Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain

Uekita, Masahiro; Takaya, Yasuhiro

doi:10.1016/j.measurement.2017.02.035

Cited by 59 publications

(18 citation statements)

References 33 publications

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“…For the purpose of NC machining of turbo drill impeller, Wang et al put forward a comprehensive processing method based on UG software [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Integral Forming of Turbine Drill Impeller

2019

IJOMAM

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In order to reduce the cost of small batch production of turbodrill and improve the quality of blade processing, the integrated processing of turbodrill impeller is taken as the main research content. Firstly, the WZ-115 turbodrill impeller is used to process the turbodrill impeller. Secondly, the three-dimensional model of the blade output from ANSYS/BladeGen module is drawn by UG software. After obtaining the three-dimensional model, the processing module of UG software is used to design the processing technology, determine the processing steps and routes, and compile the processing program. The tool path of rough and finish machining is observed and modified by using simulated processing, and the amount of tool eaten at each stage is improved to generate the processing program. Finally, the impeller is measured by the coordinate measuring instrument, and the errors of the data are analyzed by UG software. The results show that the turbodrill impeller meets the requirements of processing accuracy, the surface profile meets the design requirements, the surface processing quality meets the expectations, and the turbodrill impeller has the ability of actual processing, which can effectively solve the problem of high cost in small batch production such as university test and enterprise research and development. It can be seen that the integral forming technology of turbo drill impeller can effectively solve the problem of long production cycle in the process of turbo drill development and test, and control it. At the same time, the cost can efficiently and quickly complete the processing of turbo drill, and can ensure the processing quality.

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“…For the purpose of NC machining of turbo drill impeller, Wang et al put forward a comprehensive processing method based on UG software [4].…”

Section: Literature Reviewmentioning

confidence: 99%

Integral Forming of Turbine Drill Impeller

2019

IJOMAM

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“…Zhang et al [10] used multiple sensor data, such as cutting vibration data and power data, as well as actual processing parameters to develop a system to realize tool condition-monitoring and life estimation efficiently, which is widely used in small and medium-sized enterprises. Uekita et al [11] presented a tool condition monitoring technique combining short-time Fourier transform and spectral kurtosis analysis to identify chatter process, which will guarantee the machining precision of large-size components and provide tool state information. Javier et al [12] proposed a Gaussian mixture model based on dynamic probabilistic clustering for tool condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Huang

et al. 2020

Sensors

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Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.

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“…There are other methods devoted to the applications of online chatter detection, such as chatter identification based on wavelet transform (WT) and support vector machine (SVM), (11) Hilber-Huang transform (HHT), (12) and short-time Fourier transform (STFT). (13,14) The time-frequency signal processing tools, such as STFT, WT, HHT, and Teager-Huang transform (THT), are gradually applied to dynamic signal analyses owing to their superior performance in both time and frequency domains simultaneously. The signal-resolving powers of STFT and WT are limited by the time window function and the mother wavelet type, respectively.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Signal Analysis by Teager–Huang Transform for Milling Chatter Recognition

Lee¹,

Yu²,

Wang³

et al. 2020

Sensors and Materials

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Many types of sensor have been utilized to monitor milling vibration, and many analysis methods are devoted to the investigation of milling vibration or milling dynamics. In this work, a noncontact sensor and a time-frequency domain analysis method were applied to identify the state of milling vibration. A microphone was employed in practical tests to record the milling dynamics. The Teager-Huang transform (THT) was adopted for the acoustic signal analysis owing to its high resolution in the time-frequency domain. The potential frequency range for the analysis of milling dynamics is reported in this work to improve the recognition accuracy of milling vibration limited by the effect of environmental noise. The THT was used to distinguish the chatter state from the normal milling dynamics. In addition, the statistical index called the coefficient of variation was applied to define the threshold of chatter occurrence. Milling experiments (including dry and wet cuttings) were performed to verify the proposed chatter detection method.

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Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain

Cited by 59 publications

References 33 publications

Integral Forming of Turbine Drill Impeller

Integral Forming of Turbine Drill Impeller

A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Acoustic Signal Analysis by Teager–Huang Transform for Milling Chatter Recognition

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