predictive modeling

Today, with the unprecedented growth of artificial intelligence (AI) and its increasing applications across various fields, its use in industry has gained significant traction. A notable example of this application is the use of AI techniques for modeling and predicting power requirements in machining processes. The current state has heightened the need for adopting power prediction models to facilitate sustainable process planning. This paper introduces two AI modeling techniques—Artificial Neural Networks (ANN) and Support Vector Regression (SVR)—for predicting power consumption in machining processes. To evaluate the capabilities of these techniques in forecasting power values, a real machining experiment was conducted. The experiments were designed using the Taguchi method to study the impact of all parameters with the minimum number of trials possible. A Taguchi L16 4-level 3-factor design was employed to describe the experimental setup. The predicted power values from both techniques were compared and assessed, revealing that the ANN performs slightly better than SVR. To evaluate the models' performance, several representative hypothesis tests were conducted, including t-tests for mean testing, F-tests, and Levene's test for variance testing. The results indicate that the proposed models in this study are suitable for power prediction.