公开(公告)号：US20210364482A1

公开(公告)日：2021-11-25

申请号：US17260156

申请日：2020-03-06

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Yongqing WANG ,  Bo QIN ,  Kuo LIU ,  Mingrui SHEN ,  Mengmeng NIU ,  Honghui WANG ,  Lingsheng HAN

IPC: G01N29/44 ,  G06N3/04 ,  G06N3/08 ,  G05B19/401 ,  G01N29/14 ,  G01B17/08

Abstract: A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.