Judgement method for cutter failure

A judgment method and failure judgment model technology, applied in the direction of manufacturing tools, metal processing machinery parts, measuring/indicating equipment, etc., can solve problems such as increasing the defective rate of production products, tool failure, hidden dangers in production safety, etc., to speed up the calculation speed and calculation accuracy, to realize the effect of predicting in advance and reducing potential safety hazards

Active Publication Date: 2019-01-25
CHENGDU UNION BIG DATA TECH CO LTD
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When the amount of wear reaches a certain level, there may even be serious wear phenomena such as broken knife and edge chipping, which will lead to the failure of the knife
In the absence of effective tool wear monitoring and prediction methods, tool wear will not only increas

Method used

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  • Judgement method for cutter failure

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Embodiment Construction

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further elaborated below in conjunction with the accompanying drawings.

[0029] In this example, see figure 1 As shown, the present invention proposes a method for determining tool failure, comprising steps:

[0030] S1, use the current sensor to collect the current signal of the spindle of the processing machine, repeatedly sample the current signal with a time length of 1s and mark the current signal; obtain real-time current signal and historical current signal

[0031] S2, through feature learning, extract the eigenvalues ​​of the current signal:

[0032] S3. Cleaning the current signal data according to the characteristic value of the current signal, and normalizing the cleaned signal to obtain real-time input data and historical input data respectively;

[0033] S4, analyze the relationship between the current signal and tool breakage ...

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Abstract

The invention discloses a judgement method for a cutter failure. The method comprises the steps that a current sensor is used for collecting a current signal of a main shaft of a processing machine table, and the current signal is repeatedly sampled with 1 s as time length, marked and labelled; through feature learning, a feature value of the current signal is extracted; according to the feature value of the current signal, current signal data is cleaned, the cleaned signal is subjected to normalization, and input data is obtained; through machine learning and depth learning methods, the relation between the current signal and cutter breaking and cracks is analyzed, the input data is used as a training sample for training, and then a cutter failure judgement model is established; the inputdata is used as a testing sample to be input into the cutter failure judgement model testing, and a cutter failure assessment result is obtained. According to the judgement method for the cutter failure, cutter wear can be effectively monitored and predicted online in real time, the cutter failure states of various related factors including cutter breaking and edge cracking are predicted in advance, hidden safety dangers in the production process are greatly reduced, and the finished product rate of products is increased.

Description

technical field [0001] The invention belongs to the technical field of processing tool detection, and in particular relates to a method for judging tool failure. Background technique [0002] The tool is subjected to two main loads during the machining process: 1) the centrifugal force generated by high-speed rotation; 2) the cutting force generated by contact with the workpiece. As the machining time increases during the machining process, the degree of wear of the tool itself also increases. When the amount of wear reaches a certain level, serious wear phenomena such as broken knife and edge chipping may even occur, which will lead to tool failure. In the absence of effective tool wear monitoring and prediction methods, tool wear will not only increase the defective rate of production products, but also cause hidden dangers to safe production. At present, manufacturing companies can only find various factors related to tool breakage and edge chipping through daily experi...

Claims

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Application Information

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IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 不公告发明人
Owner CHENGDU UNION BIG DATA TECH CO LTD
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