Holistic optimization of milling and EDM operations for enhanced sustainability: integrating energy efficiency, tool wear prediction, and condition monitoring

This thesis presents a comprehensive framework for sustainable subtractive manufacturing, with a focus on conventional milling and electrical discharge machining (EDM). It investigates how energy profiling, machining parameter optimization, and predictive modelling can be used to improve productivity while reducing environmental impact. Through experimental analysis and machine learning techniques, the study identifies major sources of energy loss, proposes strategies for more efficient machine operation, and demonstrates that intelligent process control can enhance both sustainability and overall manufacturing performance.