Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations

In the era of Industry 4.0 and the transition toward Industry 5.0, advanced manufacturing is increasingly driven by data analytics, artificial intelligence, and cyber-physical systems. The integration of intelligent monitoring systems and self-learning algorithms is reshaping machining processes, enabling higher efficiency, precision, and sustainability. Recent advancements in smart factories emphasize the use of AI-powered process control, enabling machines to self-optimize, self-correct, and even self-retrain to maintain optimal performance. This paper proposes a hybrid control and monitoring framework designed to enhance turning operations by integrating artificial neural networks (ANNs) for predictive modeling and adaptive recalibration. The system leverages machine learning (ML) to improve machining efficiency, tool longevity, and energy consumption optimization. By implementing forward and inverse ANN models, the framework enables real-time estimation of cutting forces and energy consumption, facilitating data-driven decision-making in machining processes. Furthermore, an adaptive recalibration mechanism ensures continuous model updates, allowing the system to dynamically adjust based on evolving machining conditions such as tool wear, material properties, and environmental variations. This research contributes to these advancements by proposing an ANN-based hybrid approach, predictive modeling, and adaptive recalibration. The proposed framework ensures continuous monitoring, automated adjustments, and intelligent decision-making, making it a scalable and adaptable solution for modern machining operations.