Tool wear condition prediction method of numerical control machine tool based on parallel deep neural network

Abstract

The invention discloses a tool wear condition prediction method of a numerical control machine tool based on a parallel deep neural network. A dynamometer, an acceleration sensor and an acoustic sensor are installed on a workbench and a fixture of the numerical control machine tool; a milling experiment is conducted, the cutting force and vibration and acoustic signals of a milling process are collected so as to obtain multisensor data, and the wear capacity of a tool is collected; pretreatment is performed so as to obtain training data and to-be-tested data; a parallel deep neural network model is established; the treated training data and the label of the wear capacity of the tool are input into an offline training model in the parallel deep neural network model; and the to-be-tested multisensor data are introduced into the trained model so as to predict the wear capacity of the tool in real time and on line. According to the method, the implied characteristics during tool processingof the numerical control machine tool are fully mined, and the wear capacity of the tool can be predicted in real time. The method has the advantage of wide applicability and can be widely applied tovarious numerical control machine tools.

Description

technical field [0001] The invention relates to a method for predicting the wear state of a numerically controlled machine tool, in particular to a method for predicting the wear state of a numerically controlled machine tool based on a parallel neural network, and belongs to the field of predicting the wear state of a numerically controlled machine tool. Background technique [0002] In the process of machining parts by CNC machine tools, the state of use of the tool has a crucial impact on the quality of machining. Severely worn tools will lead to poor machining accuracy of the parts, and even lead to the scrapping of the workpiece in severe cases, which will greatly increase the processing cost and affect the construction period. Therefore, it is very urgent and meaningful to predict the amount of tool wear. [0003] Usually, it is difficult to directly measure the amount of tool wear, and more sophisticated instruments and complex measurement methods are required. There...

AI Technical Summary

Problems solved by technology

However, this type of method still has certain limitations, such as low prediction accuracy and limited applicability caused by the model's weak data processing ability.

Images