HYBRID OPTIMIZATION OF CUTTING SPEED, FEED RATE, AND DEPTH OF CUT FOR SUSTAINABLE MACHINING

Abstract

Sustainable machining has become an important objective in modern manufacturing by balancing machining quality, production efficiency, energy consumption, tool life, and environmental impact. Conventional machining parameter optimization often focuses on a single performance objective without considering the complex trade-offs among productivity, surface quality, tool wear, and sustainability. This paper proposes a hybrid optimization framework integrating machine learning, multi-objective optimization, predictive analytics, Industrial Internet of Things (IIoT), cloud manufacturing, and intelligent process monitoring for optimizing cutting speed, feed rate, and depth of cut in sustainable machining operations. The proposed methodology simultaneously minimizes surface roughness, tool wear, energy consumption, and production cost while maximizing machining efficiency and material removal rate. Experimental evaluation demonstrates improvements in machining quality, tool utilization, energy efficiency, operational stability, production productivity, and manufacturing sustainability. The proposed framework provides an intelligent, adaptive, and Industry 4.0-ready solution for sustainable machining optimization. Keywords— Sustainable Machining, Hybrid Optimization, Cutting Speed, Feed Rate, Depth of Cut, Machine Learning, IIoT, Smart Manufacturing.