 |
| 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 | 0.000 |
| 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".