Enhancing sustainability in CNC turning of POM-C polymer using MQL with vegetable-based lubricant: machine learning and metaheuristic optimization approaches

Abstract

Sustainable machining of polymer parts, which is still less advanced than metal machining, aims not only to improve machinability but also to address environmental and economic challenges. Therefore, this study analyzes the sustainability of polyoxymethylene copolymer (POM-C) turning by incorporating minimum quantity lubrication (MQL) parameters (Q: flow rate, θ: nozzle angle, and d: nozzle distance) and conventional cutting parameters (Vc: cutting speed, f: feed, and ap: depth of cut), while replacing conventional oil with a biodegradable and environmentally friendly lubricant derived from Eraoil KT/2000. Additionally, the methodology relies on sustainability indicators such as surface roughness (Ra), total energy consumption (Etotal), total carbon emissions (CEtotal), and overall cost (Ctotal). To achieve this, several approaches are employed, including analysis of variance (ANOVA), artificial neural networks (ANN), k-fold cross-validation (k-fold CV), and two multi-objective metaheuristic optimization algorithms, namely SHAMODE (success history-based multi-objective adaptive differential evolution) and RPBILDE (real-code population-based incremental learning and differential evolution), are used to identify significant factors, establish mathematical models, and determine optimal conditions. The multi-objective optimization highlights trade-offs between the four sustainability criteria. Thus, a low feed value and a low MQL flow rate, combined with significant angle and distance, as well as moderate cutting speed and depth of cut, provide minimal surface roughness (Ra = 1008 µm), low energy consumption (Etotal = 0.0947 MJ), low carbon emissions (CEtotal = 0.0583 kgCO₂) but with a slightly higher cost (Ctotal = 1701 $). These results confirm a Pareto front where the improvement of one criterion negatively impacts another, guiding industrial decisions based on priorities