Performance evaluation of machine learning algorithms in predicting machining responses of superalloys
Bibliographic record
Abstract
This study explores the application of machine learning algorithms—gene expression programming (GEP), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN)—to predict machining responses during the milling of Inconel 690, a superalloy known for its exceptional mechanical properties and oxidation resistance. Machining Inconel 690 presents significant challenges due to its toughness and work-hardening tendencies, which can lead to rapid tool wear and poor surface finish. Traditional optimization methods often rely on empirical models and trial-and-error approaches, which are time-consuming and costly. In contrast, machine learning techniques can effectively model complex, nonlinear relationships between machining parameters and performance outcomes, such as surface roughness, cutting force, and cutting temperature. This study employs statistical metrics, including Root mean square error (RMSE), coefficient of determination (R2), and mean absolute percentage error (MAPE), to determine the predictive performance of the models. The results show that the GEP model achieved an R2 ranging from 0.944 572 to 0.992 999, with an RMSE between 0.015 527% and 0.694 523% and a MAPE ranging from 1.452 397% to 4.947 892%. ANFIS and ANN also demonstrated strong predictive capabilities, although GEP outperformed them. The importance of this study lies in its demonstration of advanced AI techniques as effective tools for optimizing machining processes, ultimately contributing to improved efficiency and quality in manufacturing superalloys.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".