A Hybrid Momentum-Based Optimization and Gaussian Process Regression Modeling Framework with MEREC-CR Weighting for Sustainable Turning Operations

Sustainable machining of composite materials requires optimizing conflicting responses influenced by limited experimental datasets, trade-offs, nonlinear process variables, and response variability. This study proposes a hybrid framework (Gaussian Process Regression—Method based on the Removal Effects of Criteria—Criteria Reliability—Momentum-Based Optimization Algorithm: GPR–MEREC-CR–MOA) to address these challenges in turning composite materials (PA66, PA66 + GF30, and PA66 + MoS2). The GPR model learns from small datasets to capture nonlinear relationships between machining variables (workpiece material, tool approach angle, tool nose radius, cutting speed, feed rate, depth of cut) and performance characteristics (surface roughness, cutting force, vibration, tool wear rate, temperature, sound pressure level, specific cutting energy, and material removal rate). The MEREC-CR method considers experimental dispersion and response variability to enhance the robustness of the multi-response aggregation model. The weighted responses determined by MEREC were optimized by exploring the operating ranges of machining variables using MOA. The GPR model accurately predicts eight performance characteristics (R2 ≥ 0.973). The GPR–MEREC-CR–MOA model identified optimal conditions for PA66 + MoS2 and composite material (tool angle = 93°, nose radius = 0.40 mm, cutting speed = 200 m/min, feed rate = 0.300 mm/rev, depth of cut = 1.08 mm), resulting in a composite performance index (CPI) of 0.9265 and a 30.2% improvement over the best experimental datasets from Taguchi L27 design. The tool wear rate, specific cutting energy, and vibration have a significant impact on overall machining performance. Feed rate has the strongest influence on CPI, as confirmed by Partial Rank Correlation Coefficients analysis. Monte Carlo-driven uncertainty analysis validates the optimal solution with a 95% confidence level for CPI between 0.8859 and 0.9451. External validation with nine independent cases confirmed the GPR model’s strong generalizability (R2 = 0.811–0.998). Benchmarking showed that MOA achieves solution quality comparable to GA, PSO, and GWO while reducing computational time by 66–86%, making it suitable for real-time optimization. The proposed hybrid framework provides an alternative data-driven decision support approach for evaluating sustainable machining parameters using limited experimental datasets of polymer composites.