Predictive modeling of MRR, TWR, and SR in spark-EDM of Al-4.5Cu–SiC using ANN and GEP
Why this work is in the frame
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Bibliographic record
Abstract
In this study, Al-4.5Cu alloy was reinforced with varying weight percentages of SiC particles (2%, 4%, 6%, and 8%) to create metal matrix composites via the stir casting method. The formation of intermetallic compounds was confirmed through energy dispersive spectroscopy and x-ray diffraction analysis. This article compares the performance of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models in predicting the Metal Removal Rate (MRR), tool wear rate, and surface roughness in the die-sinking electro-discharge machining (EDM) process of the ex-situ developed Al-4.5%Cu–SiC composites. The study considers three machine parameters—pulse on time (TON), pulse off time (TOFF), and current (I)—along with the weight fraction of SiC particles as input variables for the models. Both ANN and GEP models demonstrated high predictive accuracy for the EDM performance metrics, with correlation coefficients (R) ranging from 0.973 68 to 0.980 65 for the ANN model and 0.980 11 to 0.982 59 for the GEP model. Notably, the GEP model exhibited superior predictive capability, as evidenced by its higher correlation coefficients and lower root mean square error, indicating greater effectiveness in predicting the EDM process outcomes than the ANN model.
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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 | 0.000 | 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 | 0.000 |
| 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 it