Experimental Investigation and Optimization of Energy Consumption in CNC Milling using IoT-Based Real-Time Monitoring and Taguchi Method

The increasing energy demand in manufacturing industries has created the need for intelligent and energy-efficient machining systems. The present study focuses on the experimental investigation and optimization of energy consumption in CNC milling using an IoT-based real-time monitoring system and Taguchi optimization method. An experimental setup consisting of current sensors, voltage sensors, power analyzer, microcontroller-based data acquisition system, and cloud-based monitoring platform was developed for continuous monitoring of machining energy consumption. The experiments were conducted on a CNC vertical milling machine under different machining conditions by varying spindle speed, feed rate, and depth of cut using an L9 orthogonal array. Real-time energy data were collected and statistically analyzed using Signal-to-Noise ratio analysis, regression analysis, and ANOVA. The experimental results revealed that machining parameters significantly influence energy consumption, with spindle speed showing the highest contribution. The optimized machining conditions resulted in considerable reduction in energy consumption and improvement in machining efficiency. The developed IoT monitoring framework successfully provided continuous real-time energy analytics and improved process visibility. The study demonstrates that the integration of IoT-based monitoring, statistical optimization, and sustainable machining strategies can effectively support intelligent and energy-efficient CNC manufacturing systems.