Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms

An effort was made to monitor the flank wear using wavelet analysis by extracting the Hoelder’s exponent as a feature and using various machine learning algorithms to forecast the tool condition. The test was conducted on a Tungsten carbide insert with selected cutting parameters and the acquired vibration signals were used to develop the prediction model. The wavelet coefficients, Hoelder’s exponent, and statistical features were extracted from...