Paper Contents
Abstract
The increasing demand for sustainable and energy-efficient manufacturing has brought significant attention to the optimization of energy consumption in CNC (Computer Numerical Control) machining. This research focuses on the development and validation of a simulation-based approach for minimizing energy usage during 3-axis CNC milling operations, using MATLAB and synthetic IoT sensor data. A predictive energy-time model was constructed using synthetic datasets generated to replicate sensor-based feedback under varying machining conditions such as spindle speed, feed rate, and depth of cut.To achieve optimization, a multi-objective function was formulated, prioritizing energy reduction while maintaining acceptable machining times. A Genetic Algorithm (GA) was applied to search the parameter space, with the MATLAB simulation environment enabling visualization through contour plots, surface plots, and heatmaps. The optimal parameter set resulted in a significant reductionup to 94%in total energy consumption when compared to baseline machining conditions. These findings demonstrate the viability of intelligent algorithms, coupled with synthetic IoT data, in supporting real-time decision-making and optimization in CNC machining environments.The study also evaluates the limitations of synthetic data modeling and identifies future opportunities for incorporating real-time IoT sensor feedback, digital twin integration, and closed-loop control systems. This research contributes toward the development of intelligent, adaptive, and energy-conscious manufacturing systems aligned with Industry 4.0 and green manufacturing principles.
Copyright
Copyright © 2025 Sandip Kumar. This is an open access article distributed under the Creative Commons Attribution License.
