# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 5 Conclusions  
The scientific justification of this research lies in the necessity of enhancing precision, adaptability, and efficiency in turning operations within modern manufacturing systems.  
This framework establishes a foundation for intelligent machining process optimization, paving the way for autonomous manufacturing systems driven by AI-based decision-making. By integrating artificial neural networks (ANNs) with adaptive learning strategies, the proposed approach enhances both predictive accuracy and system adaptability in turning operations.  
The key achievements of this research include the following:  
* The development of a robust ANN-based hybrid control and monitoring framework that integrates forward and inverse modeling for machining process optimization.
* High predictive accuracy achieved through Bayesian optimization and weighted loss functions, ensuring precise estimations of energy consumption and cutting forces.
* The implementation of adaptive recalibration strategies, allowing models to dynamically adjust based on real-time performance monitoring.  
This work opens the path for future advancements in the following:  
* Real-time deployment: Implementing the trained models on edge computing systems for real-time monitoring and control.
* Online adaptive learning: Enabling continuous learning and self-tuning models to dynamically adjust parameters in response to evolving machining conditions.
* Expanded input parameters: Integrating additional machining features such as vibration, temperature, and acoustic signals to enhance predictive accuracy.  
To further enhance system robustness and long-term adaptability, a hybrid modeling approach is proposed, combining physical principles with neural networks to achieve both stability and flexibility. The key elements of this approach include the following:  
* Baseline physical/theoretical model: Leveraging well-established machining equations and empirical relationships to provide a stable foundation.
* Neural network for real-time corrections: Employing ANN-based models to dynamically adjust and compensate for deviations caused by factors like tool wear, material inconsistencies, and environmental changes.  
This \"physics-informed neural network\" (PINN) or \"knowledge-based + machine learning\" strategy may ensure higher stability, generalization, and long-term adaptability, outperforming purely data-driven models that require frequent, full retraining.  
By integrating cyber-physical systems, AI, and machine learning, this framework contributes to the evolution of smart manufacturing in Industry 4.0 and 5.0, enabling manufacturers to optimize production, extend tool life, and enhance system resilience in data-driven machining environments.  
Conceptualization, B.F.A. and V.G.; methodology, B.F.A.; software, B.F.A.; validation, B.F.A.; formal analysis, B.F.A. and V.G.; investigation, B.F.A. and V.G.; resources, B.F.A. and V.G.; data curation, B.F.A. and V.G.; writing--original draft preparation, B.F.A. and V.G.; writing--review and editing, B.F.A. and V.G.; visualization, B.F.A. and V.G.; supervision, B.F.A.; project administration, B.F.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National University of Science and Technology POLITEHNICA Bucharest.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors wish to express their gratitude to POLITEHNICA Bucharest for providing support through access to infrastructure.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.