Sustainable Machining of EN19 Steel: Efficacy of Eco-Friendly Cooling Fluids and Hybrid Optimization Techniques

The study investigates the efficacy of eco-friendly cooling fluids, specifically vegetable oil and water mixtures, in the machining of EN19 steel, with a focus on enhancing performance metrics while promoting environmental sustainability.Machining parameters, including cutting speed, feed rate, and depth of cut (DOC), were analyzed for their effects on surface roughness, tool temperature, cutting forces, and material removal rate (MRR).The study employed a hybrid optimization approach, integrating Taguchi's orthogonal array (OA) method with grey relational analysis (GRA), to evaluate the effectiveness of these eco-friendly cutting fluids.The analysis revealed that spindle speed significantly influenced the MRR, while the DOC notably affected cutting force and tool temperature.The choice of coolant was found to have a considerable impact on surface roughness.Although the Taguchi method effectively optimized individual machining parameters, GRA provided a more comprehensive evaluation by synthesizing multiple performance metrics into a single index, achieving an accuracy of 80.17%, which surpassed the 72.44% accuracy of the Taguchi method.These findings underscore the potential of GRA to optimize the machining process of EN19 steel, offering substantial improvements in manufacturing efficiency and sustainability.The study highlights the importance of adopting eco-friendly practices in industrial machining, demonstrating that the integration of GRA and Taguchi methods can lead to more sustainable and efficient manufacturing processes.