Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 2. Modelling and Estimation of Thermally Induced Errors

2001 ◽  
Vol 17 (9) ◽  
pp. 654-658 ◽  
Author(s):  
X. Li
Keyword(s):  
Real Time ◽  
Thermally Induced ◽  
Cnc Turning ◽  
Time Prediction ◽  
Cnc Turning Centre

An Integrated Modeling Approach for ANN-Based Real-Time Thermal Error Compensation on a CNC Turning Center

2013 ◽  
Vol 664 ◽  
pp. 907-915 ◽  
Author(s):  
Abderrazak El Ouafi ◽  
Michel Guillot ◽  
Noureddine Barka
Keyword(s):  
Real Time ◽  
Error Compensation ◽  
Thermal Error ◽  
Operating Conditions ◽  
Design Stage ◽  
Thermally Induced ◽  
Cnc Turning ◽  
Modeling Approach ◽  
Turning Center ◽  
Thermal Error Compensation

Thermally induced errors play a critical role in controlling the level of machining accuracy. They can represent a significant proportion of dimensional errors in produced parts. Since thermal errors cannot totally be eliminated at the design stage, active errors compensation appears to be the most economical and realistic solution. Accurate and efficient modeling of the thermally induced errors is an indispensable part of the error compensation process. This paper presents an integrated and comprehensive modeling approach for real-time thermal error compensation. The modeling process is based on multiple temperature measurements, Taguchi’s orthogonal arrays, artificial neural networks and various statistical tools to provide cost effective selection of appropriate temperature variables and modeling conditions as well as to achieve robust and accurate thermal error models. The experimental results on a CNC turning center confirm the feasibility and efficiency of the proposed approach and show that the resultant model can accurately predict the time-variant spindle thermal drift errors under various operating conditions. After compensation, the thermally induced spindle errors were reduced from 19m to less than 1 m. The proposed modeling optimization strategy can be effectively and advantageously used for real-time error compensation since it presents the benefit of straightforward application, reduced modeling time and uncertainty.

Real-time prediction of breast lesions displacement during Ultrasound scanning using a position-based dynamics approach

2019 ◽  
Author(s):  
D Dall Alba ◽  
◽  
E Tagliabue ◽  
E Magnabosco ◽  
C Tenga ◽  
...  
Keyword(s):  
Real Time ◽  
Breast Lesions ◽  
Ultrasound Scanning ◽  
Time Prediction

Real-Time Prediction of Tropical Cyclone Intensity Using COAMPS-TC

2012 ◽  
Author(s):  
J. D. Doyle ◽  
R. M. Hodur ◽  
S. Chen ◽  
H. Jin ◽  
Y. Jin ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
Time Prediction

scholarly journals Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network

2021 ◽  
Author(s):  
Yanfei Guan ◽  
S. V. Shree Sowndarya ◽  
Liliana C. Gallegos ◽  
Peter C. St. John ◽  
Robert S. Paton
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Quantum Chemical ◽  
Organic Molecules ◽  
Chemical Shifts ◽  
13C Chemical Shifts ◽  
Time Prediction ◽  
Nmr Data ◽  
Proton Chemical Shifts

From quantum chemical and experimental NMR data, a 3D graph neural network, CASCADE, has been developed to predict carbon and proton chemical shifts. Stereoisomers and conformers of organic molecules can be correctly distinguished.

scholarly journals Real-Time Prediction of Equivalent Circulation Density for Horizontal Wells Using Intelligent Machines

ACS Omega ◽  
2020 ◽  
Author(s):  
Ahmed Alsaihati ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Real Time ◽  
Horizontal Wells ◽  
Intelligent Machines ◽  
Time Prediction
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