Enhancing CNC Machining Precision Using Al-Based Process Monitoring and Control
DOI:
https://doi.org/10.22232/stj.2025.13.01.15Keywords:
Al in CNC milling, Process optimization, Predictive maintenance, digital twin, Machining efficiency, smart manufacturing, Tool wear reduction, Sustainable machiningAbstract
This study investigates the impact of Al-based process monitoring and control on CNC milling optimization. AI integration led to a 50% increase in material removal rate (MRR), a 14.3% improvement in tool life, a 20% reduction in machining time, and a 20% decrease in energy consumption. By leveraging machine learning algorithms, real-time sensor feedback, and predictive analytics, AI dynamically adjusted spindle speed, feed rate, and cutting parameters to enhance machining efficiency. Digital twin simulations further optimized tool paths, reducing tool wear and improving precision. The results highlight that Al-driven CNC milling enhances productivity, reduces operational costs, and ensures sustainable manufacturing practices.
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