# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 1 Introduction
Several studies have focused on optimizing cutting parameters to minimize energy consumption while maintaining product quality. Techniques such as response surface methodology, Taguchi methods, and genetic algorithms have been employed for this purpose [7]. Optimizing the tool path can reduce unnecessary movements and idle time, leading to lower energy consumption. Minimum quantity lubrication and dry machining have been used to reduce energy compared to traditional flood cooling [8]. The development of energy-efficient machine tools, including those with regenerative braking, variable speed drives, and energy recovery systems, has been a focus of many studies. These technologies can significantly reduce the overall energy footprint of machining operations [9]. The yield of a processing system is its main characteristic, and it is influenced by a series of factors such as frictional losses, work speed, the quality of surfaces in relative motion, and the quality of the lubrication of elements in relative motion. Energy tests for a technological system can be completed based on cutting force or braking force. Equipment that generates braking force or momentum with friction or electricity does not allow for the direct determination of the braking force in the processing area. [PERSON] and [PERSON] [10] proposed a spindle-bearing and machine tool system consisting of a rotating shaft, tool holder, angular contact ball bearings, and machine tool mounting. The model anticipates bearing stiffness, mode shapes, and frequency response function, as well as static and dynamic deflections along the cutter and spindle shaft. [PERSON] at al. [11] provide a method for calculating the ideal bearing preload across a broad range of speeds. It takes into account both thermally induced bearing preload and initial preload synthetically, and it is validated through simulations and experiments. The fatigue life determines the maximum bearing preload, the approach is suited to machine tool spindle usage, and a bearing temperature rise of more than 30 \({}^{\circ}\)C is rare within the usual speed range. In order to completely comprehend the characteristics of additional load losses, [PERSON] [12] provides a model for additional load losses based on the power flow model, under the assumption that the spindle motor's slip is minor. Additionally, a test system is created to measure the machine tool's input power, spindle speed, and cutting torque to determine the additional load losses. A first-order polynomial can suit the experimental findings obtained from the CNC lathe, which indicate that the additional load losses coefficient varies with spindle speed and cutting torque. To experimentally validate the proposed method for determining the variable preload, the stiffness of the spindle and the temperature rise of the test bearing are measured [13]. The results indicate that the variable preload spindle performs exceptionally well, exhibiting a lower temperature rise at high speeds compared to the constant pressure preload spindle and a significant increase in dynamic stiffness at low speeds. The research progress in machining processes reflects a shift towards intelligent, data-driven approaches (e.g., ANNs and PINNs) that enhance prediction accuracy and efficiency for cutting forces, surface roughness, and energy consumption. Cutting force prediction has transitioned from physics-based analytical and FEA methods to data-driven ML approaches, offering improved accuracy, efficiency, and adaptability. ML has demonstrated remarkable potential in handling complex machining scenarios and enabling real-time applications. However, challenges such as data dependency, limited real-time capability, a lack of interpretability, and poor generalizability persist. Innovations in optimization, tool design, and machine tool technology further support sustainable manufacturing.