Simulating realistic wear behavior in a virtual machining environment is complex due to the nonlinear nature of wear mechanisms (e.g., abrasion, adhesion, diffusion).
Capturing the gradual transition from a healthy to a worn tool condition with accurate data representation required careful calibration and modeling.
Creating a seamless pipeline between the sensor system and the microcontroller block was challenging, especially for real-time data acquisition and synchronization.
Simulating digitization and preprocessing with correct timing and latency behavior posed technical integration issues.
Finite Element Analysis, particularly with dynamic simulations (e.g., modal or transient analysis), was computationally intensive.
High-fidelity simulations needed fine meshing and long run times, which affected iteration speed during development.
Training the predictive model to accurately classify tool condition required a diverse dataset representing various wear scenarios.
Generating and labeling sufficient synthetic data for training, validation, and testing was time-consuming and needed careful handling to avoid bias or overfitting.
Designing a real-time GUI that updates condition status, raises alerts, and logs data without lag involved UI/UX challenges and required efficient backend processing.
Ensuring that alerts are neither too frequent (false positives) nor too late (missed failures) required precise threshold setting and validation.
Comparing simulated data with actual machining data to validate system accuracy was limited by the availability of experimental datasets or physical setups.
Bridging the gap between virtual predictions and real-world behavior remains an ongoing challenge for deployment in industrial environments.