Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process

Keyphrases

• Convolutional Neural Network 100%
• Tool Condition Monitoring 100%
• Milling Process 100%
• Spectral Subtraction 100%
• Monitoring System 66%
• Tool Wear 66%
• Tool Wear Monitoring 66%
• Fault Indicator 66%
• Deep Learning 33%
• Support Vector Machine 33%
• Machine Learning 33%
• High Temperature 33%
• Vibration Signal 33%
• Feature Extraction Methods 33%
• Deep Learning Algorithm 33%
• Cutting Parameters 33%
• Machine Learning Techniques 33%
• Surface Quality 33%
• Machining Operations 33%
• Wavelet 33%
• Increased Productivity 33%
• Tool Breakage 33%
• Machining Process 33%
• Wavelet Packet 33%
• Current Signal 33%
• Milling Operation 33%
• Common Source 33%
• Packet-based 33%
• High Force 33%
• Force Signal 33%
• Process Monitoring 33%
• Time-frequency 33%
• LIPPS 33%
• Online Tool 33%
• Time-frequency Transform 33%
• Hybrid Feature Extraction 33%
• Tool Wear Rate 33%
• Tool Condition Monitoring System 33%
• Unscheduled Downtime 33%
• Methodological Support 33%
• Neural Network-based 33%
• Spectral Subtraction Algorithm 33%
• K-nearest Neighbor Method 33%

Engineering

• Condition Monitoring 100%
• Convolutional Neural Network 100%
• Deep Learning Method 66%
• Monitoring System 66%
• Learning System 33%
• Support Vector Machine 33%
• Feature Extraction 33%
• Cutting Parameter 33%
• Machining Operation 33%
• Surface Quality 33%
• Nearest Neighbor 33%
• Force Signal 33%
• Isolation Method 33%
• Breakage 33%
• Current Signal 33%
• Process Monitoring 33%
• Machine Learning Method 33%
• Condition Monitoring System 33%
• vibration signal 33%

Computer Science

• Convolutional Neural Network 100%
• Monitoring System 100%
• Condition Monitoring 100%
• Spectral Subtraction 100%
• Deep Learning Method 66%
• Machine Learning 66%
• Learning System 66%
• Support Vector Machine 33%
• Feature Extraction 33%
• Process Monitoring 33%
• Frequency Transformation 33%
• Neighbor Method 33%
• Machining Operation 33%