Abstract:
This study explores the application of machine learning algorithms—gene expression programming (GEP), adaptive neuro-fuzzy inference
system (ANFIS),andartificial neural networks (ANN)—topredictmachiningresponsesduringthemillingofInconel690,asuperalloyknown
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 effec
tively 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 determina
tion (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 0.944572 to 0.992999, with an RMSE between 0.015527% and 0.694523% and a MAPE ranging
from 1.452397% to 4.947892%. 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.
