Experimental investigation of effective parameters on productivity improvement of the EDM process for corrosion resistant metals
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Inconel 625 superalloy and stainless steel 304 are known for their significant corrosion resistance along with their high hardness and strength. Therefore, they are used in a wide range of industries, including oil and gas, nuclear, etc. Electrical discharge machining is among the most widely used processes for machining of these metals. However, this process has limitations, such as low material removal efficiency, high surface roughness, and the formation of a recast layer. Therefore, in this study, the effective parameters on increasing the material removal efficiency and reducing recast layer thickness are investigated. These parameters include the dielectric fluid, electrode material, discharge current, and pulse duration. After performing the test matrix, the effect of each of the input parameters on the material removal rate, surface roughness and thickness of the recast layer is evaluated using the ANOVA method. The results of this analysis showed that the type of dielectric fluid and the presence of silver oxide nanoparticles have a significant effect on output variables. When using sunflower oil fluid containing nanoparticles and the silver electrode, the recast layer and surface roughness are reduced, while the average material removal rate increases by 40% compared to the traditional mode. Also, due to the biodegradability of deionized water and sunflower oil fluids, the environmental sustainability of the process in this study is increased and while increasing productivity, it leads to the sustainable development of the EDM process.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it