# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 4 Discussion
### Inference Models and Results
Consequently, when monitored values fall outside acceptable thresholds (e.g., MAE exceeding a specified limit or R\({}^{2}\) dropping below a set standard), the system flags the necessity for recalibration or re-learning. This integrated monitoring approach establishes the initial framework necessary for implementing more sophisticated adaptive learning strategies, such as incremental retraining or transfer learning, thus ensuring the models remain accurate and reliable under evolving machining conditions. Moving forward, the model could be further improved by exploring alternative architectures, hyperparameter tuning, or incorporating additional features that might enhance performance in more complex scenarios. Although the models perform well under known conditions, adaptive recalibration requires the ability to adjust predictions dynamically in response to variations in machining parameters. Next, developments should take into account changes in material properties, such as differences in hardness or cutting resistance which can impact force and power consumption, altering the expected outputs. Additionally, tool wear can lead to gradual deviations in the machining process, influencing the forces applied and energy consumption [14]. Environmental factors and process variations introduce further unpredictability, requiring a system capable of adapting to these evolving conditions. To achieve true adaptive recalibration, several enhancements could be implemented. Automated data collection and model retraining would ensure that real-time machining data are continuously logged and analyzed. If actual measured values deviate significantly from model predictions, an automated process could be triggered to retrain the network with the updated dataset, incorporating new machining conditions. Incremental learning could also be employed instead of full retraining, where model weights are fine-tuned using recent data rather than reconstructing the model from scratch. Transfer learning techniques could further enhance adaptability by adjusting a pre-trained model without discarding previously learned knowledge. The infrastructure for continuous data logging, performance evaluation, and trigger-based recalibration decisions has been outlined clearly, yet real-time integration with edge computing or embedded industrial systems has not been fully operationalized. Real machining power includes losses (friction and motor inefficiencies) beyond the ideal \(P\) = _F_-_v_ relationship, as seen in Table 1 (e.g., P = 1.62 kW at 16 rpm, F = 12.262 daN vs. 4.69 kW at 630 rpm, F = 47.123 daN). The ANN's inverse model, using P, current (I), and n, captures these nonlinearities, achieving high accuracy (R\({}^{2}\) = 0.997541, MAE = 3.6986 daN). Current provides additional electrical insights, while the braking device setup (Section 2.2) introduces system-specific effects the ANN adapts to. Unlike a static calculation, the ANN's recalibration (Section 2.1) handles dynamic changes, making it more robust for real-time Industry 4.0/5.0 applications. In this study, cutting forces (F) are labeled as independent variables (Table 1), yet in actual turning, forces depend on user-set parameters like the depth of cut (\(\alpha_{p}\)) and feed rate (\(f\)), not direct control. This presented experimental design from Section 2.2 employs a braking device to simulate forces (F: 0-290 daN) at varying spindle speeds (n: 16-1600 rpm) rather than actual material removal. Here, F and n serve as proxies for cutting conditions, with P and I measured as outcomes (Table 1), bypassing the direct use of \(\alpha_{p}\) and f. This controlled approach isolates force-speed-energy relationships, yielding high predictive accuracy (\(R^{2}\) > 0.99), but limits direct mapping to real cutting parameters. Future work aims to incorporate \(\alpha_{p},f,\) and v\({}_{\text{c}}\) via expanded experiments, enhancing applicability to industrial turning scenarios.