A31 Cutting Force Estimation Based on Frequency Analysis of Servo Information in Ball Screw Drive Machine Tools

2012 ◽  
Vol 2012.9 (0) ◽  
pp. 61-62
Author(s):  
Makoto HASEGAWA ◽  
Ryuta SATO ◽  
Keiichi SHIRASE
Keyword(s):  
Cutting Force ◽  
Frequency Analysis ◽  
Machine Tools ◽  
Ball Screw ◽  
Force Estimation ◽  
Screw Drive ◽  
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

Improvement of Positioning Error on a Ball Screw Drive System by Liquid-Cooling

2011 ◽  
Author(s):  
Zhezhu Xu ◽  
Qi Zhang ◽  
Sungki Lyu
Keyword(s):  
Machine Tools ◽  
Cooling System ◽  
Drive System ◽  
Ball Screw ◽  
Positioning Error ◽  
Liquid Cooling ◽  
Screw Drive ◽  
Ball Screw Drive ◽  
Liquid Cooling System ◽  
Feed Drive System

The demand for higher productivity and tight part tolerances requires machine tools to have faster and more accurate feed drive system. As tried and tested technology, ball screw drive systems are still used in majority of machine tools due to their low cost and high stiffness. A high speed ball screw drive system natually generates more heat and results in greater positioning error, adversely affecting the accuracy of machined parts. In order to estimate the positioning error of the ball screw system and effectiveness of the liquid-cooling system, all possible heat gain-loss sources were analyzed and calculated as calculation factors. The following paper also presents degree of positioning error improvement which employed circulation liquid-cooling system and forced liquid-cooling system. Comparing the experimental results and the forcasts, it shows that sensational cooling performance and high consistency of reality and prediction are displayed.

FEM-Based Modeling to Simulate Thermal Deformation Process for High-Speed Ball Screw Drive Systems

2013 ◽  
Vol 481 ◽  
pp. 171-179 ◽  
Author(s):  
A.S. Yang ◽  
S.Z. Chai ◽  
H.H. Hsu ◽  
T.C. Kuo ◽  
W.T. Wu ◽  
...  
Keyword(s):  
Machine Tool ◽  
Machine Tools ◽  
Thermal Deformation ◽  
Drive System ◽  
Ball Screw ◽  
Frictional Heat ◽  
Thermal Dynamics ◽  
Screw Drive ◽  
Ball Screw Drive ◽  
Feed Drive System

Along with increasing speed and acceleration of numerically controlled machine tools, the influence of thermal dynamics characteristics on operating accuracy becomes more and more important. Improvement of thermal dynamics characteristics has turned out to be one of the crucial problems to develop machine tool of high performance. The positioning error of a feed drive system, mostly caused by the thermal deformation of a ball screw shaft, can directly affect the working accuracy of the machine tool. In this study, we applied the computational approach using the finite element method (FEM) to simulate the thermal expansion process for estimating the deformation of the ball screw system. In the numerical analysis, the deformation of the ball screw shaft and nut was modeled via a linear elasticity approach along with the assumption that the material was elastic, homogeneous, and isotropic. To model the reciprocating motions of the nut at a speed of 60m/min respecting to the screw shaft, we utilized the three-dimensional unsteady heat conduction equation with the frictional heat from the sources of the ball screw shaft, nut and bearings to calculate the temperature distributions for determining the temperature rises and axial thermal deformations in a ball screw shaft under operating situations. Simulations were conducted to explore the connection between the temperature increase of nut and the thermal deformation of the ball screw drive system, revealing the need of a compensation scheme for thermal error to improve the operating accuracy of machine tools.

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

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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