Lumped parameters modelling of the EMAs’ ball screw drive with special consideration to ball/grooves interactions to support model-based health monitoring

2019 ◽  
Vol 137 ◽  
pp. 188-210 ◽  
Author(s):  
Antonio Carlo Bertolino ◽  
Massimo Sorli ◽  
Giovanni Jacazio ◽  
Stefano Mauro
Keyword(s):  
Health Monitoring ◽  
Ball Screw ◽  
Special Consideration ◽  
Screw Drive ◽  
Model Based ◽  
Lumped Parameters ◽  
Ball Screw Drive ◽  
Support Model

Model based simulation of moving table by ball screw drive system

2021 ◽  
Vol 2021.58 (0) ◽  
pp. A012
Author(s):  
Ryuya MIZUNO ◽  
Yoshitaka MORIMOTO ◽  
Akio HAYASHI
Keyword(s):  
Drive System ◽  
Ball Screw ◽  
Screw Drive ◽  
Model Based ◽  
Ball Screw Drive

Dynamic Modeling of a Ball-Screw Drive and Identification of Its Installation Parameters

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Yu-Jia Hu ◽  
Yaoyu Wang ◽  
Weidong Zhu ◽  
Haolin Li
Keyword(s):  
Dynamic Model ◽  
Natural Frequencies ◽  
Data Fitting ◽  
Ball Screw ◽  
Mode Shapes ◽  
The Finite Element Method ◽  
Screw Drive ◽  
Log Normal ◽  
Ball Screw Drive ◽  
Log Normal Distribution

Abstract Parametric expressions of equivalent stiffnesses of a ball-screw shaft are obtained by derivation of its geometric parameters, the finite element method (FEM), and data fitting based on a modified probability density function of log-normal distribution. A dynamic model of a ball-screw drive that considers effects of bearing stiffnesses, the mass of the nut, and the axial pretension is established based on equivalent stiffnesses of its shaft. With the dynamic model and modal experimental results obtained by Bayesian operational modal analysis (BOMA), installation parameters of the ball-screw drive are identified by a genetic algorithm (GA) with a new comprehensive objective function that considers natural frequencies, mode shapes, and flexibility of the ball-screw drive. The effectiveness of the methodology is experimentally validated.

Techniques developed for fault diagnosis of long-range running ball screw drive machine to evaluate lubrication condition

Measurement ◽  
2018 ◽  
Vol 126 ◽  
pp. 274-288 ◽  
Author(s):  
Chang-Fu Han ◽  
He-Qing He ◽  
Chin-Chung Wei ◽  
Jeng-Haur Horng ◽  
Yueh-Lin Chiu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Long Range ◽  
Ball Screw ◽  
Screw Drive ◽  
Lubrication Condition ◽  
Ball Screw Drive

scholarly journals Tracking performance of NPID controller for cutting force disturbance of ball screw drive

2017 ◽  
Vol 11 (4) ◽  
pp. 3227-3239
Author(s):  
N. A. Anang ◽  
◽  
L. Abdullah ◽  
Z. Jamaludin ◽  
T.H. Chiew ◽  
...  
Keyword(s):  
Cutting Force ◽  
Tracking Performance ◽  
Ball Screw ◽  
Screw Drive ◽  
Ball Screw Drive

Online Chatter Detection for Milling Operations Using LSTM Neural Networks Assisted by Motor Current Signals of Ball Screw Drives

2020 ◽  
Vol 143 (1) ◽  
Author(s):  
Rajiv Kumar Vashisht ◽  
Qingjin Peng
Keyword(s):  
Neural Networks ◽  
Feed Rate ◽  
Short Term Memory ◽  
Ball Screw ◽  
Chatter Detection ◽  
Cnc Machine ◽  
Screw Drive ◽  
Milling Operations ◽  
Milling Tool ◽  
Ball Screw Drive

Abstract For certain combinations of cutter spinning speeds and cutting depths in milling operations, self-excited vibrations or chatter of the milling tool are generated. The chatter deteriorates the surface finish of the workpiece and reduces the useful working life of the tool. In the past, extensive work has been reported on chatter detections based on the tool deflection and sound generated during the milling process, which is costly due to the additional sensor and circuitry required. On the other hand, the manual intervention is necessary to interpret the result. In the present research, online chatter detection based on the current signal applied to the ball screw drive (of the CNC machine) has been proposed and evaluated. There is no additional sensor required. Dynamic equations of the process are improved to simulate vibration behaviors of the milling tool during chatter conditions. The sequence of applied control signals for a particular feed rate is decided based on known physical and control parameters of the ball screw drive. The sequence of the applied control signal to the ball screw drive for a particular feed rate can be easily calculated. Hence, costly experimental data are eliminated. Long short-term memory neural networks are trained to detect the chatter based on the simulated sequence of control currents. The trained networks are then used to detect chatter, which shows 98% of accuracy in experiments.

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