Hybrid Ensemble Learning with Uncertainty Quantification for Multi-objective EDM Parameter Optimization
V R Tejas *
Department of Computer Science and Engineering, RV Institute of Technology and Management, Bangalore, 560076, Karnataka, India.
N R Venkatesh Raju
Department of Computer Science and Engineering, RV Institute of Technology and Management, Bangalore, 560076, Karnataka, India.
Vishnu Prasad N
Department of Computer Science and Engineering, RV Institute of Technology and Management, Bangalore, 560076, Karnataka, India.
Mokshith K Y Gowda
Department of Computer Science and Engineering, RV Institute of Technology and Management, Bangalore, 560076, Karnataka, India.
Mohammed Irfanulla
Department of Computer Science and Engineering, RV Institute of Technology and Management, Bangalore, 560076, Karnataka, India.
Gajanan M Naik
Department of Mechanical Engineering, RV Institute of Technology and Management, Bangalore, 560076, Karnataka, India.
*Author to whom correspondence should be addressed.
Abstract
Electrical Discharge Machining (EDM) remains critical for precision machining of hard-to-cut materials, yet parameter optimization continues to challenge manufacturers due to complex non- linear interactions and conflicting objectives. This research presents a novel Hybrid Ensemble Learning (HEL) framework integrating adaptive feature selection, dynamic weight optimization, and uncertainty quantification for multi-objective EDM optimization. The framework was validated on a comprehensive dataset of 150 experimental runs with five input parameters (pulse-on time: 10-200 µs, pulse-off time: 10-100 µs, peak current: 1-30 A, gap voltage: 20-120 V, electrode diameter: 0.5-10 mm) and four response variables. The HEL framework demonstrated exceptional predictive accuracy with R² values of 0.961, 0.966, 0.890, and 0.753 for Material Removal Rate (MRR), Tool Wear Rate (TWR), Surface Roughness (Ra), and Overcut (OC) respectively. Multi- objective optimization using modified NSGA-II identified optimal parameters achieving MRR of 51.56 mm³/min, TWR of 10.66 mm³/min, Ra of 5.84 µm, with estimated OC below 18 µm, representing 23.5% improvement in productivity and 18.2% reduction in tool wear compared to conventional approaches.
Keywords: Electrical discharge machining, hybrid ensemble learning, multi-objective optimization, uncertainty quantification, adaptive feature selection, NSGA-II Algorithm