Sustainable Parametric Optimization in CNC Machining: A Statistical, Theoretical and Pedagogical Framework

The modern manufacturing sector operates under intense pressure to balance economic viability with environmental responsibility. This monograph provides a comprehensive framework to solve the persistent conflict between maximizing productivity and maintaining superior surface integrity in Computer Numerical Control (CNC) turning operations. Written for process engineers, researchers and students, the book moves beyond traditional single-variable studies. It presents a rigorous comparative analysis of two metallurgically opposed materials: the highly abrasive EN-31 alloy steel and the ductile, low-carbon Mild Steel. Readers will find a clear, step-by-step application of robust parameter design using the Taguchi method, Analysis of Variance (ANOVA), and Grey Relational Analysis (GRA) to calculate the exact machining parameters that satisfy conflicting operational goals. The text bridges the gap between conventional statistics and modern predictive manufacturing. It details the setup and training of Artificial Neural Networks (ANN) to predict material removal rates with exceptional accuracy. It also includes Finite Element Modeling (FEM) to simulate the thermo-mechanical stresses and heat gradients at the tool-workpiece interface before a physical cut is ever made. Finally, the book addresses the future of engineering education. It outlines a Project-Based Learning curriculum designed to help undergraduate students master complex multi-objective optimization through hands-on industrial application. This volume serves as a practical, data-driven guide for anyone committed to the advancement of smart, energy-efficient manufacturing systems.