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Tool wear detection method

A tool wear and detection method technology, applied in the direction of manufacturing tools, measuring/indicating equipment, metal processing equipment, etc., can solve problems such as weak generalization ability, interference processing, affecting processing accuracy and product quality, and achieve strong learning ability , the effect of high prediction accuracy

Inactive Publication Date: 2021-10-22
中国科学院沈阳计算技术研究所有限公司
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Problems solved by technology

In the process of high-speed milling, it is difficult to detect the state of tool wear, and severe tool wear will affect machining accuracy and product quality
[0003] The traditional method mainly relies on the cutting force coefficient to analyze tool wear, needs to install additional force measuring devices, and interferes with processing, so there are inevitable problems of human factors
At the same time, the existing shallow models are more for analysis and discrimination of a certain type or a certain signal, so it is very easy to fall into local optimum. In order to solve the problems of weak learning ability and weak generalization ability of shallow models, The present invention proposes a tool wear detection method based on 3-KMMBS

Method used

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Embodiment Construction

[0032] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0033] The features extracted by the deep neural model are more natural and hierarchical, and can discover complex information in high-dimensional data structures, avoiding the drawbacks of shallow models that require human participation to extract sample features. At the same time, the pre-training process is used to avoid the problem that the shallow model is easy to fall into local optimum. Combining the advantages of deep learning and the characteristics of tool wear, the present invention proposes a tool wear detection method based on 3-KMMBS (where 3-K stands for 3-K-Means algorithm, MB stands for Multi-selectionMultihiddenlayerBP neural network, S stands for Softmax classifier). In order to realize the above parts, the technical solution adopted by the present invention is as follows: a tool detection method, comprising the following s...

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Abstract

The invention relates to a numerical control technology and a neural network technology, in particular to a tool wear detection method. The method comprises the following steps: firstly, collecting vibration and acoustic emission signals in different axial directions during milling of a high-speed cutter, and carrying out data preprocessing; then, adopting an improved 3-K-Means clustering algorithm to cluster three wear state intervals of the cutter, proposing a multi-selection multi-hidden-layer neural network structure to carry out feature learning on the three wear state intervals, and adopting Softmax for classification; and finally, performing parameter fine adjustment on the whole deep network by adopting stochastic gradient descent, and establishing a tool wear detection model. Experimental results show that the accuracy rate of the method provided by the invention on tool wear detection is as high as 95%.

Description

technical field [0001] The invention relates to numerical control technology and neural network technology, in particular to a tool wear detection method. Background technique [0002] In the process of high-speed milling, the wear condition of the tool is extremely complicated, and it is difficult to detect the wear state of the tool through the traditional artificial model method. How to solve this problem through a more effective method has become a research hotspot in the field of intelligent machining tools. In the process of high-speed milling, it is difficult to detect the state of tool wear, and severe tool wear will affect machining accuracy and product quality. [0003] The traditional method mainly relies on the cutting force coefficient to analyze the tool wear, needs to install an additional force measuring device, and interferes with the processing, so there are inevitable problems of human factors. At the same time, the existing shallow models are more for an...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 王鸿亮黄鹤翔刘璐朱湘宇李航宇
Owner 中国科学院沈阳计算技术研究所有限公司
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