OPTIMIZATION OF MACHINING PERFORMANCE USING DATADRIVEN MODELING AND MULTI-SENSOR INDUSTRIAL ANALYTICS

Abstract

The optimization of machining performance has become a major requirement in modern manufacturing because industrial organizations must simultaneously improve productivity, dimensional accuracy, surface integrity, tool life, energy efficiency, and operational reliability. Conventional machining optimization approaches generally depend on fixed parameter settings, periodic inspection, isolated sensor measurements, and offline statistical analysis, which may not adequately represent the dynamic and nonlinear behavior of machining processes under changing cutting conditions. This paper proposes a data-driven modeling and multisensor industrial analytics framework for optimizing machining performance through continuous acquisition, integration, and interpretation of heterogeneous process information. The proposed methodology combines cutting-force, vibration, acousticemission, spindle-current, temperature, rotational-speed, feed, and contextual production data to construct a unified representation of machining condition. Raw sensor streams are collected through industrial Internet of Things devices and edge-enabled acquisition units, followed by synchronization, noise reduction, normalization, segmentation, feature extraction, sensor-quality assessment, and multi-source data integration. Data-driven analytical models are then employed to identify relationships among machining parameters, tool condition, process stability, surface quality, material removal behavior, and energy consumption. The framework introduces adaptive performance assessment in which cutting speed, feed rate, depth of cut, tool state, and machine condition are evaluated jointly rather than as independent variables. Multi-sensor analytics supports early identification of chatter, progressive tool wear, abnormal thermal behavior, excessive cutting loads, unstable spindle conditions, and quality deterioration. Representative experimental analysis demonstrates that the proposed integrated framework can provide higher machining-state classification accuracy, improved surface-quality prediction, earlier tooldegradation recognition, and lower false-alarm behavior than isolated single-sensor monitoring. The study concludes that combining data-driven modeling with multi-sensor industrial analytics provides a scalable foundation for intelligent machining optimization, predictive quality control, condition-aware parameter adjustment, and sustainable smart manufacturing.