Assessing the Feasibility of Micro-Plasma Technology for Additive Manufacturing
Bibliographic record
Abstract
In this research, a micro-plasma system was investigated for its capability in additive manufacturing (AM). Micro-plasma AM system has the advantage of lower cost and higher deposition rate over laser based AM systems, and generates leaner and cleaner weld deposit than other arc based AM systems. However, the micro-plasma system is complex and involves a large number of process variables. In this study, the feasibility of using a micro-plasma system for additive manufacturing was assessed based on surface features, mechanical properties and microstructure. In addition, two arc and wire feed modes were examined to understand the effects of these two variables. Each was used to produce IN 718 superalloy samples for macro- and microstructure evaluation, hardness, wear, and tensile tests along both long and transverse directions. Preliminary results showed that crack free samples, measured up to 100 mm × 40 mm, can be generated without measurable distortion. Some surface discoloration was observed, ranging from light straw to a purple tint. After heat treatment, the hardness of the samples varies from 403 to 440 HV, with the transverse surface showing slightly lower hardness values. Pin-on-disk wear test yielded consistent wear volume for three sets of the samples produced using different process parameters; however, samples produced with no modifications to the current and wire feed mode showed marginally higher wear rate. Microstructural analysis with SEM and EDS revealed presence of small pinholes, measured from submicron up to 22 μm in diameter, and no indication of any cracks or boundary layers between passes. SEM analysis revealed the presence of high contrast Nb/Mo rich carbides along with γ″-Ni3Nb in the γ matrix. Finally, tensile test was carried out to understand the anisotropic behavior; the results showed that transverse direction had lower tensile strength and ductility. Samples produced with pulsed current and wire feed mode had lower yield/tensile strength but higher ductility than that without current and wire feed mode modification.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".