Feature extraction multi-objective optimization method for wear condition of milling tool

A multi-objective optimization, wear state technology, applied in manufacturing tools, metalworking mechanical parts, measuring/indicating equipment, etc., to avoid the effect of low prediction accuracy

Active Publication Date: 2019-02-12
温州大学苍南研究院
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Problems solved by technology

However, characteristic parameters with strong correlation do not necessarily achieve good monitoring performance

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  • Feature extraction multi-objective optimization method for wear condition of milling tool
  • Feature extraction multi-objective optimization method for wear condition of milling tool
  • Feature extraction multi-objective optimization method for wear condition of milling tool

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Embodiment Construction

[0040] The present invention is described in further detail below in conjunction with accompanying drawing:

[0041] Such as figure 1 with figure 2 As shown, in the embodiment of the present invention, a multi-objective optimization method for feature extraction of milling tool wear state is proposed, which includes the following steps:

[0042] S1. Collect multiple physical field signals through multiple sensor channels. It mainly includes:

[0043] For new cutting tools, periodically collect the time-domain signals of sensing channels of S physical fields (such as vibration, current, acoustic emission, cutting force, sound, etc.) and the corresponding tool wear under the tool running state, and collect T times to form a training sample set (X, Y)={(X t ,Y t )}, X t ∈ R N×T , Y t ∈R,t=1,2,...,T,X t =(x 1t ,...,x St ) represents the signals of all S sensing channels when the signal is collected for the tth time, Y t Indicates the corresponding tool wear amount whe...

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Abstract

The invention discloses a feature extraction multi-objective optimization method for a wear condition of a milling tool. The multi-objective optimization method comprises the following steps of collecting multiple physical field signals by multiple sensor channels; calculating a plurality of statistical feature parameters and wavelet energy in a time domain and a frequency domain of a signal of each sensor channel to form a feature parameter candidate set; constructing a multi-objective optimization model by taking tool condition prediction accuracy and a feature parameter number as optimization objectives; performing global optimization on the optimization model by employing an intelligent optimization algorithm; and making a sensor feature parameter set corresponding to an optimal solution of the optimization model serve as feature parameters required by tool condition monitoring. The multi-objective optimization method disclosed by the invention has the following advantages and effects that tool wear loss prediction accuracy corresponding to each feature parameter set is inspected from prediction accuracy by taking the prediction accuracy and the feature parameter number as theoptimization objectives, so that the phenomenon of high relativity but low prediction accuracy is avoided.

Description

technical field [0001] The invention relates to the field of manufacturing process monitoring, in particular to a multi-objective optimization method for feature extraction of milling tool wear state. Background technique [0002] With the increasingly fierce market competition, manufacturing enterprises have an increasing demand for automation of the production process, and the automation of CNC milling machines is an important part of the automation of most manufacturing processes. Milling tool is the most vulnerable part of CNC milling machine, so it is very important to carry out timely and effective condition monitoring and fault identification. [0003] In recent years, the indirect milling tool condition monitoring (TCM) method based on multi-sensor feature fusion has attracted extensive attention from scholars at home and abroad. The TCM method based on multi-sensing feature fusion is to obtain the relevant signals of the cutting process through multiple physical fi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 周余庆雷芝向家伟孙维方钟永腾
Owner 温州大学苍南研究院
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