# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 2 Materials and Methods  
### The Framework for Turning Operations Using Artificial Neural Networks  
This document presents the iterative development of a hybrid control and monitoring framework for turning operations using artificial neural networks. The goal of this framework is to support real-time decision-making, optimizing machining parameters for efficiency, energy savings, and tool protection--Figure 1.  
The framework is structured around three key objectives:  
1. **Real-time force prediction**: The inverse ANN model estimates the cutting force (F) based on electrical parameters such as current (I) and power (P). This enables real-time monitoring of machining conditions, helping to prevent excessive tool wear and optimize cutting parameters dynamically.  
Figure 1: ANN Model Management System.  
2. **Energy consumption estimation**: The forward ANN model predicts energy consumption based on cutting force and machining parameters. This facilitates real-time efficiency assessment and enables the optimization of power usage in machining operations.
3. **Adaptive recalibration**: The system incorporates an adaptive recalibration mechanism that detects deviations in model accuracy due to changing machining conditions, such as tool wear, material variations, or process drift. This is achieved through continuous data acquisition, monitoring, and an intelligent trigger mechanism that activates recalibration when error metrics (e.g., MAE and R2) exceed predefined thresholds. To maintain accuracy under varying conditions, the recalibration system supports:
* Incremental learning--fine-tuning the model with new data while retaining existing knowledge.
* Transfer learning--adapting pre-trained models to new machining scenarios.
* Full retraining--rebuilding models using updated datasets for significant changes in machining conditions.  
The Model Management System includes a repository of trained models, dynamically storing and selecting models based on real-time machining conditions. The recalibration trigger mechanism can operate on either fixed thresholds or an adaptive approach that continuously assesses when recalibration is necessary, either in real time or at periodic intervals.  
The framework integrates the ability to predict cutting forces in real time using an inverse ANN model that enables dynamic adjustments to machining parameters. Simultaneously, the forward ANN model estimates energy consumption, allowing for real-time optimization of power usage, thus integrating monitoring with actionable control for enhanced efficiency. The adaptive recalibration mechanism further supports this hybrid approach by continuously monitoring prediction accuracy (via error metrics like MAE and R2) and triggering model updates (through incremental learning, transfer learning, or full retraining) to maintain performance under varying conditions such as tool wear or material changes.  
This framework aligns with Industry 4.0 and 5.0 principles, integrating continuous sensor data acquisition, intelligent data logging, and automated recalibration through machine learning-based strategies. By implementing dynamic model selection and self-learning capabilities, the system ensures optimal machining performance, reduced energy consumption, and enhanced tool life.