A Project-Based Learning Approach for Teaching Multi-Objective Optimization in CNC Turning: An Interdisciplinary Case Study

Abstract

Teaching the complex, non-linear trade-offs between surface roughness and material removal rate (MRR) in Computer Numerical Control (CNC) turning remains a significant pedagogical challenge in mechanical engineering education. Traditional theoretical instruction often fails to bridge the gap between statistical optimization mathematics and real-world machining environments. To address this, this paper presents an interdisciplinary Project-Based Learning (PBL) framework designed to teach multi-objective optimization through a comparative, hands-on case study. Engineering students were guided to apply the Taguchi method and Analysis of Variance (ANOVA) to optimize and compare the machining performance of EN-31 high-carbon alloy steel and low-carbon Mild Steel under identical conditions. By integrating Signal-to-Noise (S/N) ratios, Grey Relational Analysis (GRA), and Python-based 3D data visualization, the experiential project enabled learners to actively discover the distinct microstructural behaviors of opposed materials. The pedagogical outcomes demonstrate that this data-driven, statistical approach significantly enhances comprehension of dominant machining parameters, predictive regression modelling, and multi-criteria decision-making. This interdisciplinary PBL model provides engineering educators with a highly applicable, replicable methodology to transition from passive lectures to active, industry-aligned manufacturing education.