The article "Advanced Monitoring of Machining Operations" by R. Teti, K. Jemielniak, G. O'Donnell, and D. Dornfeld provides a comprehensive review of sensor monitoring techniques and advanced signal processing methods for machining operations. The authors highlight the historical development of sensor monitoring, from early research on tool wear and unmanned machining to more recent advancements in signal processing and decision-making strategies. They discuss various sensor types, including motor power and current sensors, force and torque sensors, acoustic emission (AE) sensors, and vibration sensors, detailing their mechanisms, advantages, and limitations. The article also delves into advanced signal processing techniques such as pre-processing methods (filtering, amplification, A/D conversion), feature extraction (time domain, frequency domain, and time-frequency domain), and pattern recognition techniques (Auto Regressive, Moving Average, Principal Component Analysis, Singular Spectrum Analysis, Permutation Entropy). These methods are crucial for extracting meaningful information from sensor signals to monitor tool and process conditions. Finally, the authors present case studies and applications of these technologies in industrial settings, emphasizing the importance of sensor-based monitoring for improving machining processes, tool life, and overall productivity. They also discuss future challenges and trends in sensor-based machining operation monitoring, highlighting the need for more robust and adaptive systems to handle complex machining environments.The article "Advanced Monitoring of Machining Operations" by R. Teti, K. Jemielniak, G. O'Donnell, and D. Dornfeld provides a comprehensive review of sensor monitoring techniques and advanced signal processing methods for machining operations. The authors highlight the historical development of sensor monitoring, from early research on tool wear and unmanned machining to more recent advancements in signal processing and decision-making strategies. They discuss various sensor types, including motor power and current sensors, force and torque sensors, acoustic emission (AE) sensors, and vibration sensors, detailing their mechanisms, advantages, and limitations. The article also delves into advanced signal processing techniques such as pre-processing methods (filtering, amplification, A/D conversion), feature extraction (time domain, frequency domain, and time-frequency domain), and pattern recognition techniques (Auto Regressive, Moving Average, Principal Component Analysis, Singular Spectrum Analysis, Permutation Entropy). These methods are crucial for extracting meaningful information from sensor signals to monitor tool and process conditions. Finally, the authors present case studies and applications of these technologies in industrial settings, emphasizing the importance of sensor-based monitoring for improving machining processes, tool life, and overall productivity. They also discuss future challenges and trends in sensor-based machining operation monitoring, highlighting the need for more robust and adaptive systems to handle complex machining environments.