Developing a deep learning-based uncertainty-aware tool wear prediction method using smartphone sensors for the turning process of Ti-6Al-4V

Engineering

• Deep Learning Method 100%
• Error Value 20%
• Experimental Result 20%
• Kalman Filter 20%
• Learning Approach 20%
• Mean Absolute Error 20%
• Model Uncertainty 20%
• Noise Level 40%
• Noise Suppression 20%
• Prediction Performance 20%
• Probabilistic Method 20%
• Process Monitoring 20%
• Sensor Data 20%
• State-of-the-Art Method 20%
• Ti-6al-4v 100%
• Uncertainty Quantification 20%

Material Science

• Density 50%
• Machining 100%
• Ti-6Al-4V 100%