Compensation of Measurement Error for Inclinometer Based on Neural Network

2009 ◽  
pp. 463-473
Author(s):  
Xiangwen Wen ◽  
Haiyang Cai ◽  
Minghua Pan ◽  
Guoli Zhu
Keyword(s):  
Neural Network ◽  
Measurement Error

Technology of NC Machine Error Parameter Identification Based on BP Neural Network

2013 ◽  
Vol 423-426 ◽  
pp. 2399-2403
Author(s):  
Yao Ming Li ◽  
Xing Quan Shen
Keyword(s):  
Neural Network ◽  
Measurement Error ◽  
Parameter Identification ◽  
High Precision ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Probe Measurement ◽  
Machine Error ◽  
Nc Machine ◽  
Error Parameter

NC machines touch-trigger probes generally have three-dimensional measuring function, its working principle is equivalent to a duplication of contacts with high precision switch, under the control of the measurement software, to achieve high-precision machining center automatically detected. Paper proposed based on BP neural network in NC machines tool probe measurement error parameter identification method. Through theoretical analysis and experimental research to determine the probe installation error parameters and the probe measurement error parameters of the model, but also identify ways to eliminate probe system error term uncertainty of the condition, in order to determine the detection method and planning of testing provides a reliable path basis.

Analysis of Measurement Error Compensation of Stationary αβ Stator Currents done through Auto-Associative Neural Network

2021 ◽  
Author(s):  
Thyago Estrabis ◽  
Matheus Pelzl ◽  
Raymundo Cordero ◽  
Walter Suemitsu ◽  
Luigi Galotto ◽  
...  
Keyword(s):  
Neural Network ◽  
Measurement Error ◽  
Error Compensation ◽  
Auto Associative Neural Network

Neural network approximation of resistivity tool responses in three–layer formation model

2020 ◽  
Author(s):  
Dmitry Yu. Kushnir ◽  
◽  
Nikolay N. Velker ◽  
Darya V. Andornaya ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Measurement Error ◽  
Approximation Method ◽  
Relative Position ◽  
Function Approximation ◽  
Model Parameters ◽  
Layer Formation ◽  
Formation Model ◽  
Test Database

We apply neural networks function approximation method to the problem of resistivity data modeling on the example of a three–layer geoelectric formation model. The model parameters distribution of the training database depends on the relative position of the tool coils and the layer boundaries. It is obtained on a test database that the signals calculated using neural networks coincide with the synthetic ones within one measurement error for more than 99.9% of the test samples.

On-Machine Measurement for Touch-Trigger Probes and its Error Compensation

2008 ◽  
Vol 375-376 ◽  
pp. 558-563 ◽  
Author(s):  
Xiao Ming Qian ◽  
Wen Hua Ye ◽  
Xiao Mei Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Measurement Error ◽  
Error Compensation ◽  
Error Modeling ◽  
Probe Measurement ◽  
Advantages And Disadvantages ◽  
Machining Center ◽  
Touch Trigger Probe ◽  
Artificial Neural

This paper advances a method to implement the on-machine measurement (OMM) with the touch-trigger probe, also called switching probes. Some of the advantages and disadvantages for touch-trigger probe are discussed. However, the touch-trigger probe errors exist and become one of the major errors for the measurement accuracy. Major factors that influence the probe measurement have been analyzed. The basic technique of probe measurement error modeling with artificial neural network was researched, and also the probe measurement error compensation with 3-layered backpropagation artificial neural network was presented. At last in the experimental system composed of DIXI 50 machining center, Fanuc 16i control system, Blum CNC P82.046 probe and PC, valid the correlated techniques. In addition, the connection and communication between the machining center equipped with probe system and the computer have been introduced. The experiment indicated that, using the touch-trigger probe makes on-machine measurement more automatic and efficient. And by using the back-propagation neural network for error compensation make on-machine measurement more precise.

Sign in / Sign up

Export Citation Format

Share Document