Chongqing University of Posts and Telecommunications

Institute of Industrial Internet, Chongqing University of Posts and Telecommunications

Zhen Kang

Yan Zhang

Qingqing Huang

Ping Wang

Shuaiyong Li

Jiajun Zhou

Yan Han

Disclosed is a prediction method for tool remaining life of a numerical control machine tool based on a hybrid neural model, including: constructing a hybrid neural network model, specifically including the following steps: constructing sample data according to the sampling frequency of tool data; obtaining a first feature vector representing the tool life by utilizing a convolutional neural network and a long short-term memory network; generating working condition signals of sampling points into a second feature vector representing the tool life by utilizing an NFM neural network; and inputting a current working time of a tool and the acquired feature vectors into a multi-layer perceptron for fusion to predict the tool life.

Prediction method for tool remaining life of numerical control machine tool based on hybrid neural model

A method for predicting a remaining life of a tool of a computer numerical control machine is provided. In the method, indirect measurement indicators of the tool are selected based on monitoring and analyzing a current state of the tool, a prediction model for the remaining life of the tool is established based on data de-noising, feature extraction and a multi-kernel W-LSSVM algorithm. Thereby, a method for predicting a remaining life of a tool of a computer numerical control machine is provided.

Method for predicting remaining life of numerical control machine tool

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Chongqing, China

Zhen Kang