Study on the extraction method of tool wear symptom based on wavelet packet analysis

With the emergence of PCBN cutting tools, hard turning technology was used more and more widely in the difficult-to-cut materials. Therefore, it is particularly important to study the tool wear mechanism of ultrasonic hard cutting and intelligent monitoring technology of tool wear. Then, a multisignal intelligent monitoring test platform of tool wear was established through Kistler9257B resistance dynamometer, the INV306DF east vibration isolation system and Japan Roy keane and high-speed microscopic photography system. The test was designed about GCr15 hardened bearing steel machined by PCBN under ultrasonic vibration and tool wear mechanism was analyzed. A special characterization and the influence of cutting conditions on tool wear are obtained under the conditions of ultrasonic cutting. Because the sensor signal information Obtained from can´t be directly used to identify the tool wear state a variety of signals collected in different processing time were analyzed and disposed in this paper. And the vibration signal were analyzed by wavelet transform. Then characteristics of the tool wear state were obtained.