Machine learning estimation of surface roughness and carbon emissions in Inconel 718 turning process using power analyzer-based signals

Nickel-based superalloys are commonly employed in the aerospace and defense industries because of their high temperature resistance and exceptional mechanical qualities. However, poor heat conductivity and a high tendency for work hardening make these materials difficult to process, leading to reduced surface quality and increased energy consumption. This study aims to predict surface roughness (Ra) and carbon emissions (CE) of Inconel 718 utilizing machine learning methods based on electrical signals obtained from a power analyzer during turning. Experiments were conducted under dry conditions at three cutting speeds, three feed rates, three depths of cut, and two cutting lengths. Forty-seven statistical features and truncation parameters extracted from current (Irms), total harmonic distortion (Ithd), active power (AP), and fundamental active power (FAP) signals were used as input to the model. Total CE has been calculated, taking into account energy consumption, cutting inserts, materials, and chip-related components. RF, SVR, XGBoost, and MLP algorithms were compared with SHAP, RFE, PFI, and PCC-based feature selection methods. The results showed that progress in Ra estimation was the most dominant parameter and that FAP and AP-based features had high representational power. With the XGBoost–RFE combination, a MAPE of 5.82% and an R2 of 0.9728 were obtained for Ra. In CE estimation, cutting length and depth of cut were the key factors; XGBoost–PCC achieved a MAPE of 0.0367% and an R2 of 0.9999. The findings reveal that power analyzer signals can be effectively used in the simultaneous estimation of quality and sustainability indicators. © 2026 Elsevier Ltd