HYBRID OPTIMIZATION FRAMEWORK FOR IMPROVING MACHINING QUALITY AND EXTENDING CUTTING TOOL LIFE

Abstract

Modern intelligent manufacturing systems require machining strategies capable of simultaneously improving product quality, extending cutting tool life, maintaining production efficiency, and adapting to dynamic process conditions. Conventional machining optimization approaches frequently focus on individual objectives such as minimizing surface roughness, reducing tool wear, increasing material removal rate, or lowering energy consumption. Such isolated optimization can produce conflicting outcomes because aggressive machining parameters may improve productivity while accelerating cutting tool degradation, whereas conservative settings may extend tool life but reduce production efficiency. This paper proposes a hybrid optimization framework for improving machining quality and extending cutting tool life through the integration of multi-sensor process monitoring, data-driven predictive modeling, machinelearning-assisted quality estimation, digital twin synchronization, multi-objective search, adaptive parameter recommendation, and lifecycle-aware decision support. The framework collects heterogeneous information from vibration, cutting force, acoustic emission, spindle current, temperature, machining parameters, tool usage history, and quality inspection records. A preprocessing and feature engineering stage transforms raw observations into stable indicators representing process dynamics and progressive cutting edge degradation. Predictive modules estimate tool condition, surface quality, defect risk, and future deterioration tendencies, while the hybrid optimization engine combines global exploration, local refinement, data-driven prediction, and constraint-aware decision logic to identify balanced parameter settings. The methodology considers cutting speed, feed rate, depth of cut, thermal behavior, tool condition, productivity requirements, and quality constraints without relying on a single optimization objective. Digital twin-based process representation supports continuous comparison between expected and observed machining behavior, while secure API integration enables communication among machine tools, sensor gateways, optimization services, and enterprise applications. Representative results indicate that the proposed framework can reduce surface roughness, decrease progressive tool wear, extend effective tool life, improve quality consistency, and maintain acceptable production performance compared with fixed-parameter, singleobjective, and conventional optimization strategies. The study provides a scalable foundation for intelligent machining environments in which quality improvement and cutting tool life extension are treated as interconnected objectives within a continuously adaptive manufacturing system.