Thermal Modelling and Multi Decision Making Optimization of EDM of Non Conductive SiC-CNT Ceramic Composite Used for Li-ion Battery and Sensor
Bibliographic record
Abstract
In this research work the thermal modeling of a nonconductive Silicon carbide Ceramic matrix composite (CMC) machined by Die Sinking Electric Discharge Machining (EDM) has been done. Though SiC is a non-conductive material but the presence of CNT makes it a conductive material which can be machined with EDM. The modeling procedure carried out by considering some realistic approach like Gaussian heat Flux, Specific Discharge Energy, Variable Latent heat etc. For this analysis a 2D continuum has been designed as work domain. By simulating the work domain model by a Finite Element Analysis (FEA) Software (COMSOL), material removal rate (MRR) has been estimated with variable thermal properties. Parametric analysis of effect of Variable Specific heat on MRR by considering different current, Voltage and Pulse-On time has been performed. The effect of different input parameters (peak current and Pulse-on time) on Crater geometry has been done. A new concept of Specific discharge energy has been introduced during modelling to make it a more realistic model which can also be used as electrode support for electrochemical energy devices as Polymer Electrolyte Membrane Fuel Cells on Li-ion battery. Desirability analysis has been done to get an optimize set of input parameters for I= 3A, V=30V, Ton= 75 µs for machining ceramic matrix composite by EDM. The optimized MRR at this setting is 7.25 mm3/min whereas PFE is 87%. The experimental analysis has been also performed to strengthen the thermal and mathematical modelling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".