It is a fact that acoustic emission(AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection.However,AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems,it is difficult to obtain a reliable result by these raw AE data.It is generally known that the process of tool wear belongs to detect weak singularity signals in strong noise.The objective of this paper is to combine Newland Harmonic wavelet and Richman-Moorman(2000) sample entropy for detecting weak singularity signals embedded in strong signals.First,the raw AE signal is decomposed by harmonic wavelet and transformed into the three-dimensional time-frequency mesh map of the harmonic wavelet,at the same time,the contours of the mesh map with log space is induced.Second,the profile map of the three-dimensional time-frequency mesh map is offered,which corresponds to decomposed level on harmonic wavelets.Final,by computing sample entropy in each level,the weak singularity signal can be easily extracted from strong noise.Machining test was carried out on HL-32 NC turning center.This lathe does not have a tailstock.Tungsten carbide finishing tool was used to turn free machining mild steel.The work material was chosen for ease of machining,allowing for generation of surfaces of varying quality without the use of cutting fluids.In turning experiments,the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions with the sharp tool and the worn tool,54 group data are sampled by AE.The sample entropy of each level of wavelet decomposed for each one of 54 AE datum is computed,wear tool and shaper tool can be distinguished obviously by the sample entropy value at the 12th level,this is a criterion.The proposed research provides a new theoretical basis and a new engineering application on the tool condition monitoring. It is a fact that acoustic emission (AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection. However AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems, it is difficult to obtain a reliable result by these raw AE data. It is generally known that the process of tool wear belongs to detect weak singularity signals in strong noise. The objective of this paper is to combine Newland Harmonic wavelet and Richman-Moorman (2000) sample entropy for detecting weak singularity signals embedded in strong signals. First, the raw AE signal is decomposed by harmonic wavelet and transformed into the three-dimensional time-frequency mesh map of the harmonic wavelet, at the same time, the contours of the mesh map with log space is induced. Second, the profile map of the three-dimensional time-frequency mesh map is offered, which corresponds to decomposed level on harmonic wavelets. Final, by computing sample entropy in each level, the weak singularity signal can be easily extracted from strong noise. Machining test was carried out on HL-32 NC turning center. This lathe does not have a tail stock.Tungsten carbide finishing tool was used to turn free machining mild steel The work material was chosen for ease of machining, allowing for generation of surfaces of varying quality without the use of cutting fluids.In turning experiments, the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions with the sharp tool and the worn tool, 54 group data are sampled by AE. sample entropy of each level of wavelet decomposed for each one of 54 AE datum is computed, wear tool and shaper tool can be obviously obviously by the sample entropy value at the 12th level, this is a criterion.The proposed research provides a new theoretical basis and a new engineering application on the tool condition monitoring.