公开(公告)号：US20210197335A1

公开(公告)日：2021-07-01

申请号：US16970301

申请日：2020-02-28

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Yongqing WANG ,  Mengmeng NIU ,  Kuo LIU ,  Bo QIN ,  Mingrui SHEN ,  Dawei LI

IPC: B23Q17/09 ,  G06N3/04 ,  G06N3/08

Abstract: The invention provides a data augmentation method based on generative adversarial networks in tool condition monitoring. Firstly, the sensor acquisition system is used to obtain the vibration signal and noise signal during the cutting process of the tool; second, the noise data subject to the prior distribution is input to the generator to generate data, and the generated data and the collected real sample data are input to the discriminator for identification, the confrontation training between the generator and the discriminator until the training is completed; then, use the trained generator to generate sample data, and determine whether the generated sample data and the actual tool state sample data are similar in distribution; finally, combined with the accuracy of the deep learning network model to predict the state of the tool to verify the availability of the generated data.

公开(公告)号：US20200249130A1

公开(公告)日：2020-08-06

申请号：US16470925

申请日：2019-02-21

Applicant: Dalian University of Technology

Inventor： Kuo LIU ,  Yongqing WANG ,  Haibo LIU ,  Xu LI ,  Mingrui SHEN ,  Mengmeng NIU ,  Ziyou BAN

IPC: G01M99/00 ,  F16C25/06

Abstract: A method for determining the preload value of the screw based on thermal error and temperature rise weighting. Firstly, thermal behavior test of the feed shaft under typical working conditions is carried out to obtain the maximum thermal error and the temperature rise at the key measuring points in each preloaded state. Then, a mathematical model of the preload value of the screw and the maximum thermal error is established; meanwhile, another mathematical model of the preload value of the screw and the temperature rise at the key measuring points is also established. Finally, the optimal preload value of the screw is obtained. The thermal error of the feed shaft and the temperature rise of the moving components are comprehensively considered, improving the processing accuracy and accuracy stability of the machine tool, and ensuring the service life of the moving components such as bearings.

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.

公开(公告)号：US20210287098A1

公开(公告)日：2021-09-16

申请号：US15734940

申请日：2020-02-28

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Kuo LIU ,  Mingrui SHEN ,  Bo QIN ,  Renjie HUANG ,  Mengmeng NIU ,  Yongqing WANG

IPC: G06N3/08 ,  G06N3/04

Abstract: An on line prediction method of part surface roughness based on SDAE-DBN algorithm. The tri-axis acceleration sensor is adsorbed on the rear bearing of the machine tool spindle through the magnetic seat to collect the vibration signals of the cutting process, and a microphone is placed in the left front of the processed part to collect the noise signals of the cutting process of the machine tool; the trend term of dynamic signal is eliminated, and the signal is smoothed; a stacked denoising autoencoder is constructed, and the greedy algorithm is used to train the network, and the extracted features are used as the input of deep belief network to train the network; the real-time vibration and noise signals in the machining process are input into the deep network after data processing, and the current surface roughness is set as output by the network.