Cutter wear state recognition and prediction method based on hidden Markov model

Abstract

The invention discloses a cutter wear state recognition and prediction method based on a hidden Markov model. The method comprises: extracting signal features; obtaining 41 signal characteristic quantities through screening; dividing the cutter abrasion stage into an initial abrasion stage, a stable abrasion stage, a rapid abrasion stage, a serious abrasion stage and a wear-out stage; constructinga wear stage identification model; constructing a cutter remaining service life prediction model; performing online wear stage identification and residual service life prediction: collecting a tool machining process signal in real time in an online link, extracting characteristic quantities according to a model training process, and respectively inputting the characteristic quantities as observation sequences into a wear stage identification model and a residual service life prediction model for stage identification and residual service life prediction; and model training updating: repeatingthe steps S1 to S6 on the new signal and the tool wear state data along with monitoring data accumulation, and updating the wear stage identification model and the remaining service life prediction model. Reference is provided for online identification and prediction of the tool wear state.

Description

technical field [0001] The invention relates to the field of tool intelligent state monitoring, in particular to a tool wear state recognition and prediction method based on an extended optimization hidden Markov model. Background technique [0002] The quality of machining depends on the performance of machining equipment. As the most direct factor affecting the quality of machining, the on-line monitoring of tool wear status is of great significance to the stable production of increasingly complex production systems. During the use of the tool, there is bound to be performance degradation. The light one will affect the processing accuracy and product quality consistency, and the severe one will directly lead to unqualified product quality and equipment shutdown. At present, the planned tool change is usually carried out in the way of regular or fixed processing volume, which leads to low tool use efficiency and greatly increases production costs. In recent years, with the...

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Problems solved by technology

[0004] New methods are constantly emerging in the above-mentioned technical links, but most of them are based on theoretical research and experimental testing of artificial intelligence models and machine learning methods, and there are difficulties in practical application

In practical applications, regular shutdown detection plans are still used to change tools, or online health index monitoring, and the implementation efficiency and accuracy need to be improved

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