Table 1 Dataset variables used in the... | Emerald Publishing

Table 1

Dataset variables used in the proposed physics-guided anomaly detection framework

Category	Variables	Physical significance
Process parameters	N, ap, ae, F, Z, D	Govern cutting mechanics, chip formation, tool engagement, material removal, and machining stability
MTConnect sensor variables	watt, energy, smarms, xmarms, ymarms, zmarms, Spindle_Speed, S_Axis_Load	Represent spindle power behaviour, vibration severity, spindle loading, machine dynamics, and process instability
Machining labels	Normal cutting, abnormal cutting, tool defect	Define machining health state and provide the target classes for anomaly detection

Category	Variables	Physical significance
Process parameters	N, ap, ae, F, Z, D	Govern cutting mechanics, chip formation, tool engagement, material removal, and machining stability
MTConnect sensor variables	watt, energy, smarms, xmarms, ymarms, zmarms, Spindle_Speed, S_Axis_Load	Represent spindle power behaviour, vibration severity, spindle loading, machine dynamics, and process instability
Machining labels	Normal cutting, abnormal cutting, tool defect	Define machining health state and provide the target classes for anomaly detection