公开(公告)号：US20200064126A1

公开(公告)日：2020-02-27

申请号：US16462238

申请日：2018-01-23

Applicant: Dalian University of Technology

Inventor： Yongqing WANG ,  Kuo LIU ,  Jiakun WU ,  Haibo LIU ,  Zhisong LIU ,  Haining LIU

IPC: G01B11/27

Abstract: A method for the rapid detection of the linear axis angular error of an NC machine tool, belongs to the technical field of NC machine tool testing. Firstly, the measuring device is mounted on the linear axis. Then, the linear axis moves at three different speeds at a constant speed, and the upper measurement system automatically performs multi-channel acquisition and storage of the motion measurement's point measurement data. Then, based on the same geometric error signal, it is decomposed into the different frequency components, and the measurement angle error is filtered at the different speeds. Finally, the measurement angle errors at the three speeds after filtering are superimposed to complete the rapid measurement of the linear axis angular error of the machine tool. The measurement efficiency is high and data processing capability is strong.

公开(公告)号：US20200272134A1

公开(公告)日：2020-08-27

申请号：US16471478

申请日：2018-08-30

Applicant: Dalian University of Technology

Inventor： Kuo LIU ,  Haibo LIU ,  Te LI ,  Haining LIU ,  Yongqing WANG ,  Zhenyuan JIA

IPC: G05B19/418

Abstract: An application method of the thermal error-temperature loop in the spindle of a CNC machine tool. This uses a bar and two displacement sensors to determine radial thermal errors of the spindle. Meanwhile two temperature sensors are used to determine the temperature of the upper and lower surfaces of the spindle box. Then, the thermal error-temperature loop is drawn with the temperature difference between two temperature sensors as the abscissa and the radial thermal error of the spindle as the ordinate. Finally, the loop is employed to analyze the mechanism of the radial thermal deformation of the spindle and the thermal error level is evaluated. Since the method is based on measured data, the results of the analysis are closer to the reality, compared to those from the numerical simulations.

公开(公告)号：US20200064810A1

公开(公告)日：2020-02-27

申请号：US16325984

申请日：2017-11-06

Applicant: Dalian University of Technology

Inventor： Kuo LIU ,  Yongqing WANG ,  Haibo LIU ,  Te LI ,  Haining LIU ,  Dawei LI

IPC: G05B19/404 ,  B23Q11/14 ,  B23Q17/22

Abstract: The invention provides a method for modeling and compensating for the spindle's radial thermal drift error in a horizontal CNC lathe, which belongs to the field of error compensation technology of CNC machine tools. Firstly, the thermal drift error of two points in the radial direction of the spindle and the corresponding temperature of the key points are tested; then the thermal inclination angle of the spindle is obtained based on the thermal tilt deformation mechanism of the spindle, and the correlation between the thermal inclination angle and the temperature difference between the left and right sides of the spindle box is analyzed. According to the positive or negative thermal drift error of the two points that have been measured and the elongation or shortening of the spindle box on the left and right sides, the thermal deformation of the spindle is then classified and the thermal drift error model under various thermal deformation attitudes is then established. Then the influence of the size of the machine tool's structure on the prediction results of the model is analyzed. In real-time compensation, the thermal deformation attitude of the spindle is automatically judged according to the temperature of the key points, and the corresponding thermal drift error model is automatically selected to apply the compensation to the spindle. The method is used to distinguish the thermal deformation attitude of the spindle in a CNC lathe, and the thermal deformation mechanism is used to predict the radial thermal drift error of the spindle.

公开(公告)号：US20210064988A1

公开(公告)日：2021-03-04

申请号：US16636258

申请日：2019-02-26

Applicant: DALIAN UNIVERSITY OF TECHNOLOGY

Inventor： Kuo LIU ,  Yongqing WANG ,  Xu LI ,  Bo QIN ,  Yongquan GAN ,  Dawei LI ,  Haining LIU

IPC: G06N3/08 ,  G06N3/04 ,  G06F17/10 ,  G05B19/4065

Abstract: A method for calculating the reliability of the thermal error model of a machine tool based on deep neural network (DNN) and the Monte Carlo method, which belongs to the field of the thermal error compensation of computer numerical control (CNC) machine tools. Firstly, according to the probability distribution of the thermal parameters and thermal error model, a set of data for training the DNN is generated. Next, the DNN is constructed based on the deep belief networks (DBNs) and trained with the training data. Then, a group of random sampling data is obtained according to the probability distribution of the thermal characteristic parameters of the machine tool, and the group of random sampling is taken as the input and the output is obtained by the trained depth neural network. Finally, the reliability of the thermal error model is calculated based on the Monte Carlo method.

公开(公告)号：US20210026319A1

公开(公告)日：2021-01-28

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.