Tool abrasion state identification method based on convolutional neural network and long-short-time memory neural network combined model

Abstract

The invention discloses a tool abrasion state identification method based on a convolutional neural network and long-short-time memory neural network combined model. A force measuring instrument and an acceleration sensor are arranged on a workbench clamp of a numerical control machine tool and a workpiece, three-direction force signals and vibration acceleration signals are collected, collected data are subjected to data pre-processing, normalization processing and unified segmentation are conducted on the same row of data, one-dimension data are converted into two-dimension data to serve asinput, the convolutional neural network in the combined model is used for extracting abstraction features, the long-short-time memory neural network in the combined model is used for finding relevancebetween the data, and finally the tool abrasion state is output. An established double-network structure is arranged in a serial manner, the internal relation between the two kinds of signals can beestablished, the more abstract features are extracted through convolution, the timing sequence feature is determined according to the long-short-time memory, accordingly, the purpose of deeper relation of the data and the model is achieved, and applicability is achieved on various machine tools.

Description

technical field [0001] The invention belongs to a state recognition method of a numerically controlled machine tool processing tool, and belongs to the field of state recognition of a numerically controlled machine tool processing tool, or a deep learning milling cutter state recognition method. Background technique [0002] Tool wear state recognition is to collect the original signal in the process of production and processing to the computer through several sensors during the production process, and to establish a deterministic relationship with the tool wear state by performing a series of signal processing on the original signal , and finally through the intelligent state identification algorithm to identify the current wear state of the tool. [0003] According to the depth of the network structure of the recognition model used, it can be divided into two categories, one is the shallow recognition model of "feature extraction + machine learning", and the other is to si...

AI Technical Summary

Problems solved by technology

[0006] The traditional machine learning method cannot reflect the changes in the time series of the wear amount. Therefore, it is very important to propose a joint network of self-extracting features, convolution and long-short-term memory neural networks to find the internal relationship between tool signals.

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