INTELLIGENT MACHINING PARAMETER OPTIMIZATION USING DATA-DRIVEN MANUFACTURING ANALYTICS

Abstract

Modern manufacturing industries increasingly require machining processes that achieve high product quality, low tool degradation, reduced production cost, efficient energy utilization, and consistent operational performance under changing process conditions. Conventional machining parameter selection commonly depends on handbook recommendations, operator experience, fixed experimental settings, or isolated optimization methods that may not adapt effectively to complex interactions among cutting speed, feed rate, depth of cut, tool condition, workpiece properties, machine dynamics, thermal behavior, and environmental variation. This paper proposes an intelligent machining parameter optimization framework using data-driven manufacturing analytics. The proposed methodology integrates multi-source machining data acquisition, sensor fusion, data quality management, contextual process modeling, feature engineering, machinelearning-based quality prediction, tool wear assessment, anomaly detection, multi-objective parameter recommendation, digital twin-assisted validation, adaptive feedback, and continuous model governance. Historical machining records and real-time operational signals are combined to identify relationships between process parameters and manufacturing outcomes such as surface roughness, dimensional quality, tool wear, material removal performance, energy consumption, and defect occurrence. The framework generates context-aware parameter recommendations according to machine condition, tool state, material category, production objective, and quality constraints rather than applying universal static settings. A representative experimental evaluation demonstrates that the proposed data-driven approach can reduce surface roughness, decrease tool wear, lower defect rate, improve material removal efficiency, reduce energy consumption per accepted component, and increase overall process stability compared with conventional parameter selection. The architecture further supports explainable recommendations, controlled deployment, and continuous learning from production outcomes. The findings establish that data-driven manufacturing analytics can provide a practical foundation for intelligent machining optimization by combining historical knowledge, real-time sensing, predictive models, digital representations, and adaptive decision support.