Prediction of three-dimensional coordinate measurement of space points based on BP neural network

2020 ◽  
Vol 15 (3) ◽  
pp. 218
Author(s):  
Xiaohong Lu ◽  
Yongquan Wang ◽  
Jie Li ◽  
Yang Zhou
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Coordinate Measurement ◽  
Three Dimensional Coordinate

Research on the influence of background light on the accuracy of a three-dimensional coordinate measurement system based on dual-PSD

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xiaohong Lu ◽  
Yu Zhou ◽  
Jinhui Qiao ◽  
Yihan Luan ◽  
Yongquan Wang
Keyword(s):  
Neural Network ◽  
Measurement System ◽  
Bp Neural Network ◽  
Measurement Accuracy ◽  
Three Dimensional ◽  
Electrical Method ◽  
Content Type ◽  
Coordinate Measurement ◽  
Background Light ◽  
Three Dimensional Coordinate

Purpose The purpose of this paper is to analyze the measurement error of a three-dimensional coordinate measurement system based on dual-position-sensitive detector (PSD) under different background light. Design/methodology/approach The mind evolutionary algorithm (MEA)-back propagation (BP) neural network is used to predict the three-dimensional coordinates of the points, and the influence of the background light on the measurement accuracy of the three-dimensional coordinates based on PSD is obtained. Findings The influence of the background light on the measurement accuracy of the system is quantitatively calculated. The background light has a significant influence on the prediction accuracy of the three-dimensional coordinate measurement system. The optical method, electrical method and photoelectric compensation method are proposed to improve the measurement accuracy. Originality/value BP neural network based on MEA is applied to the coordinate prediction of the three-dimensional coordinate measurement system based on dual-PSD, and the influence of background light on the measurement accuracy is quantitatively analyzed.

Three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA

2019 ◽  
Vol 36 (6) ◽  
pp. 2066-2083 ◽  
Author(s):  
Xiaohong Lu ◽  
Yongquan Wang ◽  
Jie Li ◽  
Yang Zhou ◽  
Zongjin Ren ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Three Dimensional ◽  
Measuring System ◽  
Content Type ◽  
Coordinate Measurement ◽  
World Coordinate System ◽  
Measurement Algorithm ◽  
Three Dimensional Coordinate

Purpose The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high. Design/methodology/approach A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points. Findings The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points. Originality/value A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.

scholarly journals Three-dimensional mandibular motion trajectory-tracking system based on BP neural network

2020 ◽  
Vol 17 (5) ◽  
pp. 5709-5726
Author(s):  
Sukun Tian ◽  
◽  
Ning Dai ◽  
Linlin Li ◽  
Weiwei Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Bp Neural Network ◽  
Tracking System ◽  
Three Dimensional ◽  
Motion Trajectory

Hull Plate Bending Springback Prediction Based on Artificial Neural Network

2014 ◽  
Vol 988 ◽  
pp. 309-312
Author(s):  
Shao Juan Su ◽  
Yong Hu ◽  
Cheng Fang Wang ◽  
Bin Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Plate Bending ◽  
Forming Process ◽  
Cold Bending ◽  
Matlab Programming ◽  
Springback Prediction ◽  
Surface Plate

In the process of three-dimensional curved hull plate forming, springback caused serious influence on the forming accuracy, in order to ensure the forming quality of the asymmetric multiple pressure heads CNC bending machine of ship hull 3D surface plate, to achieve the automatic processing, it is necessary to solve the problem of springback in the hull plate forming process. It is rarely to see the research on the cold bending springback problem of middle-thickness hull plate now. To established nonlinear model of plate parameters and springback amount based on BP neural network, accurately analyzing the prediction of springback, and getting the sptringback prediction model based on the BP neural network in the Matlab programming.

scholarly journals High-NA fiber point-diffraction interferometer for three-dimensional coordinate measurement

2014 ◽  
Vol 22 (21) ◽  
pp. 25550 ◽  
Author(s):  
Daodang Wang ◽  
Xixi Chen ◽  
Yangbo Xu ◽  
Fumin Wang ◽  
Ming Kong ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Coordinate Measurement ◽  
Three Dimensional Coordinate

Bone Scaffold Forming Filament Width Prediction of LDM Based on the Improved BP Neural Network

2013 ◽  
Vol 568 ◽  
pp. 187-192
Author(s):  
Yuan Yuan Liu ◽  
Zhen Zhong Han ◽  
Shu Hui Fang ◽  
Da Li Liu ◽  
Ying Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pore Size ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Influence Factors ◽  
Bone Scaffold ◽  
Forming Process ◽  
Size Accuracy ◽  
Forming Precision ◽  
Neural Network Theory

LDM process is used for preparing three-dimensional scaffolds for tissue engineering rapid prototyping technologies. Because of its forming process is complex, which influenced by a variety of factors, so the processing environment is not stable, the forming of scaffold pore size can not be guaranteed, therefore the forming precision is poor. However, the scaffold pore size accuracy is mainly decided by the wire filament width. Neural network theory and development provides a powerful tool for the study of nonlinear systems. This article analyzed the influence factors for forming bone scaffold filament width of LDM process, based on improved BP neural network, using MATLAB software programming, then predicted the filament width. The results show that model prediction error was less than 8%, it has high forecasting precision, and it can be used to guide the LDM process parameter selection and forming precision of prediction.

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