# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 1 Introduction  
Turning operations play a fundamental role in machining processes within the manufacturing industry, and their integration with Industry 4.0 and 5.0 is essential for achieving digital transformation. As smart manufacturing evolves, real-time data acquisition, predictive analytics, and AI-driven process optimization are becoming critical for enhancing efficiency, reducing downtime, and improving sustainability. Implementing digital solutions in turning operations enables adaptive control, intelligent monitoring, and automated recalibration, ensuring precision and energy-efficient machining. Deep learning-based models, reinforcement learning strategies, and hybrid AI-driven control systems are being actively developed to enhance real-time decision-making and process automation. Artificial neural networks (ANNs) are highly effective machine learning algorithms that are inspired by the human brain's functionality and complex structure. ANNs consistof interconnected neurons, which allows them to transmit and process information with great efficiency [1]. The continuous evolution of ANNs in manufacturing is driven by their ability to model complex, nonlinear relationships between machining parameters, offering superior predictive accuracy compared to conventional analytical models. Current research is expanding towards physics-informed neural networks (PINNs), transfer learning, and federated learning, enabling models to leverage prior knowledge, generalize across different machining setups, and collaboratively improve across distributed systems [2]. These developments contribute to the creation of self-adaptive, highly efficient, and autonomous manufacturing systems, aligning with the future vision of sustainable and intelligent production ecosystems. The prediction of cutting forces in metal cutting processes relies on physical principles and can be categorized into two main approaches: analytical models and numerical simulations using finite element analysis. With advancements in artificial intelligence and machine learning, various algorithms have been developed to enhance the accuracy and efficiency of force prediction [3]. Also, surface roughness prediction is crucial for improving the surface integrity of machined parts to reduce energy and frictional losses; at the same time, minimizing cutting force and tool wear would contribute to a cost-effective and environmentally friendly machining environment [4]. The most important criteria on the basis of which the optimal cutting regimes are determined are the criterion of maximum productivity or minimum processing time and the criterion of minimum processing cost. When determining the cutting regimes based on these criteria, the problem of the efficiency with which the machine tool works and the consumption of energy for cutting is not often raised. The lack of knowledge of the connection between the working parameters of the machine tool, as well as the effects of the operation of the machine tools at the highest possible efficiency on the specific consumption of electrical energy and the productivity of cutting constitute an explanation for this problem. Studies have consistently shown that cutting parameters such as cutting speed, feed rate, and the depth of cut significantly influence power and energy consumption. For instance, higher cutting speeds generally increase power consumption due to greater mechanical work and heat generation [5]. The geometry of the cutting tool, including the diameter, number of flutes, and material, influences energy usage. Tools with improved shape can lower cutting forces and thus energy consumption [6]. Empirical models based on experimental data have been widely used to predict power and energy consumption in milling. These models often relate cutting parameters to energy use through regression analysis [5]. Mechanical models focus on the physics of the cutting process, considering torque, forces, and power at the tool-workpiece interface.