ENERGY-AWARE MACHINING PARAMETER OPTIMIZATION FOR QUALITY AND TOOL LIFE IMPROVEMENT

Abstract

Energy-efficient machining has become a critical objective in modern manufacturing because energy consumption directly influences production cost, environmental sustainability, and operational efficiency. Conventional machining parameter optimization primarily focuses on surface quality or productivity while neglecting energy utilization and tool life. This paper proposes an energy-aware machining parameter optimization framework integrating machine learning, predictive analytics, Industrial Internet of Things (IIoT), cloud manufacturing, intelligent sensors, and adaptive process monitoring for simultaneously improving machining quality, reducing energy consumption, and extending tool life. The proposed methodology optimizes cutting speed, feed rate, and depth of cut using multi-objective decisionmaking while continuously monitoring machining performance through real-time sensor data. Experimental evaluation demonstrates improvements in surface finish, tool utilization, machining stability, energy efficiency, production productivity, and manufacturing sustainability. The proposed framework provides an intelligent, scalable, and Industry 4.0-ready solution for energy-aware machining optimization in advanced manufacturing environments. Keywords— Energy-Aware Machining, Cutting Parameter Optimization, Machine Learning, Tool Life, Surface Quality, Industrial Internet of Things, Predictive Analytics, Sustainable Manufacturing.