公开(公告)号：US11761930B2

公开(公告)日：2023-09-19

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

CPC classification number: G01N29/4481 ,  G01B17/08 ,  G01N29/14 ,  G05B19/401 ,  G06N3/04 ,  G06N3/08 ,  G01N2291/028 ,  G01N2291/2698 ,  G05B2219/33099

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：US11287795B2

公开(公告)日：2022-03-29

申请号：US16639959

申请日：2019-02-21

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Kuo Liu ,  Yongqing Wang ,  Jiakun Wu ,  Haining Liu ,  Mingrui Shen ,  Bo Qin ,  Haibo Liu

IPC: G05B19/404

Abstract: A self-adaptive compensation method for feed axis thermal error, which belongs to the field of error compensation in NC machine tools. First, based on laser interferometer and temperature sensor, the feed axis thermal error test is carried out; following, the thermal error prediction model, based on the feed axis thermal error mechanism, is established and the thermal characteristic parameters in the model are identified, based on the thermal error test data; next, the parameter identification test is carried out, under the preload state of the nut; next, the adaptive prediction model is established, based on the thermal error prediction model, while the parameters in the measurement model are identified; finally, adaptive compensation of thermal errors is performed, based on the adaptive error prediction model, according to the generated feed axis heat.

公开(公告)号：US12026625B2

公开(公告)日：2024-07-02

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: G06N3/088 ,  G06N3/04 ,  G06N3/045 ,  G06N3/047

CPC classification number: G06N3/088 ,  G06N3/045 ,  G06N3/047

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.

公开(公告)号：US11467066B2

公开(公告)日：2022-10-11

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.