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Online prediction method for future tool wearing capacity

A tool wear and prediction method technology, applied in the direction of manufacturing tools, measuring/indicating equipment, metal processing machinery parts, etc., to achieve the effect of strong generalization performance

Active Publication Date: 2019-06-28
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The invention solves the problem of predicting the evolution trend of tool wear, has the characteristics of simple process, fast processing, accurate prediction and good generalization performance, and can be applied to cutting processes under different working conditions

Method used

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  • Online prediction method for future tool wearing capacity
  • Online prediction method for future tool wearing capacity
  • Online prediction method for future tool wearing capacity

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Embodiment Construction

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

[0033] Such as figure 1 As shown, an online prediction method for future tool wear provided by the present invention includes the following steps:

[0034] Step 1: Take the tool wear from the 1st moment to the mth moment in the machining process as the input sample to form a two-dimensional tensor M with a structure of [m,1] 1 , Take the tool wear at the time m+1 to m+n as the output sample to form a two-dimensional tensor N with the structure [n,1] 1 , Two-dimensional tensor M 1 And the two-dimensional tensor N 1 A sample pair is formed, where m is the number of historical moments of the user-defined tool wear, and n is the number of future moments of the user-defined tool wear; then the tool wear from the second time to the m+1 time As an input sample, form a two-dimensional tensor M with the structure [m,1] 2 , Take the tool wear from m+2 to m+n+1 as the output sample...

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Abstract

The invention provides an online prediction method for future tool wearing capacity. The tool wearing capacity for some time to come is predicted by taking tool wearing capacity data in some time agoas input. The online prediction method for the future tool wearing capacity comprises the steps that firstly, influences of the historical wearing capacity on the future wearing capacity are calculated through a long-short-term memory unit encoder, and a state tensor is generated; secondly, the state tensor is taken as input of a long-short-term memory unit decoder, and the wearing capacity for some time to come is generated through the decoder; and in the encoding and decoding process, the encoder, the decoder and the state tensor form a recurrent neural network for predicting future wearingcapacity changes, internal parameters of the long-short-term memory unit encoder and the long-short-term memory unit decoder are automatically obtained through an adam algorithm, and influence factorsof the historical wearing capacity are adjusted. According to the online prediction method for the future tool wearing capacity, the tool wearing capacity evolution trend prediction problem is solved, and the online prediction method for the future tool wearing capacity has the characteristics that the process is easy and convenient to operate, the processing speed is high, prediction is accurate, and the generalization performance is good and can be suitable for the cutting process under different working conditions.

Description

Technical field [0001] The invention belongs to the technical field of numerical control processing, and specifically relates to a method for predicting tool wear. Background technique [0002] In the process of CNC cutting, the tool gradually fails with the increase of wear. Tool wear has a very important impact on the surface quality of the workpiece and the control of processing costs. Through the processing and identification of cutting force, vibration, and acoustic emission signals, the wear status of the tool can be monitored indirectly, and the subjectivity of human judgment can be avoided. However, merely monitoring tool wear cannot meet the needs of intelligent manufacturing. If you cannot predict the amount of tool wear in the future, you cannot make decisions about tool changes or cutting parameter optimization in advance. Therefore, the future development trend of online prediction of tool wear is one of the key issues that need to be resolved in the field of inte...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B23Q17/09
Inventor 莫蓉张纪铎孙惠斌曹大理
Owner NORTHWESTERN POLYTECHNICAL UNIV
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